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.