Cellularity habitat-based MRI radiomics for non-invasive grading and IDH mutation prediction in adult-type diffuse glioma

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Authors: Fangrong Liang, Xin Zhen, Jiaxin Lin, Junjie Li, Jie Zhan, Ruili Wei, Wanli Zhang, Yujie He, Mengni Wu, Yongzhou Xu, Yaou Liu, Shengsheng Lai
+1 more Ruimeng Yang
Cited by: 1
References: 36

Abstract

Magnetic resonance imaging (MRI) radiomics has shown promise in glioma grading and isocitrate dehydrogenase (IDH) mutation prediction, but traditional whole-tumor approaches overlook intratumoral heterogeneity, limiting diagnostic accuracy and interpretability. This study aims to explore cellularity habitat-based MRI radiomics for precise grading and IDH mutation status prediction in adult-type diffuse glioma (ADG). A total of 625 ADG patients were retrospectively collected. Whole-tumor volumes of interest (VOIs) were delineated on four conventional MRI sequences (T1WI, T2WI, T2-FLAIR, and CE-T1WI) and segmented into three cellularity habitats using apparent diffusion coefficient (ADC)-based K-means clustering: H 1 (low ADC), H 2 (medium ADC), and H 3 (high ADC). Radiomic features were extracted from individual and combined habitats, and predictive models were developed using a disentangled-learning-based multi-sequence fusion network (DMSFN). Performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). The optimal habitats for ADG grading (Grade 2 vs. Grade 3 + 4, Grade 2 + 3 vs. Grade 4) and IDH prediction were H 1 + 2 , H 1 + 2 , and H 2 + 3 , respectively. Combining T1WI, CE-T1WI, and T2-FLAIR sequences yielded the highest AUCs of 0.9360, 0.9605, and 0.8721 in the training set, and 0.8070, 0.8236, and 0.8180 in the independent test set. Shapley Additive exPlanation (SHAP) analysis identified key radiomic features contributing to model predictions, with CE-T1WI features consistently demonstrating high discriminative power. Integrating ADC-derived cellularity habitats with MRI radiomics significantly improves the accuracy and biological interpretability of ADG grading and IDH mutation status prediction, offering a robust, non-invasive approach for glioma characterization. Retrospectively registered.

Fields of Study & Subjects

Glioma Diagnosis and Treatment Radiomics and Machine Learning in Medical Imaging MRI in cancer diagnosis

Publication details

Journal / source
BMC Medical Imaging
Publisher
Springer Science and Business Media LLC
Volume
26
Issue
1
Publication date
31 January 2026
Language
EN