MultiCubeNet: Multitask Deep Learning for Molecular Subtyping and Prognostic Prediction in Gliomas.

Hongbo Zhang, Beibei Zhou,Hanwen Zhang, Yuze Zhang, Ying Ouyang, Ruru Su, Xumei Tang,Yi Lei,Biao Huang

Neuro-oncology advances（2025）

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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 (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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【要点】：论文提出了MultiCubeNet模型，一种多任务深度学习框架，能够同时预测胶质瘤的关键分子标记物和预后情况，实现了对个性化治疗方案的支持。

【方法】：研究采用了一种多序列、多尺度、多任务的深度学习框架，通过回顾性分析457例成人弥漫性胶质瘤的磁共振成像（MRI）数据，训练并验证了模型。

【实验】：研究使用了193例训练队列和两个验证队列（SZS队列162例，The Cancer Genome Atlas（TCGA）队列102例）的数据。模型在训练集上对IDH突变、1p/19q共缺失和TERT启动子突变的预测AUC分别达到0.966、0.961和0.851，在SZS外部测试集上的AUC分别为0.877、0.730和0.705，在TCGA队列上的AUC低于0.8。生存分析显示，模型在所有队列中均具有显著的预后分层能力（C-index：0.706-0.866）。