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

Abstract

Rationale and Objectives

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.

Materials and Methods

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.

Results

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.

Conclusion

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