MultiCubeNet: Multitask deep learning for molecular subtyping and prognostic prediction in gliomas
Bibliographic record
Abstract
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 (IDH) mutation, 1p/19q co-deletion, and telomerase reverse transcriptase (TERT) promoter mutation, along with prognosis, remains challenging. We aimed to develop and validate a deep learning (DL) model capable of simultaneously predicting key genetic molecular markers and prognosis in gliomas. 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 IDH mutation, 1p/19q co-deletion, TERT promoter mutation, and prognosis. Model performance was benchmarked against conventional radiomics pipelines and neuroradiologist annotations. Classification accuracy was evaluated by the area under the receiver operating characteristic curve (AUC), with prognostic performance quantified using Harrell’s concordance index (C-index). 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 IDH mutation, 0.961 for 1p/19q co-deletion, and 0.851 for TERT promoter mutation. In the external test set (SZS), the model maintained strong performance with AUCs of 0.877, 0.730, and 0.705 for IDH mutation, 1p/19q co-deletion, and TERT promoter mutation, respectively. The performance in TCGA cohort was less optimal, with AUCs below 0.8. The framework consistently matched or exceeded both radiomics pipelines and neuroradiologists in molecular marker identification. Survival analysis revealed significant prognostic stratification across all cohorts (C-index: 0.706–0.866). 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".