Use of a Pathomics Nomogram to Predict Postoperative Liver Metastasis in Patients with Stage III Colorectal Cancer.

BACKGROUND

Approximately 25% of patients with stage III colorectal cancer experience liver metastasis after radical resection; however, there is currently a lack of methods to predict liver metastasis. This study aims to develop and validate a pathomics nomogram to predict liver metastasis in patients with stage III colorectal cancer.

METHODS

A total of 318 enrolled patients were divided into three cohorts: a training cohort (n = 139), a validation cohort (n = 69), and an external cohort (n = 110). A competitive risk nomogram was established by the pathomics signature and clinicopathological characteristics and assessed by calibration, discrimination, and clinical usefulness.

RESULTS

A significant correlation between the pathomics signature and liver metastasis in stage III colorectal cancer was found. Multivariate Fine-Gray analysis indicated that preoperative carcinoembryonic antigen level, postoperative chemotherapy, and pathomics signature were independent predictors of liver metastasis. A competitive risk nomogram was developed to predict liver metastasis in patients with stage III colorectal cancer. The predicting nomogram shows good discrimination and calibration, with C-indexes of 0.811 (95% confidence interval [CI] 0.651-0.971), 0.759 (95% CI 0.531-0.987), and 0.845 (95% CI 0.641-0.999), with area under the receiver operating characteristic (AUROC) curves at 5 years of 0.833 (95% CI 0.742-0.925), 0.760 (95% CI 0.652-0.893), and 0.812 (95% CI 0.692-0.931) in the training, validation, and external cohorts, respectively. Compared with the clinicopathological nomogram, the nomogram combined with the pathomics signature had better performance (AUROC 0.823 [95% CI 0.764-0.881] vs. 0.678 [95% CI 0.606-0.751]; p < 0.001).

CONCLUSIONS

The pathomics signature is a predictive indicator for liver metastasis in patients with stage III colorectal cancer, and the integrated nomogram can be used to predict liver metastasis better than the clinicopathological nomogram alone.