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

• Published in: Academic radiology Vol. 32; no. 7; p. 4164
• Main Authors: Chen, Yini, Guo, Weiya, Li, Yushi, Lin, Hongsen, Dong, Deshuo, Qi, Yiwei, Pu, Renwang, Liu, Ailian, Li, Wei, Sun, Bo
• Format: Journal Article
• Language: English
• Published: United States 01.07.2025
• Subjects: Adult; Aged; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - secondary; Contrast Media; Diagnosis, Differential; Female; Glioblastoma - diagnostic imaging; Glioblastoma - pathology; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Middle Aged; Radiomics; Retrospective Studies; Brain-to-tumor interface; Keyword3; Solitary brain metastasis; Glioblastoma
• ISSN: 1878-4046
• DOI: 10.1016/j.acra.2025.04.008

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. 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 three diagnostic models. The DeLong test was used to compare these models with the radiomic model. Ultimately, ten radiomic features were used for modeling. The model established using the logistic regression (LR) algorithm had an AUC of 0.893 on the training set and 0.808 on the test set. The AUCs of the three radiologists with different diagnostic experiences on the test set were 0.699, 0.740, and 0.789, respectively, all lower than that of the radiomic model. The DeLong test showed that Model performed significantly better than Doctor 1 (p<0.05) in the test set, but there was no statistically significant difference in performance between Model and Doctors 2 and 3. The radiomic model constructed based on the 10 mm brain-tumor interface can effectively differentiate between GBM and SBM, capturing tumor heterogeneity from a new perspective, thereby significantly improving diagnostic performance and providing assistance for clinical diagnosis. The original contributions presented in the study are included in the article/Supplemental material, further inquiries can be directed to the corresponding authors.