ArticleBMC medical imaging2026
Exploring the value of peritumoral brain zone for classification of two malignant brain tumors based on the MRI interpretable models.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
objectivesThis study aims to develop and validate a novel multimodal interpretable artificial intelligence model capable of fusing radiomics features and imaging features to accurately classify primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM), and to explore the impact of including the tumor and different peritumoral brain zones (PBZs) on the diagnostic performance of the model. MATERIALS AND
methodsA retrospective cohort of 242 patients with PCNSL or GBM, along with their multi-sequence MRI scans and clinical data, was enrolled from two medical centers. After image preprocessing, tumor and PBZ regions were segmented as volumes of interest (VOIs), and imaging features were evaluated. Based on different combinations of three image sequences and four VOIs, a total of seven models (T1WI-Tumor-Model, T2WI-Tumor-Model, T1CE-Tumor-Model, T1CE-Tumor-Edema-Model, T1CE-Tumor-PBZ10mm-Model, T1CE-Tumor-PBZ20mm-Model, and Multimodal-Radiomics-Model) were established. Receiver operating characteristic (ROC) curves, sensitivity, specificity, calibration curves, and Decision Curve Analysis (DCA) were used to assess the models' performance and clinical application value. Additionally, the best model was analyzed for interpretability using SHapley Additive exPlanations (SHAP).
resultsThe T1CE-Tumor-Edema-Model and Multimodal-Radiomics-Model demonstrated superior predictive performance among the seven models, with AUCs of 0.91 and 0.94 in the external validation cohort, respectively. The SHAP interpretation of the results revealed that the Rad-Score and Clinical-Rad-Score features contributed the most to the models' decision-making process.
conclusionA radiomics framework based on interpretable machine learning is proposed. Based on this framework, models combining different ranges of peritumoral brain regions with tumors were constructed for the first time, and radiomics features were fused with imaging features to improve classification accuracy and interpretability in distinguishing between PCNSL and GBM.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.