Prediction of mucinous adenocarcinoma in colorectal cancer with mucinous components detected in preoperative biopsy diagnosis.

objectivesEndoscopic biopsy diagnosis for the preoperative assessment of mucinous components in patients with colorectal cancer is limited. This study investigated a radiomics model and established an explainable prediction model by using machine learning to differentiate between adenocarcinoma with mucinous components and mucinous adenocarcinoma.

methodsThe derivation cohort included 312 patients with colorectal cancer with mucinous components detected during preoperative endoscopic biopsy diagnosis. These patients were randomly divided into training and validation sets in a 7:3 ratio. Radiomics features were extracted, followed by feature engineering, to create a radiomic score (radscore). Subsequently, 24 features, including the radscore, clinical data, and serological characteristics, were used to develop machine learning models by using nine different machine learning algorithms. The SHapley Additive exPlanation (SHAP) method was employed to elucidate the workings of the machine learning models and visualize individual variable predictions.

resultsThe radiomics model achieved an area under the curve (AUC) of 0.810. The random forest model outperformed the other models and had the highest AUC of 0.832; thus, this model was defined as the hybrid model. The clinical model, which was built using clinical data and serological characteristics, had an AUC of 0.732, whereas the radiomics model achieved an AUC of 0.810. SHAP model interpretation revealed that among the 14 features with non-zero SHAP values, the radscore and clinical T stage had notably higher values.

conclusionThis interpretable predictive model effectively differentiates between adenocarcinoma with mucinous components and mucinous adenocarcinoma in patients with colorectal cancer, thereby facilitating informed treatment decisions for individuals in whom mucinous components are identified during preoperative biopsy diagnosis.