Interpretable CT habitat analysis for preoperative prediction of histological differentiation in gastric cancer.

Authors

Xiuzhen Yao, Shibao Zheng, Lianggen Xu, Cheng Yan, Xiaoyu Han, Sikai Wu, Ling Wang, Weiqun Ao

Published in

European journal of radiology. Volume 205. Pages 113231. Sep 09, 2026. Epub Sep 09, 2026.

Abstract

To develop and validate an interpretable preoperative model for predicting histological differentiation in gastric cancer using CT habitat analysis combined with clinical features.
This retrospective study included 978 patients with pathologically confirmed gastric cancer from two institutions. Patients from Center 1 (n = 678) were randomly divided into a training cohort (n = 406) and a testing cohort (n = 272), while patients from Center 2 (n = 300) served as an external validation cohort. Independent clinical predictors of histological differentiation were identified using multivariable logistic regression to construct a clinical model. Tumor habitats were generated from preoperative venous-phase CT using K-means clustering, and habitat scores, subregional volumes, and volume fractions were extracted to develop a Habitat model. Clinical and habitat features were then integrated into a nomogram.
Age, sex, tumor location, CT-detected T stage, CT-detected N stage, and CA19-9 level were independent predictors of histological differentiation. The optimal habitat partition was achieved with two clusters (k = 2) according to the Calinski-Harabasz index. In the training cohort, area under the receiver operating characteristic curve (AUC) were 0.747 (95% CI: 0.700-0.794) for the Clinical model, 0.835 (0.794-0.874) for the Habitat model, and 0.872 (0.836-0.904) for the Nomogram model. In the testing cohort, corresponding AUCs were 0.652 (0.585-0.716), 0.778 (0.723-0.831), and 0.803 (0.745-0.855). In the external validation cohort, AUCs were 0.682 (0.620-0.738), 0.831 (0.780-0.881), and 0.839 (0.791-0.887), respectively. SHapley Additive exPlanations (SHAP) analysis identified habitat-derived features as the strongest contributors to model predictions.
CT habitat analysis enables accurate preoperative prediction of histological differentiation in gastric cancer. Incorporating clinical variables further improves performance.

PMID:
42731502
Bibliographic data and abstract were imported from PubMed on 13 Sep 2026.

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