Non-invasive prediction of occult peritoneal metastasis (OPM) in gastric cancer using logistic regression and random forest integrative models with CT radiomics and clinical parameters: machine learning prediction of gastric OPM.

Background: Occult peritoneal metastasis (OPM) in gastric cancer is frequently missed on preoperative contrast-enhanced computed tomography (CT), leading to understaging in about 30-40% of patients and necessitating invasive laparoscopy for confirmation. A non-invasive tool to preoperatively identify OPM would help avoid unnecessary surgery and guide individualized treatment, but existing models lack interpretability and do not compare linear Methods: We retrospectively enrolled 164 gastric adenocarcinoma patients (100 with OPM, 64 non-metastatic) from a single center. Inclusion criteria were biopsy-proven adenocarcinoma, no prior malignancy, complete clinical data, and contrast-enhanced CT at initial diagnosis showing no definite peritoneal metastasis, with OPM subsequently confirmed by laparoscopic exploration, peritoneal biopsy, or positive peritoneal lavage cytology. Exclusion criteria included prior treatment, poor-quality images, unconfirmed peritoneal lesions, other distant metastases, or remnant stomach. Nine clinical-laboratory variables [age, sex, body mass index, alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), albumin (ALB), tumor location, and tumor/node (T/N) stage] were collected, and three-dimensional (3D) radiomics features were extracted from primary-tumor CT volumes. The cohort was randomly split 7:3 into training (n=114, 70 OPM) and validation (n=50, 30 OPM) sets using stratified sampling. After intraclass correlation coefficient (ICC) (>0.75) filtering, least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation [1 - standard error (1-SE) rule] and Spearman correlation (threshold 0.75) selected six stable radiomics features from CT images. Integrated models combining age, T stage, and radiomics signature were built using five machine learning algorithms. Evaluation included area under the curve (AUC), calibration curves, Brier score, decision curve analysis (DCA), precision-recall (PR) curves, and average precision (AP). SHapley Additive exPlanations (SHAP) analysis was performed on both logistic regression (LR) and random forest (RF) models, and Friedman's H-statistic quantified feature interactions. Results: Age and T stage were independent clinical predictors. The integrated LR model achieved training/validation AUCs of 0.907/0.868, with validation sensitivity of 0.833 and specificity of 0.800. The RF model yielded training/validation AUCs of 0.839/0.857, with validation sensitivity of 0.833 and specificity of 0.800. SHAP comparison revealed crossing decision paths in LR Conclusions: This LR-based integrated model demonstrates promising performance and interpretability for preoperative OPM risk stratification in CT-negative gastric cancer patients in this single-center internal validation. Comparative SHAP analysis uncovers nonlinear dynamics and a novel paradoxical radiomics signature, providing biological insights beyond conventional prediction. However, prospective multicenter external validation is required before this approach can be considered for clinical implementation.