Concordance Between Watson for Oncology and Multidisciplinary Teams in Colorectal Cancer: Prognostic Implications and Predicting Concordance

Background Watson for Oncology (WFO) is a cognitive computing system that provides clinical decision support. This study examined the concordance between the treatment recommendations for colorectal cancer (CRC) proposed by WFO and those recommended by the multidisciplinary teams (MDTs), and evaluated the influence of concordance on the prognosis. Methods We retrospectively collected 175 patients with colorectal cancer who received treatment recommended by MDTs at a hospital in China, and evaluated them using WFO. Concordance between the two recommendations was analyzed. The overall survival was analyzed between concordant and non-concordant groups. Logistic regression analyses were performed and a concordance-predicting model was developed. Results Concordance between WFO’ and MDTs’ recommendations occurred in 66.9% (117/175) of cases. The overall survival (OS) was significantly better in concordant group and non-concordance was found to be an independent prognostic factor [hazard ratio (HR)=2.784 (95% CI 1.264–6.315)]. Logistic regression analyses determined that tumor type [odds ratio (OR)= 2.195 for left colon cancer and OR=2.502 for rectum cancer], and TNM stage (OR=0.545 for stage II, OR=0.187 for stage III, OR=0.127 for stage IV) were independently related with concordance, which were used to develop a concordance-predictive-nomogram. Conclusions Treatment recommendations for patients with colorectal cancer determined by WFO and MDTs were mostly concordant. However, the survival was better among concordant patients and non-concordance was found to be an independent prognostic factor. This study presents a nomogram that can be conveniently used for predicting individualized concordance. However, our findings should be prospectively validated in multi-center trials.

Abstract licence: CC BY

14 more references, whose wording the paper’s licence keeps private.

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.