Deep learning model for predicting the RAS oncogene status in colorectal cancer liver metastases.

Abstract

Background: To develop a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CECT) to assess the rat sarcoma (RAS) oncogene status and predict targeted therapy response in colorectal cancer liver metastases (CRLM).

Methods: This multicenter retrospective study comprised 185 CRLM patients who were categorized into three cohorts: training (n = 88), internal test (n = 39), and external test (n = 58). A total of 1126 radiomic features and 2589 DL signatures were extracted from each region of interest in the CECT. Fourteen significant radiomic features associated with RAS mutation were selected. Subsequently, various models (DL-arterial phase (AP), DL-venous phase (VP), AP+VP-DL, radiomics, and DL-R) were developed and validated. The model performance was compared using the area under the receiver operating characteristic (AUROC) curves and the DeLong test. The predictive usefulness of the DL score for progression-free survival and overall survival (OS) was determined.

Results: The AP+VP-DL model achieved the highest AUC (0.98), outperforming the radiomics (0.90), DL-AP (0.93), DL-VP (0.87), and DL-R (0.97) models. Significant associations were observed between OS and the carcinoembryonic antigen (CEA), disease control rate (DCR), and DL scores, leading to the development of a DL nomogram. A high-risk RAS mutation status correlated with significantly lower 1-year (88% vs. 96%), 3-year (12% vs. 35%), and 5-year (0% vs. 15%) cumulative survival rates compared to a low-risk status (P = 0.03).

Conclusions: The DL model demonstrated satisfactory predictive performance, aiding clinicians in noninvasively predicting the RAS gene status for informed treatment decisions.