No retraction, correction or concern found when last checked on Oct 21, 2026.

Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

Martina Sollini, Lidija Antunovic, Arturo Chiti and 1 more

European Journal of Nuclear Medicine and Molecular Imaging · 2019 · Systematic review · CC BY

Abstract

Purpose. The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process.

Methods. Among the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies.

Results. Overall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the development of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials.

Conclusions. The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.

Electronic supplementary material. The online version of this article (10.1007/s00259-019-04372-x) contains supplementary material, which is available to authorized users.

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 21, 2026. Paragraph numbers are ours, so an answer can point at one.

Introduction

1The “Artificial Intelligence (AI) winter” is over. AI and radiomics approaches applied to medical images for the non-invasive characterisation of diseases (i.e., image mining) have remarkably increased in recent years. The first reports on AI and radiomics applied to medical images date back to 1963 and 1973, respectively, but the enthusiasm of those years broke off quite soon. Recently, increasing amounts of electronic medical data, technological improvements, and health sustainability issues resulted in a renewed interest in both AI and radiomics applications.

2Image mining is claimed to have a potentially huge clinical relevance with the possibility to non-invasively diagnose, characterise and predict the outcome in almost all medical conditions. However, despite the amount of published studies, some issues including significance, goodness, and strength of the reported results are still to be addressed. Particularly, it is not clear how far image mining is from clinical practice.

3Therefore, the aim of this systematic review was to analyse literature on AI and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process.

Material and methods

4This systematic review was performed according to the PRISMA statement. The PRISMA checklist is provided in Supplemental material.

Search, eligibility criteria and study selection

5The endpoint of the analysis was to assess the potential of AI applied to medical images and radiomics (i.e., image mining) to be implemented in clinics. Our search algorithm within the PubMed/MEDLINE database consisted of the combination of the following terms: “Artificial intelligence[Mesh]” OR “Radiomic” OR “Radiomics” AND/OR “Radiography[Mesh]”, OR “Ultrasonography[Mesh]” OR “Tomography[Mesh]”, OR “Image interpretation, computed-assisted[Mesh]”, OR “Multimodal imaging[Mesh]”, OR “Tomography, emission-computed[Mesh]”, OR “Photography[Mesh]”. No start date limit was used, and the search was extended until September 5th, 2018.

6According to the scope of the review, we considered AI (i.e., algorithms that take images as inputs) and radiomics investigations aimed at relevant objectives in clinical practice: biological characterisation, risk stratification, treatment response prediction, toxicity prediction, and prognostication of a certain disease. The imaging modalities we considered were ultrasound, radiography, mammography, endoscopy, skin pictures, ocular fundus pictures, computed tomography (CT), magnetic resonance imaging (MRI), scintigraphy (either planar images, SPECT, or SPECT/CT) and positron emission tomography (PET) or PET/CT. Subsequently, we applied the following exclusion criteria: (a) articles not in the English language; (b) studies not within the field of interest; (c) guidelines, review articles and meta-analysis, editorials or letters, comments, and conference proceedings; (d) “in vitro”, phantom or animal studies; (e) case reports or small case series (≤ 10 patients); (f) studies involving healthy subjects; (g) research articles focused on methodological aspects (algorithm and/or software development and/or comparison; evaluation/comparison of method(s) for parameters optimization, segmentation and features extraction; test–retest studies); (h) testing data (not medical images) as input for AI algorithm(s); (i) radiomics studies evaluating descriptors of shape and size or image intensity histogram only (i.e., not textural features); (j) lack of validation in a clinical setting; (k) lack of conventional metrics (i.e., sensitivity, specificity, accuracy, and/or hazard ratio, and/or recall, and/or AUC, and/or C-index) for the report of validation results.

7Two reviewers (MK and MS) independently performed an initial screening of the identified titles and abstracts applying the inclusion/exclusion criteria. The discrepancies were resolved by a third reviewer (LA). The decision rule for consensus was simple majority. Then, the reviewers retrieved the full-text reports of the selected abstracts and, subsequently, performed an independent second-step selection.

Quality assessment of the literature

8Quality assessment of selected papers was performed according to the QUADAS-2 criteria, assessing 4 domains: (1) patient selection, (2) index test, (3) reference standard, and (4) flow and timing. The signalling questions for each QUADAS-2 domain were tailored for the aim of this review as detailed in Table 1. This evaluation assigned the risk of bias to a study and ranked it as low (score = 2), high (score = 1), or indeterminate (score = 0) for each domain. We calculated the overall QUADAS-2 score as the sum of the scores. The appropriateness of statistical analysis was defined considering two aspects. First, the total number of patients analysed was considered appropriate if at least five patients/feature (after feature selection, if performed) were included in a radiomics study; while AI studies with more than 50 patients were considered as acceptable quality. The sample size criterion for radiomics studies was used adapting the conventional rule for multiple regression: the number of data points (i.e., observations or cases) should be considerably more than 5–10 times the number of variables. At least ten patients per feature have been recommended in radiomics studies. The sample size criterion for AI studies was established assuming that at least 50 patients are needed to train and validate an algorithm, minimising the effects of overfitting and improving the quality of performance metrics, similarly to what is recommended for biomarker discovery. Second, we assessed the balance in the number of patients between the subgroups (e.g. number of patients with benign vs malignant lesions in a study aimed at differential diagnosis); an imbalance of more than 2/3 was considered inappropriate.

Phases classification criteria

9We developed the phases classification criteria for image mining studies, inspired by the classification applied to the clinical trials (Fig. 1). The parameters for phase categorisation included: sample size, type of study (retrospective/prospective), type of validation approach (internal/independent), and the development stage (pre-/post-marketing). Figure 1 reports the classification criteria in detail. We assigned each selected article to a phase: from 0 to IV.

Statistical analysis

10A database was created for the qualitative synthesis of the papers and the studies’ results. We recorded the results obtained in the validation set only. In case of multiple aims within the same article, the primary one was considered. If image mining was applied to different imaging modalities within the same article (e.g., PET and CT), results were recorded for each one. If different approaches were tested within the same paper (e.g., models derived from random forest and support vector machine), the best in terms of diagnostic performance was analysed. The articles reporting identical or very similar sample size, research hypothesis, methodology and results sharing with almost the same authorship—“salami” publishing—were excluded to avoid overlap in the study population and results redundancy. Accordingly, in case of a series of articles considered as “salami”, the one with the larger sample size was evaluated and the other(s) excluded. Papers with a QUADAS-2 ≥ 7 were included in the quantitative analysis. Descriptive statistical measures were used to summarise the data. Excel ® 2017 (Microsoft®, Redmond, WA) was used for analysis.

Search, eligibility criteria and study selection

11Overall 34,626 articles were retrieved using the search algorithm. Subsequently, 33,997 papers were excluded reviewing titles and abstracts and applying the inclusion/exclusion criteria. Five papers, suspected to be a “salami” publishing, were excluded. Figure 2 summarises the research process. Table S1 reports a qualitative summary of the selected 300, including the 171 high quality articles. In recent years, a striking increase in the number of papers published on image mining occurred. In fact, especially in 2017 and 2018, 66 (22%) and 131 (44%) articles, respectively, were published. Figure 3 shows the literature trend. The vast majority (more than 80%) come from the oncology field. However, more than 50% of the studies included in the qualitative analysis have been assigned a high risk of bias (Fig. 4). Accordingly, a substantial proportion 129/300 (43%) studies have been scored as having a considerable risk of bias, mainly in the “index test” and “patient selection” domains (QUADAS-2 ≤ 6) (Fig. 5). Table S2 reports a qualitative summary of the 171 high-quality (QUADAS ≥7) papers. The temporal trend of the literature according to the phase of the study is shown in Fig. 6. Figures 7, 8, and 9 represent the graphical syntheses of the high-quality articles (QUADAS-2 ≥ 7) considered from three different points of view: the clinician, the imager, and the researcher. Quantitative synthesis is summarised in Table 2. The main results of the phase III studies are reported in Table 3.

Discussion

12The present systematic review is the first assessing the potential for implementation of image mining tools in clinical practice, by means of classification of the literature in development phases. Despite the amount of literature on image mining with a validation analysis, more than 90% of studies were classified as phase 0, I or II (i.e., retrospective). Collectively, their results were uncertain in terms of significance, goodness, and strength and their generalisability weak. Even among the studies with a QUADAS-2 ≥ 7, only 4.6% were categorised as phase III studies. As it emerges from the present systematic review, the results are promising but still not mature enough for clinical implementation and widespread use of image mining tools. Nonetheless, the study quality has increased in recent years.

13Because of the paucity of phase III and IV studies, we did not proceed to a meta-analysis. Therefore, no definitive conclusion can be drawn on which approach among radiomics and AI should be preferred. AI techniques, in particular convolutional neural networks, have the advantage over radiomics of not requiring tumour segmentation, feature calculation and selection. These steps are even more critical in tiny lesions that have to be submitted to radiomics processing. On the other hand, vast cohorts are crucial for a robust AI-based model development that require big efforts to be collected and analysed. Also, an unbiased reference standard, not always easy to obtain, should be chosen to ensure AI model reliability. The combined radiomics/AI strategy is at its early stages and the complementary role of radiomics and AI techniques should be addressed. Which is the best image mining approach is still an open question.

14Similar to the other “omics” domains (e.g., proteomics), few of the image mining biomarkers reached clinical practice. The translation of image mining research in the clinical arena is limited by the huge variability of the methods used for image analysis, together with the impasse to reproduce the results when tested in a different cohort of patients. Validation is a critical issue. Theoretically, the validation analysis of a successful model should provide consistent performance measures to those obtained in the training process. Thereafter, results obtained in the validation cohort should be confirmed by the test-independent validation. Finally, the proposed approach should be effective for the indication within the “real world” population of patients the model has been developed for. The validation process may be internal (e.g., cross-validation and bootstrapping) or external (using data not used for training). Typically, the internal one, used for a preliminary evaluation or for the fine-tuning of the model under development, overestimate the performance. In fact, the same cohort is used twice, once to choose the filtered subset and again to build a classification model resulting in the overfitting of the algorithm to the data. The external validation may be performed using three different strategies: (i) temporal (i.e., data obtained in newly recruited patients), (ii) geographic (i.e., data collected in a different institution), and (iii) split-sample (i.e., data split from the entire dataset and kept untouched for the test). External validation is crucial to verify the generalisability of the results; and the random patient selection is an essential prerequisite, as well as the balance in patient characteristics. Temporal or geographic validations should be preferred to the split-sample one. Particularly, the geographic validation, which accounts for technical variability aspects (scanners, acquisition parameters and protocols), is expected to be more representative of the clinical setting.

15We excluded a priori from the present analysis studies testing shape and size as well as histogram-based features since our aim was to assess the “maturity” of advanced image analysis at its’ full potential, entailing textural indexes derived from up to second-order features. Image analysis based on the gray level histogram only does not provide any information about the relative position of pixels/voxels to each other within the region of interest. Therefore, these features are not able to describe whether any low/high gray levels are positioned together, or if they are distributed between high/low-value gray levels.

16In order to develop a valid and trustworthy image mining tool the cohorts in study (training, validation and test) should be representative of the target population. This means that the sample size should be big enough to minimise the effects of overfitting, be comprehensive of the “outliers”, and, consequently, be reliable when used for the assessment of unseen patients. We proposed that at least 50 patients should be included in AI studies, as also suggested by simulated analyses. However, especially for deep learning approaches and complex tasks, much larger populations are needed. The effect of the sample size on the model performance has been already demonstrated. When a limited dataset (1000 samples) vs the complete dataset (>100,000) was used for retinopathy classifier development, the weighted error resulted as 12.7% vs 6.6%, respectively. Moreover, the sample size was not the only criterion used to score papers. Accordingly, even if the number of patients was relatively low, only well-designed studies have been scored as having low risk of bias and included in the quantitative analysis.

17In the present work we arbitrarily chose 100 samples as the threshold for trial phases categorisation (II vs III ad above). This was a conservative decision. The conventional rule of the necessity to include at least ten patients per tested variable if applied for phase classification would have led to downgrading of most of the studies. Considering the fact that generally a feature reduction strategy was put in place we chose 100 as a reasonable cut-off. When planning an image mining study, both statistical recommendations (sample size and prospective design) and clinical conditions’ variability should be considered in order to develop an algorithm on a dataset that realistically represents the target patient population. The sample size calculation has been estimated in only 3 out of the 171 papers. The use of multiple images taken from the same patient should be limited since they are prone to be similar (or almost identical) with a negative impact on the generalisability potential. In fact, even if this process increases the sample size, the multiplied number of observations (or cases) not representative of the inter-patient variability, overestimates the model performance. Accordingly, data augmentation should be properly used to avoid overfitting, keeping in mind that it cannot completely overcome the requirement of a proper sample size.

18To assess the appropriateness of the statistical analysis, we considered not only the sample size but also the number of patients within the subgroups (an imbalance of more than 2/3 was considered inappropriate). Imbalanced cohorts in image mining studies may lead to constitutively biased results, which confer higher uncertainty and poor generalisability. Consequently, in the developmental phase, proper study design and analysis strategy using stratification, matching, weighting, covariate adjustment, or regression should be adopted. Therefore, imbalance per se does not prevent the use of radiomic and AI-based approaches when the prevalence of a disease or an outcome is very low.

19The applicability of the image mining framework to rare diseases is still an issue because of the limited data availability for model development. Transversal platforms for sharing and analysis of images and data, as envisaged by some research groups, could represent a valuable strategy for the investigations in this field.

20Presently, it is unrealistic to justify a medical decision by the output provided by a neural network or a radiomics feature/signature. Little or nothing is still known on the biological significance of the image-derived parameters. Correlations with tumour grading, inflammatory infiltrate, gene expression, mutation and molecular pathways have been reported. Nonetheless, more should be learned about the functioning of AI and radiomics approaches in order to solve the “black box” problem and to understand the underlying clinical and/or molecular connotation. Imagers should be able to assess the reliability of image mining approaches and to manage independently the patient (i.e., the pilot, plane and passengers during a flight). This innovative attitude, which implies the acquisition of technical and informatics skills, will contribute to remove the “black box” uncertainties, and to promote image mining towards clinical practice.

21Additionally, some technical barriers should be faced when considering implementing image mining tools into the every-day practice. These include a time-consuming workflow; uncertain reproducibility of results among different scanners, acquisition protocols, and image-processing approaches; regulatory issues concerning privacy and ethics; and data protection. Common efforts should be realised to accelerate the research path on these aspects, and to implement the technological infrastructure and make the tools easy to use. Privacy and ethics regulations may restrict data and image sharing for the purpose of research and every-day clinical practice. A shared strategy needs to be built up for the management of these aspects. These challenges are an opportunity to develop a reliable methodology able to provide controlled data collection and secure infrastructure, instead of gathering uncertain-quality datasets.

22The need to provide reliable results has generated multiple initiatives and recommendations to achieve methodology standardisation and reproducibility. The increasing awareness among researchers of the urgency to increase the quality of the investigations determined an increase in the number of phase III trials in the last 2 years. In the era of evidence-based medicine, rigorous research with strict rules is the only way forward to achieve clinical acceptance and become part of the “standard of care”. The research process should aim to address a clinical need through an adequate statistical strategy, prospective and multi centre studies, robust reference standards, and adequate timing. Independent validation is mandatory together with the clarification of the impact of the technical aspects on image mining models. These items imply that reproducible, strong, and, hopefully, excellent results will be achieved through an adequate research process. In this respect, a closer collaboration should be established among clinical researchers, algorithm developers and data scientists.

23We foresee the transfer learning from the well-known drug development process, with some specific adaptations, to the image mining discipline as the most effective way for radiomics and AI algorithms to get into routine clinical practice and avoid a new glacial era of image mining in the next decades.

References 193

1.
Bohannon J, Bohannon J. Fears of an AI pioneer. Science (80- ). 2015;349:252.
Not in our library10.1126/science.349.6245.252
2.
Lodwick GS, Keats TE, Dorst JP. The coding of roentgen images for computer analysis as applied to lung cancer. Radiology. 1963;81:185-200.
Not in our library10.1148/81.2.185
3.
Haralick R, Shanmugam K, Dinstein I. Texture features for image classification. IEEE Trans Sys Man Cybern. 1973;SMC 3:610-621.
Not in our library
4.
Zorzela L, Loke Y, Ioannidis J, Golder S, Santaguida P, Altman D, et al. PRISMA harms checklist: improving harms reporting in systematic reviews. BMJ. 2016;352:i157.
Not in our library10.1136/bmj.i157
5.
Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: strumento per valutare la qualità degli studi di accuratezza diagnostica. Evidence. 2016;8:e1000131.
Not in our library
6.
Kleinbaum DG, Kupper LL, Muller KE. Applied regression analysis and other multivariable methods. 1988.
Not in our library
7.
PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology · 2017
No notice
8.
Gillies Robert J., Kinahan Paul E., Hricak Hedvig. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016;278(2):563-577.
Not in our library10.1148/radiol.2015151169
9.
Hernández B, Parnell A, Pennington SR. Why have so few proteomic biomarkers “survived” validation? (sample size and independent validation considerations). Proteomics. 2014;14:1587-1592.
Not in our library10.1002/pmic.201300377
10.
Supak Smolcić V. Salami publication: definitions and examples. Biochem medica. 2013;23:237–41.
Not in our library10.11613/bm.2013.030
11.
Tong Tong, Wolz Robin, Gao Qinquan, Guerrero Ricardo, Hajnal Joseph V., Rueckert Daniel. Multiple instance learning for classification of dementia in brain MRI. Medical Image Analysis. 2014;18(5):808-818.
Not in our library10.1016/j.media.2014.04.006
12.
Acton PD, Newberg A. Artificial neural network classifier for the diagnosis of Parkinson’s disease using [99mTc]TRODAT-1 and SPECT. Phys Med Biol. 2006;51:3057-3066.
Not in our library10.1088/0031-9155/51/12/004
13.
Guner LA, Karabacak NI, Akdemir OU, Karagoz PS, Kocaman SA, Cengel A, et al. An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT. J Nucl Cardiol. 2010;17:405-413.
Not in our library10.1007/s12350-010-9207-5
14.
Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018;77:106-111.
Not in our library10.1016/j.jdent.2018.07.015
15.
Lee J-H, Kim D-H, Jeong S-N, Choi S-H. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. 2018;48:114-123.
Not in our library10.5051/jpis.2018.48.2.114
16.
Kong X, Gong S, Su L, Howard N, Kong Y. Automatic detection of acromegaly from facial photographs using machine learning methods. EBioMedicine. 2018;27:94-102.
Not in our library10.1016/j.ebiom.2017.12.015
17.
Wang K, Lu X, Zhou H, Gao Y, Zheng J, Tong M, et al. Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut. 2018:1–13.
Not in our library10.1136/gutjnl-2018-316204
18.
Yasaka K, Akai H, Kunimatsu A, Abe O, Kiryu S. Deep learning for staging liver fibrosis on CT: a pilot study. Eur Radiol. 2018;28:4578-4585.
Not in our library10.1007/s00330-018-5499-7
19.
Komeda Y, Handa H, Watanabe T, Nomura T, Kitahashi M, Sakurai T, et al. Computer-aided diagnosis based on convolutional neural network system for colorectal polyp classification: preliminary experience. Oncology. 2017;93:30-34.
Not in our library10.1159/000481227
20.
Byrne MF, Chapados N, Soudan F, Oertel C, Linares Pérez M, Kelly R, et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. 2017;68:94-100.
Not in our library10.1136/gutjnl-2017-314547
21.
Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images · 2017
No notice
22.
Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network · 2018
No notice
23.
Li Wei, Cao Peng, Zhao Dazhe, Wang Junbo. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images. Computational and Mathematical Methods in Medicine. 2016;2016:1-7.
Not in our library10.1155/2016/6215085
24.
Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors. Healthc Inform Res. 2018;24:236-241.
Not in our library10.4258/hir.2018.24.3.236
25.
Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence · 2017
No notice
26.
Sato M, Horie K, Hara A, Miyamoto Y, Kurihara K, Tomio K, et al. Application of deep learning to the classification of images from colposcopy. Oncol Lett. 2018;15:3518-3523.
Not in our library10.3892/ol.2018.7762
27.
Esteva Andre, Kuprel Brett, Novoa Roberto A., Ko Justin, Swetter Susan M., Blau Helen M., Thrun Sebastian. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118.
Not in our library10.1038/nature21056
28.
Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks · 2015
No notice
29.
Gargeya Rishab, Leng Theodore. Automated Identification of Diabetic Retinopathy Using Deep Learning. Ophthalmology. 2017;124(7):962-969.
Not in our library10.1016/j.ophtha.2017.02.008
30.
Niemeijer M, van Ginneken B, Russell SR, Suttorp-Schulten MSA, Abràmoff MD. Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Invest Ophthalmol Vis Sci. 2007;48:2260-2267.
Not in our library10.1167/iovs.06-0996
31.
Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy · 2017
No notice
32.
Raju M, Pagidimarri V, Barreto R, Kadam A, Kasivajjala V, Aswath A. Development of a deep learning algorithm for automatic diagnosis of diabetic retinopathy. Stud Health Technol Inform. 2017;245:559-563.
Not in our library
33.
Kermany DDS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172:1122-1131.e1129.
Not in our library10.1016/j.cell.2018.02.010
34.
Burlina PM, Joshi N, Pekala M, Pacheco KD, Freund DE, Bressler NM. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 2017;135:1170-1176.
Not in our library10.1001/jamaophthalmol.2017.3782
35.
Artificial intelligence for analyzing orthopedic trauma radiographs: Deep learning algorithms—are they on par with humans for diagnosing fractures? · 2017
Not checked
36.
Automated detection and classification of the proximal humerus fracture by using deep learning algorithm · 2018
No notice
37.
Gonzalez G, Ash SY, Vegas-Sánchez-Ferrero G, Onieva JO, Rahaghi FN, Ross JC, et al. Disease staging and prognosis in smokers using deep learning in chest computed tomography. Am J Respir Crit Care Med. 2018;197:193-203.
Not in our library10.1164/rccm.201705-0860oc
38.
Li W, Huang Y, Zhuang BW, Liu GJ, Hu HT, Li X, et al. Multiparametric ultrasomics of significant liver fibrosis: a machine learning-based analysis. Eur Radiol. 2019;29:1496-1506.
Not in our library10.1007/s00330-018-5680-z
39.
Zhang X, Xu X, Tian Q, Li B, Wu Y, Yang Z, et al. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J Magn Reson Imaging. 2017;46:1281-1288.
Not in our library10.1002/jmri.25669
40.
Wu S, Zheng J, Li Y, Yu H, Shi S, Xie W, et al. A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Clin Cancer Res. 2017;23:6904-6911.
Not in our library10.1158/1078-0432.ccr-17-1510
41.
Wu S, Zheng J, Li Y, Wu Z, Shi S, Huang M, et al. Development and validation of an MRI-based radiomics signature for the preoperative prediction of lymph node metastasis in bladder cancer. EBioMedicine. 2018;34:76-84.
Not in our library10.1016/j.ebiom.2018.07.029
42.
Zhang Z, Yang J, Ho A, Jiang W, Logan J, Wang X, et al. A predictive model for distinguishing radiation necrosis from tumor progression after gamma knife radiosurgery based on radiomic features from MR images HHS public access. Eur Radiol. 2018;28:2255-2263.
Not in our library10.1007/s00330-017-5154-8
43.
Kim JY, Park JE, Jo Y, Shim WH, Nam SJ, Kim JH, et al. Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro-Oncology. 2018:1–11.
Not in our library10.1093/neuonc/noy133
44.
A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication · 2018
No notice
45.
Coroller TP, Bi WL, Huynh E, Abedalthagafi M, Aizer AA, Greenwald NF, et al. Radiographic prediction of meningioma grade by semantic and radiomic features. PLoS One. 2017;12:1-15.
Not in our library10.1371/journal.pone.0187908
46.
Chen W, Liu B, Peng S, Sun J, Qiao X. Computer-aided grading of gliomas combining automatic segmentation and radiomics. Int J Biomed Imaging. 2018;2018:2512037.
Not in our library10.1155/2018/2512037
47.
Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1 · 2018
No notice
48.
Li Z-C, Bai H, Sun Q, Li Q, Liu L, Zou Y, et al. Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: a multicentre study. Eur Radiol. 2018;28:3640-3650.
Not in our library10.1007/s00330-017-5302-1
49.
Arita H, Kinoshita M, Kawaguchi A, Takahashi M, Narita Y, Terakawa Y, et al. Lesion location implemented magnetic resonance imaging radiomics for predicting IDH and TERT promoter mutations in grade II/III gliomas. Sci Rep. 2018;8:1-10.
Not in our library10.1038/s41598-018-30273-4
50.
Li Y, Liu X, Qian Z, Sun Z, Xu K, Wang K, et al. Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature. Eur Radiol. 2018;28:2960-2968.
Not in our library10.1007/s00330-017-5267-0
51.
Zhang X, Tian Q, Wang L, Liu Y, Li B, Liang Z, et al. Radiomics strategy for molecular subtype stratification of lower-grade glioma: detecting IDH and TP53 mutations based on multimodal MRI. J Magn Reson Imaging. 2018;48:916-926.
Not in our library10.1002/jmri.25960
52.
Akbari H, Bakas S, Pisapia JM, Nasrallah MP, Rozycki M, Martinez-Lage M, et al. In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature. Neuro-Oncology. 2018;20:1068-1079.
Not in our library10.1093/neuonc/noy033
53.
Lu CF, Hsu FT, Hsieh KLC, Kao YCJ, Cheng SJ, Hsu JBK, et al. Machine learning–based radiomics for molecular subtyping of gliomas. Clin Cancer Res. 2018;24:4429-4436.
Not in our library10.1158/1078-0432.ccr-17-3445
54.
Han Y, Xie Z, Zang Y, Zhang S, Gu D, Zhou M, et al. Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas. J Neuro-Oncol. 2018;140:297-306.
Not in our library10.1007/s11060-018-2953-y
55.
Li Y, Liu X, Xu K, Qian Z, Wang K, Fan X, et al. MRI features can predict EGFR expression in lower grade gliomas: a voxel-based radiomic analysis. Eur Radiol. 2018;28:356-362.
Not in our library10.1007/s00330-017-4964-z
56.
García-Gómez JM, Luts J, Julià-Sapé M, Krooshof P, Tortajada S, Robledo JV, et al. Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy. MAGMA. 2009;22:5-18.
Not in our library10.1007/s10334-008-0146-y
57.
Li Y, Qian Z, Xu K, Wang K, Fan X, Li S, et al. Radiomic features predict Ki-67 expression level and survival in lower grade gliomas. J Neuro-Oncol. 2017;135:317-324.
Not in our library10.1007/s11060-017-2576-8
58.
Li Y, Qian Z, Xu K, Wang K, Fan X, Li S, et al. MRI features predict p53 status in lower-grade gliomas via a machine-learning approach. NeuroImage Clin. 2018;17:306-311.
Not in our library10.1016/j.nicl.2017.10.030
59.
Ortiz-Ramón R, Larroza A, Ruiz-España S, Arana E, Moratal D. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol. 2018;28:4514-4523.
Not in our library10.1007/s00330-018-5463-6
60.
Suh HB, Choi YS, Bae S, Ahn SS, Chang JH, Kang SG, et al. Primary central nervous system lymphoma and atypical glioblastoma: differentiation using radiomics approach. Eur Radiol. 2018;28:3832-3839.
Not in our library10.1007/s00330-018-5368-4
61.
Chen Y, Li Z, Wu G, Yu J, Wang Y, Lv X, et al. Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features. Int J Neurosci. 2018;128:608-618.
Not in our library10.1080/00207454.2017.1408613
62.
Kang D, Park JE, Kim YH, Kim JH, Oh JY, Kim J, et al. Diffusion radiomics as a diagnostic modal for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation. Neuro-Oncology. 2018;20:1251-1261.
Not in our library10.1093/neuonc/noy021
63.
Yu Jinhua, Shi Zhifeng, Lian Yuxi, Li Zeju, Liu Tongtong, Gao Yuan, Wang Yuanyuan, Chen Liang, Mao Ying. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. European Radiology. 2016;27(8):3509-3522.
Not in our library10.1007/s00330-016-4653-3
64.
Liu Z, Wang Y, Liu X, Du Y, Tang Z, Wang K, et al. Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas. NeuroImage Clin. 2018;19:271-278.
Not in our library10.1016/j.nicl.2018.04.024
65.
Kickingereder P, Götz M, Muschelli J, Wick A, Neuberger U, Shinohara RT, et al. Large-scale Radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-Angiogenic treatment response. Clin Cancer Res. 2016;22:5765-5771.
Not in our library10.1158/1078-0432.ccr-16-0702
66.
Kickingereder P, Burth S, Wick A, Götz M, Eidel O, Schlemmer H-P, et al. Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology. 2016;280:880-889.
Not in our library10.1148/radiol.2016160845
67.
Ingrisch M, Schneider MJ, Nörenberg D, De Figueiredo GN, Maier-Hein K, Suchorska B, et al. Radiomic analysis reveals prognostic information in T1-weighted baseline magnetic resonance imaging in patients with glioblastoma. Investig Radiol. 2017;52:360-366.
Not in our library10.1097/rli.0000000000000349
68.
Li Q, Bai H, Chen Y, Sun Q, Liu L, Zhou S, et al. A fully-automatic multiparametric radiomics model: towards reproducible and prognostic imaging signature for prediction of overall survival in glioblastoma multiforme. Sci Rep. 2017;7:1–9.
Not in our library10.1038/s41598-017-14753-7
69.
Beig N, Patel J, Prasanna P, Hill V, Gupta A, Correa R, et al. Radiogenomic analysis of hypoxia pathway is predictive of overall survival in glioblastoma. Sci Rep. 2018;8:7.
Not in our library10.1038/s41598-017-18310-0
70.
Kickingereder P, Neuberger U, Bonekamp D, Piechotta PL, Götz M, Wick A, et al. Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma. Neuro-Oncology. 2018;20:848-857.
Not in our library10.1093/neuonc/nox188
71.
Grossmann P, Narayan V, Chang K, Rahman R, Abrey L, Reardon DA, et al. Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab. Neuro-Oncology. 2017;19:1688-1697.
Not in our library10.1093/neuonc/nox092
72.
Chaddad A, Sabri S, Niazi T, Abdulkarim B. Prediction of survival with multi-scale radiomic analysis in glioblastoma patients. Med Biol Eng Comput. 2018;56:2287-2300.
Not in our library10.1007/s11517-018-1858-4
73.
Bickelhaupt S, Jaeger PF, Laun FB, Lederer W, Daniel H, Kuder TA, et al. Radiomics based on adapted diffusion kurtosis imaging helps to clarify most mammographic findings suspicious for cancer. Radiology. 2018;287:170273.
Not in our library10.1148/radiol.2017170273
74.
Lim WK, Er MJ. Classification of mammographic masses using generalized dynamic fuzzy neural networks. Med Phys. 2004;31:1288-1295.
Not in our library10.1118/1.1708643
75.
Sapate SG, Mahajan A, Talbar SN, Sable N, Desai S, Thakur M. Radiomics based detection and characterization of suspicious lesions on full field digital mammograms. Comput Methods Prog Biomed. 2018;163:1-20.
Not in our library10.1016/j.cmpb.2018.05.017
76.
Chen W, Giger ML, Lan L, Bick U. Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. Med Phys. 2004;31:1076-1082.
Not in our library10.1118/1.1695652
77.
Sahiner B, Chan HP, Roubidoux MA, Helvie MA, Hadjiiski LM, Ramachandran A, et al. Computerized characterization of breast masses on three-dimensional ultrasound volumes. Med Phys. 2004;31:744-754.
Not in our library10.1118/1.1649531
78.
Nandi RJ, Nandi AK, Rangayyan R, Scutt D. Genetic programming and feature selection for classification of breast masses in mammograms. Conf Proc IEEE Eng Med Biol Soc. 2006;44:3021–4.
Not in our library10.1109/iembs.2006.260460
79.
Cui J, Sahiner B, Chan HP, Nees A, Paramagul C, Hadjiiski LM, et al. A new automated method for the segmentation and characterization of breast masses on ultrasound images. Med Phys. 2009;36:1553-1565.
Not in our library10.1118/1.3110069
80.
Moon WK, Shen YW, Huang CS, Chiang LR, Chang RF. Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images. Ultrasound Med Biol. 2011;37:539-548.
Not in our library10.1016/j.ultrasmedbio.2011.01.006
81.
Ramos-Pollán R, Guevara-López MA, Suárez-Ortega C, Díaz-Herrero G, Franco-Valiente JM, Rubio-Del-Solar M, et al. Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J Med Syst. 2012;36:2259-2269.
Not in our library10.1007/s10916-011-9693-2
82.
Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR: a machine learning approach. · 2014
Not checked
83.
Dash R, Roy S, Beura S, Majhi B. Classification of mammogram using two-dimensional discrete orthonormal S-transform for breast cancer detection. Healthc Technol Lett. 2015;2:46-51.
Not in our library10.1049/htl.2014.0108
84.
Cai H, Liu L, Peng Y, Wu Y, Li L. Diagnostic assessment by dynamic contrast-enhanced and diffusion-weighted magnetic resonance in differentiation of breast lesions under different imaging protocols. BMC Cancer. 2014;14:1-12.
Not in our library10.1186/1471-2407-14-366
85.
Ma W, Ji Y, Qi L, Guo X, Jian X, Liu P. Breast cancer Ki67 expression prediction by DCE-MRI radiomics features. Clin Radiol. 2018;73:909.e1-909.e5.
Not in our library10.1016/j.crad.2018.05.027
86.
Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set · 2016
No notice
87.
Liang C, Cheng Z, Huang Y, He L, Chen X. An MRI-based radiomics classifier for preoperative prediction of Ki-67 status in breast cancer. Acad Radiol. 2018;25:1111-1117.
Not in our library10.1016/j.acra.2018.01.006
88.
Dong Y, Feng Q, Yang W, Lu Z, Deng C, Zhang L, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol. 2018;28:582-591.
Not in our library10.1007/s00330-017-5005-7
89.
Liu C, Ding J, Spuhler K, Gao Y, Serrano Sosa M, Moriarty M, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI. J Magn Reson Imaging. 2019;49:131-140.
Not in our library10.1002/jmri.26224
90.
Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI · 2017
Corrected
91.
Fan M, Wu G, Cheng H, Zhang J, Shao G, Li L. Radiomic analysis of DCE-MRI for prediction of response to neoadjuvant chemotherapy in breast cancer patients. Eur J Radiol. 2017;94:140-147.
Not in our library10.1016/j.ejrad.2017.06.019
92.
Park H, Lim Y, Ko ES, Cho H-H, Lee JE, Han B-K, et al. Radiomics signature on magnetic resonance imaging: association with disease-free survival in patients with invasive breast cancer. Clin Cancer Res. 2018;24:4705-4714.
Not in our library10.1158/1078-0432.ccr-17-3783
93.
Huang SY, Franc BL, Harnish RJ, Liu G, Mitra D, Copeland TP, et al. Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis. NPJ Breast Cancer. 2018;4:24.
Not in our library10.1038/s41523-018-0078-2
94.
Liu Y, Zhang Y, Cheng R, Liu S, Qu F, Yin X, et al. Radiomics analysis of apparent diffusion coefficient in cervical cancer: a preliminary study on histological grade evaluation. J Magn Reson Imaging. 2019;49:1-11.
Not in our library10.1002/jmri.26192
95.
Radiomics of the primary tumour as a tool to improve 18F-FDG-PET sensitivity in detecting nodal metastases in endometrial cancer · 2018
Not checked
96.
Reuzé S, Orlhac F, Chargari C, Nioche C, Limkin E, Riet F, et al. Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners. Oncotarget. 2017;8:43169-43179.
Not in our library10.18632/oncotarget.17856
97.
Schernberg A, Reuze S, Orlhac F, Buvat I, Dercle L, Sun R, et al. A score combining baseline neutrophilia and primary tumor SUVpeakmeasured from FDG PET is associated with outcome in locally advanced cervical cancer. Eur J Nucl Med Mol Imaging. 2018;45:187-195.
Not in our library10.1007/s00259-017-3824-z
98.
Altazi BA, Fernandez DC, Zhang GG, Hawkins S, Naqvi SM, Kim Y, et al. Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes. Phys Medica. 2018;46:180-188.
Not in our library10.1016/j.ejmp.2017.10.009
99.
Lucia F, Visvikis D, Desseroit M-C, Miranda O, Malhaire J-P, Robin P, et al. Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging. 2018;45:768-786.
Not in our library10.1007/s00259-017-3898-7
100.
Huang X, Cheng Z, Huang Y, Liang CC, He L, Ma Z, et al. CT-based radiomics signature to discriminate high-grade from low- grade colorectal adenocarcinoma. Acad Radiol RA. 2018;25:1285-1297.
Not in our library10.1016/j.acra.2018.01.020
101.
Yang L, Dong D, Fang M, Zhu Y, Zang Y, Liu Z, et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? Eur Radiol. 2018;28:2058-2067.
Not in our library10.1007/s00330-017-5146-8
102.
The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer · 2016
No notice
103.
Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol. 2016;34:2157-2164.
Not in our library10.1200/jco.2015.65.9128
104.
Carvalho S, Leijenaar RTH, Troost EGC, van Timmeren JE, Oberije C, van Elmpt W, et al. 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)-radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC) - a prospective externally validated study. PLoS One. 2018;13:e0192859.
Not in our library10.1371/journal.pone.0192859
105.
Cusumano D, Dinapoli N, Boldrini L, Chiloiro G, Gatta R, Masciocchi C, et al. Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer. Radiol Med. 2018;123:286-295.
Not in our library10.1007/s11547-017-0838-3
106.
Liu Z, Zhang XY, Shi YJ, Wang L, Zhu HT, Tang Z, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res. 2017;23:7253-7262.
Not in our library10.1158/1078-0432.ccr-17-1038
107.
Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer · 2018
Corrected
108.
Cui Y, Yang X, Shi Z, Yang Z, Du X, Zhao Z, et al. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol. 2019;29:1211-1220.
Not in our library10.1007/s00330-018-5683-9
109.
Meng Y, Zhang Y, Dong D, Li C, Liang X, Zhang C, et al. Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer. J Magn Reson Imaging. 2018;48:605-614.
Not in our library10.1002/jmri.25968
110.
Tan X, Ma Z, Yan L, Ye W, Liu Z, Liang C. Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma. Eur Radiol. 2019;29:392-400.
Not in our library10.1007/s00330-018-5581-1
111.
Shen C, Liu Z, Wang Z, Guo J, Zhang H, Wang Y, et al. Building CT radiomics based nomogram for preoperative esophageal cancer patients lymph node metastasis prediction. Transl Oncol. 2018;11:815-824.
Not in our library10.1016/j.tranon.2018.04.005
112.
Qu J, Shen C, Qin J, Wang Z, Liu Z, Guo J, et al. The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer. Eur Radiol. 2019;29:906-914.
Not in our library10.1007/s00330-018-5583-z
113.
Hou Z, Li S, Ren W, Liu J, Yan J, Wan S. Radiomic analysis in T2W and SPAIR T2W MRI: predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma. J Thorac Dis. 2018;10:2256-2267.
Not in our library10.21037/jtd.2018.03.123
114.
Moyo K, Porter C, Chilima B, Mwenda R, Kabue M, Zungu L, et al. Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer. Phys Med Biol. 2016;4:1-19.
Not in our library10.1088/1361-6560/aacd22
115.
Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks · 2015
No notice
116.
Larue RTHM, Klaassen R, Jochems A, Leijenaar RTH, Hulshof MCCM, van Berge Henegouwen MI, et al. Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer. Acta Oncol (Madr). 2018;57:1475-1481.
Not in our library10.1080/0284186x.2018.1486039
117.
Chen T, Ning Z, Xu L, Feng X, Han S, Roth HR, et al. Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively. Eur Radiol. 2019;29:1074-1082.
Not in our library10.1007/s00330-018-5629-2
118.
Bektas CT, Kocak B, Yardimci AH, Turkcanoglu MH, Yucetas U, Koca SB, et al. Clear cell renal cell carcinoma: machine learning-based quantitative computed tomography texture analysis for prediction of Fuhrman nuclear grade. Eur Radiol. 2019;29:1153-1163.
Not in our library10.1007/s00330-018-5698-2
119.
Peng J, Zhang J, Zhang Q, Xu Y, Zhou J, Liu L. A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis b virus-related hepatocellular carcinoma. Diagn Interv Radiol. 2018;24:121-127.
Not in our library10.5152/dir.2018.17467
120.
Zhang J, Wang Y, Dong Y, Wang Y. Computer-aided diagnosis of cervical lymph nodes on ultrasonography. Comput Biol Med. 2008;38:234-243.
Not in our library10.1016/j.compbiomed.2007.10.005
121.
Bogowicz M, Riesterer O, Ikenberg K, Stieb S, Moch H, Studer G, et al. Computed tomography radiomics predicts HPV status and local tumor control after definitive Radiochemotherapy in head and neck squamous cell carcinoma. Int J Radiat Oncol Biol Phys. 2017;99:921-928.
Not in our library10.1016/j.ijrobp.2017.06.002
122.
Leijenaar RT, Bogowicz M, Jochems A, Hoebers FJ, Wesseling FW, Huang SH, et al. Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study. Br J Radiol. 2018;91:20170498.
Not in our library10.1259/bjr.20170498
123.
Al Ajmi E, Forghani B, Reinhold C, Bayat M, Forghani R. Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm. Eur Radiol. 2018;28:2604-2611.
Not in our library10.1007/s00330-017-5214-0
124.
Wang G, He L, Yuan C, Huang Y, Liu Z, Liang C. Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma. Eur J Radiol. 2018;98:100-106.
Not in our library10.1016/j.ejrad.2017.11.007
125.
Zhang B, Tian J, Dong D, Gu D, Dong Y, Zhang L, et al. Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res. 2017;23:4259-4269.
Not in our library10.1158/1078-0432.ccr-16-2910
126.
Leijenaar RTH, Carvalho S, Hoebers FJP, Aerts HJWL, van Elmpt WJC, Huang SH, et al. External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. Acta Oncol. 2015;54:1423-1429.
Not in our library10.3109/0284186x.2015.1061214
127.
Bogowicz Marta, Riesterer Oliver, Stark Luisa Sabrina, Studer Gabriela, Unkelbach Jan, Guckenberger Matthias, Tanadini-Lang Stephanie. Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma. Acta Oncologica. 2017;56(11):1531-1536.
Not in our library10.1080/0284186x.2017.1346382
128.
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer · 2017
No notice
129.
Ouyang F-S, Guo B-L, Zhang B, Dong Y-H, Zhang L, Mo X-K, et al. Exploration and validation of radiomics signature as an independent prognostic biomarker in stage III-IVb nasopharyngeal carcinoma. Oncotarget. 2017;8:74869-74879.
Not in our library10.18632/oncotarget.20423
130.
van Dijk LV, Thor M, Steenbakkers RJHM, Apte A, Zhai TT, Borra R, et al. Parotid gland fat related magnetic resonance image biomarkers improve prediction of late radiation-induced xerostomia. Radiother Oncol. 2018;128:459-466.
Not in our library10.1016/j.radonc.2018.06.012
131.
Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol. 2018;19:1180-1191.
Not in our library10.1016/s1470-2045(18)30413-3
132.
Colen RR, Fujii T, Bilen MA, Kotrotsou A, Abrol S, Hess KR, et al. Radiomics to predict immunotherapy-induced pneumonitis: proof of concept. Investig New Drugs. 2018;36:601-607.
Not in our library10.1007/s10637-017-0524-2
133.
Alilou M, Beig N, Orooji M, Rajiah P, Velcheti V, Clinic C, et al. An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT. Med Phys. 2017;44:3556-3569.
Not in our library10.1002/mp.12208
134.
Novel high-resolution computed tomography-based radiomic classifier for screen-identified pulmonary nodules in the National Lung Screening Trial. · 2018
Not checked
135.
Tu Shu-Ju, Wang Chih-Wei, Pan Kuang-Tse, Wu Yi-Cheng, Wu Chen-Te. Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening. Physics in Medicine & Biology. 2018;63(6):065005.
Not in our library10.1088/1361-6560/aaafab
136.
Carter BW, Godoy MC, Erasmus JJ. Predicting malignant nodules from screening CTs. J Thorac Oncol. 2016;11:2045-2047.
Not in our library10.1016/j.jtho.2016.09.117
137.
Orooji M, Alilou M, Rakshit S, Beig N, Khorrami MH, Rajiah P, et al. Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. J Med Imaging. 2018;5:1.
Not in our library10.1117/1.jmi.5.2.024501
138.
Chen X, Fang M, Dong D, Wei X, Liu L, Xu X, et al. A radiomics signature in preoperative predicting degree of tumor differentiation in patients with non–small cell lung cancer. Acad Radiol. 2018;25:1548-1555.
Not in our library10.1016/j.acra.2018.02.019
139.
Yuan M, Zhang Y-D, Pu X-H, Zhong Y, Li H, Wu J-F, et al. Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival. Eur Radiol. 2017;27(11):4857-4865.
Not in our library10.1007/s00330-017-4855-3
140.
Zhang L, Chen B, Liu X. Quantitative biomarkers for prediction of epidermal growth factor receptor mutation in non- small cell lung cancer. Transl Oncol. 2018;11:94-101.
Not in our library10.1016/j.tranon.2017.10.012
141.
Defining the biological basis of radiomic phenotypes in lung cancer · 2017
No notice
142.
Rios Velazquez E, Parmar C, Liu Y, Coroller TP, Cruz G, Stringfield O, et al. Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res. 2017;77:3922-3930.
Not in our library10.1158/0008-5472.can-17-0122
143.
She Y, Zhang L, Zhu H, Dai C, Xie D, Xie H, et al. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules. Eur Radiol. 2018;28:5121-5128.
Not in our library10.1007/s00330-018-5509-9
144.
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology · 2016
No notice
145.
Kirienko M, Cozzi L, Rossi A, Voulaz E, Antunovic L, Fogliata A, et al. Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging. 2018;45:1649-1660.
Not in our library10.1007/s00259-018-3987-2
146.
Raniery J, Junior F, Koenigkam-santos M, Enrique F, Cipriano G, Todorovic A, et al. Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Comput Methods Prog Biomed. 2018;159:23-30.
Not in our library10.1016/j.cmpb.2018.02.015
147.
Song So Hee, Park Hyunjin, Lee Geewon, Lee Ho Yun, Sohn Insuk, Kim Hye Seung, Lee Seung Hak, Jeong Ji Yun, Kim Jhingook, Lee Kyung Soo, Shim Young Mog. Imaging Phenotyping Using Radiomics to Predict Micropapillary Pattern within Lung Adenocarcinoma. Journal of Thoracic Oncology. 2017;12(4):624-632.
Not in our library10.1016/j.jtho.2016.11.2230
148.
Fan L, Fang MJ, Li ZB, Tu WT, Wang SP, Chen WF. et al, Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule. Eur Radiol. 2018:1–9.
Not in our library10.1007/s00330-018-5530-z
149.
Zhu X, Dong D, Chen Z, Fang M, Zhang L, Song J, et al. Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol. 2018;28:2772-2778.
Not in our library10.1007/s00330-017-5221-1
150.
Ma Yuan, Feng Wei, Wu Zhiyuan, Liu Mengyang, Zhang Feng, Liang Zhigang, Cui Chunlei, Huang Jian, Li Xia, Guo Xiuhua. Intra-tumoural heterogeneity characterization through texture and colour analysis for differentiation of non-small cell lung carcinoma subtypes. Physics in Medicine & Biology. 2018;63(16):165018.
Not in our library10.1088/1361-6560/aad648
151.
Xue X, Yang Y, Huang Q, Cui F, Lian Y, Zhang S, et al. Use of a radiomics model to predict tumor invasiveness of pulmonary adenocarcinomas appearing as pulmonary ground-glass nodules. Biomed Res Int. 2018;2018:6803971.
Not in our library10.1155/2018/6803971
152.
Yuan M, Liu JY, Zhang T, Zhang YD, Li H, Yu TF. Prognostic impact of the findings on thin-section computed tomography in stage i lung adenocarcinoma with visceral pleural invasion. Sci Rep. 2018;8:1-9.
Not in our library10.1038/s41598-018-22853-1
153.
Liu Y, Kim J, Balagurunathan Y, Hawkins S, Stringfield O, Schabath MB, et al. Prediction of pathological nodal involvement by CT-based radiomic features of the primary tumor in patients with clinically node-negative peripheral lung adenocarcinomas. Med Phys. 2018;45:2518-2526.
Not in our library10.1002/mp.12901
154.
Yang X, Pan X, Liu H, Gao D, He J, Liang W, et al. A new approach to predict lymph node metastasis in solid lung adenocarcinoma: a radiomics nomogram. J Thorac Dis. 2018;10:S807-S819.
Not in our library10.21037/jtd.2018.03.126
155.
Coroller TTP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RTH, Hermann G, et al. CT-based radiomic signature predicts distant metastasis in lung. Radiother Oncol. 2015;114:345-350.
Not in our library10.1016/j.radonc.2015.02.015
156.
Coroller TP, Agrawal V, Huynh E, Narayan V, Lee SW, Mak RH, et al. Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC. J Thorac Oncol. 2017;12:467-476.
Not in our library10.1016/j.jtho.2016.11.2226
157.
Huynh E, Coroller TP, Narayan V, Agrawal V, Hou Y, Romano J, et al. CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Int J Radiat. 2016;120:258-266.
Not in our library10.1016/j.radonc.2016.05.024
158.
Huang Yanqi, Liu Zaiyi, He Lan, Chen Xin, Pan Dan, Ma Zelan, Liang Cuishan, Tian Jie, Liang Changhong. Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non—Small Cell Lung Cancer. Radiology. 2016;281(3):947-957.
Not in our library10.1148/radiol.2016152234
159.
Yu W, Tang C, Hobbs BP, Li X, Koay EJ, Wistuba II, et al. Development and validation of a predictive radiomics model for clinical outcomes in stage I non-small cell lung cancer. Int J Radiat Oncol Biol Phys. 2018;102:1090-1097.
Not in our library10.1016/j.ijrobp.2017.10.046
160.
Virginia BM, Laura F, Silvia R, Roberto F, Francesco F, Eva H, et al. Prognostic value of histogram analysis in advanced non-small cell lung cancer: a radiomic study. Oncotarget. 2018;9:1906-1914.
Not in our library10.18632/oncotarget.22316
161.
Arshad MA, Thornton A, Lu H, Tam H, Wallitt K, Rodgers N, et al. Discovery of pre-therapy 2-deoxy-2-18F-fluoro-D-glucose positron emission tomography-based radiomics classifiers of survival outcome in non-small-cell lung cancer patients. Eur J Nucl Med Mol Imaging. 2018;46:455-466.
Not in our library10.1007/s00259-018-4139-4
162.
Tang C, Hobbs B, Amer A, Li X, Behrens C, Canales JR, et al. Development of an immune-pathology informed radiomics model for non-small cell lung cancer. Sci Rep. 2018;8:1-9.
Not in our library10.1038/s41598-018-20471-5
163.
Carvalho S, Leijenaar RTH, Troost EGC, Van Timmeren JE, Oberije C, Van Elmpt W, et al. 18FDG-PET-radiomics of metastatic lymph nodes and primary tumour in NSCLC - a prospective externally validated study. PLoS One. 2018;13:e0192859.
Not in our library10.1371/journal.pone.0192859
164.
Mattonen SA, Palma DA, Johnson C, Louie AV, Landis M, Rodrigues G, et al. Detection of local cancer recurrence after stereotactic ablative radiation therapy for lung cancer: physician performance versus radiomic assessment. Int J Radiat Oncol Biol Phys. 2016;94:1121–8.
Not in our library10.1016/j.ijrobp.2015.12.369
165.
van Timmeren JE, Leijenaar RTH, van Elmpt W, Reymen B, Oberije C, Monshouwer R, et al. Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images. Radiother Oncol. 2017;123:363–9.
Not in our library10.1016/j.radonc.2017.04.016
166.
Chaddad A, Desrosiers C, Toews M, Abdulkarim B. Predicting survival time of lung cancer patients using radiomic analysis. Oncotarget. 2017;8:104393-104407.
Not in our library10.18632/oncotarget.22251
167.
Kirienko M, Cozzi L, Antunovic L, Lozza L, Fogliata A, Voulaz E, et al. Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging. 2018;45:207-217.
Not in our library10.1007/s00259-017-3837-7
168.
Krafft SP, Rao A, Stingo F, Briere TM, Court LE, Liao Z, et al. The utility of quantitative CT radiomics features for improved prediction of radiation pneumonitis. Med Phys. 2018;45:5317-5324.
Not in our library10.1002/mp.13150
169.
Guo J, Liu Z, Shen C, Li Z, Yan F, Tian J, et al. MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation. Eur Radiol. 2018;28:3872-3881.
Not in our library10.1007/s00330-018-5381-7
170.
Lucidarme O, Akakpo JP, Granberg S, Sideri M, Levavi H, Schneider A, et al. A new computer-aided diagnostic tool for non-invasive characterisation of malignant ovarian masses: results of a multicentre validation study. Eur Radiol. 2010;20:1822-1830.
Not in our library10.1007/s00330-010-1750-6
171.
Chakraborty J, Midya A, Gazit L, Attiyeh M, Langdon-Embry L, Allen PJ, et al. CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas. Med Phys. 2018;45:5019-5029.
Not in our library10.1002/mp.13159
172.
Bonekamp D, Kohl S, Wiesenfarth M, Schelb P, Radtke JP, Götz M, et al. Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values. Radiology. 2018;289:128-137.
Not in our library10.1148/radiol.2018173064
173.
Shiradkar R, Ghose S, Jambor I, Taimen P, Ettala O, Purysko AS, et al. Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: preliminary findings. J Magn Reson Imaging. 2018;48:1626-1636.
Not in our library10.1002/jmri.26178
174.
Corino VDA, Montin E, Messina A, Casali PG, Gronchi A, Marchianò A, et al. Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions. J Magn Reson Imaging. 2018;47:829-840.
Not in our library10.1002/jmri.25791
175.
Wu Y, Xu L, Yang P, Lin N, Huang X, Pan W, et al. Survival prediction in high-grade osteosarcoma using radiomics of diagnostic computed tomography. EBioMedicine. 2018;34:27-34.
Not in our library10.1016/j.ebiom.2018.07.006
176.
Iyatomi H, Oka H, Celebi ME, Hashimoto M, Hagiwara M, Tanaka M, et al. An improved internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm. Comput Med Imaging Graph. 2008;32:566-579.
Not in our library10.1016/j.compmedimag.2008.06.005
177.
Liu YY, Ishikawa H, Chen M, Wollstein G, Duker JS, Fujimoto JG, et al. Computerized macular pathology diagnosis in spectral domain optical coherence tomography scans based on multiscale texture and shape features. Investig Ophthalmol Vis Sci. 2011;52:8316-8322.
Not in our library10.1167/iovs.10-7012
178.
Bodduluri S, Newell JD, Hoffman EA, Reinhardt JM. Registration-based lung mechanical analysis of chronic obstructive pulmonary disease (COPD) using a supervised machine learning framework. Acad Radiol. 2013;20:527-536.
Not in our library10.1016/j.acra.2013.01.019
179.
Bocchi L, Nori J. Shape analysis of microcalcifications using radon transform. Med Eng Phys. 2007;29:691-698.
Not in our library10.1016/j.medengphy.2006.07.012
180.
Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma · 2017
No notice
181.
A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme · 2017
No notice
182.
Ning Z, Luo J, Li Y, Han S, Feng Q, Xu Y, et al. Pattern classification for gastrointestinal stromal tumors by integration of radiomics and deep convolutional features. IEEE J Biomed Heal Informatics. 2018:1–1.
Not in our library10.1109/jbhi.2018.2841992
183.
Huynh BQ, Li H, Giger ML. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging. 2016;3:034501.
Not in our library10.1117/1.jmi.3.3.034501
184.
Kirienko M, Sollini M, Silvestri G, Mognetti S, Voulaz E, Antunovic L, et al. Convolutional neural networks promising in lung cancer T-parameter assessment on baseline FDG-PET/CT. Contrast Media Mol Imaging. 2018;2018:1-6.
Not in our library10.1155/2018/1382309
185.
Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology. 2018;286:800-809.
Not in our library10.1148/radiol.2017171920
186.
Srinivasan GN, Shobha G. Statistical texture analysis. Proc WORLD Acad Sci Eng Technol. 2008;36:1264-1269.
Not in our library
187.
Way TW, Sahiner B, Hadjiiski LM, Chan H-P. Effect of finite sample size on feature selection and classification: a simulation study. Med Phys. 2010;37:907-920.
Not in our library10.1118/1.3284974
188.
Chan H-P, Sahiner B, Wagner RF, Petrick N. Classifier design for computer-aided diagnosis: effects of finite sample size on the mean performance of classical and neural network classifiers. Med Phys. 1999;26:2654-2668.
Not in our library10.1118/1.598805
189.
Mazurowski MA, Habas PA, Zurada JM, Lo JY, Baker JA, Tourassi GD. Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw. 2008;21:427-436.
Not in our library10.1016/j.neunet.2007.12.031
190.
Brazauskas R, Logan BR. Observational studies: matching or regression? Biol Blood Marrow Transplant. 2016;22:557-563.
Not in our library10.1016/j.bbmt.2015.12.005
191.
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749-762.
Not in our library10.1038/nrclinonc.2017.141
192.
Vallieres M, Zwanenburg A, Badic B, Cheze-Le Rest C, Visvikis D, Hatt M. Responsible radiomics research for faster clinical translation. J Nucl Med. 2018;59(2):189-193.
Not in our library10.2967/jnumed.117.200501
193.
Zwanenburg A, Leger S, Vallières M, Löck S. Initiative for the IBS. Image biomarker standardisation initiative. arXiv:1612.07003.
Not in our library

Cited by papers we hold 6

Joint EANM/SNMMI guideline on radiomics in nuclear medicine · 2022
No notice
Radiomic analysis for predicting prognosis of colorectal cancer from preoperative 18F-FDG PET/CT · 2022
Not checked
Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results. · 2021
Not checked
Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis · 2021
No notice
Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives · 2020
No notice
Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology · 2020
Not checked

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.