A Hybrid Machine Learning CT-Based Radiomics Nomogram for Predicting Cancer-Specific Survival in Curatively Resected...

To develop and validate a computed tomography-based radiomics nomogram for cancer-specific survival (CSS) prediction in curatively resected colorectal cancer (CRC), and its performance was compared with the American Joint Committee on Cancer (AJCC) staging and clinical-pathological models.

A total of 794 patients with curatively resected CRC from a prospective cancer registry program were included and randomly divided into the training (n = 556) and validation (n = 238) cohorts. A radiomics signature (RS) predicting CSS was constructed with a hybrid automatic machine learning strategy, and the prognostic value was assessed with Kaplan-Meier (KM) survival analysis. The performance of the established models was assessed by the discrimination, calibration, and clinical utility.

A 10-feature-based RS with independent prognostic value was developed. KM survival curves showed that high-risk patients defined by RS had a worse CSS than the low-risk patients (log-rank P<0.001). The radiomics nomogram integrating the RS and clinical-pathological factors had the optimal performance in predicting CSS in terms of Harrell's concordance index (0.803 [95% confidence interval: 0.761-0.845] for the primary cohort, 0.772 [95% confidence interval: 0.702-0.841] for the validation cohort), time-dependent receiver operating curves (time-ROC) (the area under the time-ROC curves [AUC] at three years were 84.06±2.86 and at five years were 86.35±2.19 in the primary cohort, the AUC at three years were 77.6±4.76, and at five years were 84±3.66 in the validation cohort), calibration curves and decision curve analysis, in comparison with the AJCC staging model, clinical-pathological model, and the RS alone.

The radiomics nomogram integrating the RS and clinical-pathological factors could be a valuable individualized predictor of the CSS for curatively resected CRC patients.