CT-based habitat radiomics for preoperative differentiation of adenocarcinoma in situ/minimally invasive adenocarcinoma from invasive adenocarcinoma manifesting as ground-glass nodules: a multicenter study

作者：Ning Dong, Yumao Yan, Yunxin Li, Guochao Li, Ping Wang, Lin Li, Hu Zhang, Hui Sheng, Xiaoyuan Sun · 发表于：Frontiers in Oncology · 年份：2025 · DOI：10.3389/fonc.2025.1660071 · 被引用次数：3 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Colorectal Cancer Surgical Treatments、Colorectal and Anal Carcinomas

Objectives: To develop a CT-based habitat radiomics model for preoperative differentiation of adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) from invasive adenocarcinoma (IAC) manifesting as ground-glass nodules (GGNs), and to construct a combined model integrating clinical risk factors for optimizing individualized treatment decisions. Methods: We retrospectively collected imaging and clinical data from 630 patients with pathologically confirmed ground-glass nodules (GGNs) who underwent surgical resection at two medical centers between January 2020 and December 2024. Patients from Center 1 were randomly divided into training and internal validation sets at a 7:3 ratio, while patients from Center 2 served as the external validation set. Tumor habitats were generated using K-means clustering, and radiomics features were extracted from intratumoral, peritumoral 1mm, peritumoral 2mm, and habitat regions. Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and predictive models were constructed using multiple machine learning algorithms. A combined nomogram was developed by integrating the Habitat model, Intratumoral model, and Clinic model. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: In the training set, the Combined model demonstrated optimal performance (AUC = 0.928), followed by the Habitat model (A...