ArticleNeuro-oncology advances
MultiCubeNet: Multitask deep learning for molecular subtyping and prognostic prediction in gliomas.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comparing artificial intelligence and physician performance in predicting IDH mutation status in glioma.NPJ digital medicine · 2026Article
- From Anatomy to Genomics Using a Multi-Task Deep Learning Approach for Comprehensive Glioma Profiling.Bioengineering (Basel, Switzerland) · 2025Article
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Authors and funding
9 authors.
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Abstract
Background: Gliomas, the most prevalent type of primary brain tumors, require precise molecular characterization for effective diagnosis and treatment. Despite advancements in radiomics, simultaneous prediction of key molecular markers, such as isocitrate dehydrogenase ( Methods: We conducted a retrospective analysis of 457 adult-type diffuse gliomas (193 training cohorts; 162 and 102 cases in SZS and The Cancer Genome Atlas (TCGA) validation cohorts, respectively). We developed MultiCubeNet, a multisequence, multiscale, multitask DL framework designed to predict Results: The median age of the patients was 49 years, and 266 were men (58.2%). The model demonstrated high efficiency in the training set, achieving AUCs of 0.966 for Conclusions: MultiCubeNet, a multitask DL model leveraging multisequence and multiscale magnetic resonance imaging, demonstrated strong performance in predicting key molecular markers and prognosis in gliomas, thereby supporting personalized treatment approaches.
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