Clinical Calculator Based on Molecular and Clinicopathologic Characteristics Predicts Recurrence Following Resection of Stage I-III Colon Cancer.

Clinical calculators and nomograms have been endorsed by the American Joint Committee on Cancer (AJCC), as they provide the most individualized and accurate estimate of patient outcome. Using molecular and clinicopathologic variables, a third-generation clinical calculator was built to predict recurrence following resection of stage I-III colon cancer. Prospectively collected data from 1,095 patients who underwent colectomy between 2007 and 2014 at Memorial Sloan Kettering Cancer Center were used to develop a clinical calculator. Discrimination was measured with concordance index, and variability in individual predictions was assessed with calibration curves. The clinical calculator was externally validated with a patient cohort from Washington University's Siteman Cancer Center in St Louis. The clinical calculator incorporated six variables: microsatellite genomic phenotype; AJCC T category; number of tumor-involved lymph nodes; presence of high-risk pathologic features such as venous, lymphatic, or perineural invasion; presence of tumor-infiltrating lymphocytes; and use of adjuvant chemotherapy. The concordance index was 0.792 (95% CI, 0.749 to 0.837) for the clinical calculator, compared with 0.708 (95% CI, 0.671 to 0.745) and 0.757 (0.715 to 0.799) for the staging schemes of the AJCC manual's 5th and 8th editions, respectively. External validation confirmed robust performance, with a concordance index of 0.738 (95% CI, 0.703 to 0.811) and calibration plots of predicted probability and observed events approaching a 45° diagonal. This third-generation clinical calculator for predicting cancer recurrence following curative colectomy successfully incorporates microsatellite genomic phenotype and the presence of tumor-infiltrating lymphocytes, resulting in improved discrimination and predictive accuracy. This exemplifies an evolution of a clinical calculator to maintain relevance by incorporating emerging variables as they become validated and accepted in the oncologic community.