ArticleNPJ precision oncology2026
Radiomics model integrating MRI and ECV enhances prediction accuracy for progression in high-grade glioma.
Article in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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10 authors.
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Abstract
Glioma is the most common primary brain tumor, with high-grade glioma (HGG) posing significant clinical challenges due to its poor survival outcomes. One-year tumor recurrence indicates a poor prognosis, making accurate progression risk prediction models critical for clinical decision-making. This study aimed to develop a novel combined model (DL_com) based on the MobileNet-based Hybrid Network (MobHy-Net), integrating clinical variables and deep learning features from both T2-FLAIR and extracellular volume images to predict 1-year progression risk. Preoperative multi-sequence MRI (T1WI, T1C, and T2-FLAIR) from 193 HGG patients across two centers was analyzed. DL_com demonstrated superior predictive performance, with area under the curve values of 0.954 (training), 0.911 (validation), and 0.919 (test), significantly outperforming other models (P < 0.05). Furthermore, decision curve analysis confirmed its clinical utility, and Shapley Additive Explanations analysis enhanced its visualization and interpretability. DL_com effectively predicts 1-year progression risk in HGG, offering a valuable tool for risk stratification and clinical decision support.
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