CT habitat radiomics and topological data analysis based on interpretable machine learning for prediction of pancreatic ductal adenocarcinoma pathological grading

作者：Jiadong Song, Tianyu Zhao, Meng Zhang, Jinzhi Yang, Aonan Zhu, Xin Qi, Chao Yang, Yang Dong · 发表于：BMC Medical Imaging · 年份：2025 · DOI：10.1186/s12880-025-02094-1 · 被引用次数：2 · 研究领域：Pancreatic and Hepatic Oncology Research、Radiomics and Machine Learning in Medical Imaging、Advanced X-ray and CT Imaging

BACKGROUND: This study explores the feasibility and effectiveness of an interpretable machine learning model for assessing the pathological grading of pancreatic ductal adenocarcinoma (PDAC) using radiomics and topological features derived from contrast-enhanced CT habitat subregions. METHODS: A retrospective study was conducted on a total of 306 patients with PDAC from two hospitals: a training cohort (n = 176), a validation cohort (n = 76), and a test cohort (n = 54). K-means clustering analysis was first used to segment portal venous phase CT images into three habitat regions. Radiomics features of the whole-tumour region, along with radiomics and topological features of each habitat region, were extracted respectively. LASSO regression was applied for feature dimensionality reduction to construct the radiomics score (Rad-score) for the whole-tumour region and the habitat score (H-score) for each habitat region. Meanwhile, logistic regression was used to identify statistically significant predictors from clinical and semantic features. Five machine learning algorithms were used to construct Habitat-TDA models, with interpretability analysis performed via SHAP analysis. RESULTS: Total volume, diabetes, and M staging were identified as independent risk factors for predicting the pathological grading of PDAC, and were used to construct the Clinical model. 6 radiomics features with non-zero coefficients were selected to calculate the Rad-score, which was further used to constr...