A Clinical-Radiomic Combined Model based on Dual-Layer Spectral CT for Predicting Pathological T4 in Gastric Cancer

This study aimed to develop and validate a dual-layer spectral CT based clinical-radiomic model for pre-treatment prediction of pathological T4 (pT4) in gastric cancer (GC) patients.

This retrospective study included 148 surgically confirmed GC patients who underwent dual-layer spectral CT scanning before surgery and were divided into a training (n=104) and test (n=44) cohorts. Subjective assessments were performed based on conventional 120-kV CT images by two readers. Clinical models were developed using patient demographics, serum tumor markers, and image features from CT scans. Radiomics model included features extracted from conventional 120-kV CT and dual-layer CT-derived spectral base image (SBI), such as virtual monoenergetic images (40 keV, 70 keV, 100 keV), iodine density (ID), effective atomic number (Zeff), and electron density (ED) images for both the arterial phase (AP) and portal venous phase (PVP). A clinical-radiomic combined model was developed and visualized using a nomogram.

Tumor thickness on CT and serum level of CA19-9 levels were identified as independent predictors. The clinical-radiomic combined model demonstrated superior performance compared to subjective image interpretation and other models, with an AUC of 0.906 (95% CI, 0.848-0.963) in the training cohort and 0.873 in the test cohort. The nomogram was significantly associated with pT4 status, supporting its potential utility in clinical prediction.

The integration of clinical characteristics with radiomic features from conventional CT and dual-layer CT-derived SBI achieved a high diagnostic accuracy for predicting pT4 in GC patients. This combined approach could assist in treatment planning and patient management in GC.