Utilizing Radiomics of Peri‐Lesional Edema in T2‐FLAIR Subtraction Digital Images to Distinguish High‐Grade Glial Tumors From Brain Metastasis

• Published in: Journal of magnetic resonance imaging Vol. 61; no. 4; pp. 1728 - 1737
• Main Authors: Demirel, Emin, Dilek, Okan
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.04.2025; Wiley Subscription Services, Inc
• Subjects: Adult; Aged; Algorithms; Artificial intelligence; Brain; Brain - diagnostic imaging; Brain cancer; Brain Edema - diagnostic imaging; brain metastasis; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - secondary; Brain tumors; Diagnosis, Differential; Digital imaging; Edema; Female; Field strength; FLAIR WI; glioblastoma; Glioma; Glioma - diagnostic imaging; Glioma - pathology; Humans; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted - methods; Machine Learning; Magnetic Resonance Imaging - methods; Male; Medical imaging; Metastases; Metastasis; Middle Aged; Neoplasm Grading; Neuroimaging; Performance measurement; Population studies; Radiomics; Redundancy; Reproducibility of Results; Retrospective Studies; Sensitivity and Specificity; Statistical analysis; Statistical tests; T2 WI; Variance analysis; T2 WI; brain metastasis; glioblastoma; machine learning; FLAIR WI
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.29572

Background Differentiating high‐grade glioma (HGG) and isolated brain metastasis (BM) is important for determining appropriate treatment. Radiomics, utilizing quantitative imaging features, offers the potential for improved diagnostic accuracy in this context. Purpose To differentiate high‐grade (grade 4) glioma and BM using machine learning models from radiomics data obtained from T2‐FLAIR digital subtraction images and the peritumoral edema area. Study Type Retrospective. Population The study included 1287 patients. Of these, 602 were male and 685 were female. Of the 788 HGG patients included in the study, 702 had solitary masses. Of the 499 BM patients included in the study, 112 had solitary masses. Initially, the model was developed and tested on solitary masses. Subsequently, the model was developed and tested separately for all patients (solitary and multiple masses). Field Strength/Sequence Axial T2‐weighted fast spin‐echo sequence (T2WI) and T2‐weighted fluid‐attenuated inversion recovery sequence (T2‐FLAIR), using 1.5‐T and 3.0‐T scanners. Assessment Radiomic features were extracted from digitally subtracted T2‐FLAIR images in the area of peritumoral edema. The maximum relevance‐minimum redundancy (mRMR) method was then used for dimensionality reduction. The naive Bayes algorithm was used in model development. The interpretability of the model was explored using SHapley Additive exPlanations (SHAP). Statistical Tests Chi‐square test, one‐way analysis of variance, and Kruskal–Wallis test were performed. The P values <0.05 were considered statistically significant. The performance metrics include area under curve (AUC), sensitivity (SENS), and specificity (SPEC). Results The mean age of HGG patients was 61.4 ± 13.2 years and 61.7 ± 12.2 years for BM patients. In the external validation cohort, the model achieved AUC: 0.991, SENS: 0.983, and SPEC: 0.922. The external cohort results for patients with solitary lesions were AUC: 0.987, SENS: 0.950, and SPEC: 0.922. Data Conclusion The artificial intelligence model, developed with radiomics data from the peritumoral edema area in T2‐FLAIR digital subtraction images, might be able to differentiate isolated BM from HGG. Evidence Level 3 Technical Efficacy Stage 2