Differentiation of Glioblastoma and Solitary Brain Metastasis Using Brain-Tumor Interface Radiomics Features Based on MR Images: A Multicenter Study

作者：Yini Chen, Weiya Guo, Yushi Li, Hongsen Lin, Deshuo Dong, Yiwei Qi, Renwang Pu, Ailian Liu, Xing Li, Bo Sun · 发表于：Academic Radiology · 年份：2025 · DOI：10.1016/j.acra.2025.04.008 · 被引用次数：6 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Glioma Diagnosis and Treatment、MRI in cancer diagnosis

Rationale and Objectives Glioblastoma (GBM) and solitary brain metastasis (SBM) exhibit similar radiomics features on magnetic resonance imaging (MRI), yet their treatment strategies and prognoses significantly differ. Therefore, accurate differentiation between these two types of tumors is crucial for clinical decision-making. This study aims to establish and validate an efficient diagnostic model based on the radiomic features of the T1-weighted contrast-enhanced (T1CE) sequence in the 10 mm brain-tumor interface region to achieve precise differentiation between GBM and SBM. Methods This study retrospectively collected contrast-enhanced T1-weighted imaging data from 226 GBM patients and 206 SBM patients at three centers between January 2010 and October 2024. Samples from centers 1 and 2 were used as the training set, while samples from center 3 were used as the test set. Two observers manually delineated the tumor edges on the T1CE images layer by layer to obtain the Region of Interest (ROI) covering the entire tumor volume. A 10 mm brain-to-tumor interface (BTI) was extracted using Python code. Radiomic features were extracted from the 10 mm BTI region, followed by feature selection and model construction. Finally, SHAP (SHapley Additive exPlanations) was used to visualize the model. Three radiologists with 2, 6, and 18 years of diagnostic experience independently evaluated the test set samples without knowing the patient information or pathology results, establishing thre...