Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients

作者：Xuewei Wu, Shuaitong Zhang, Zhenyu Zhang, Zhenyu Zhang, Zexin Xu, Zexin Xu, Weiwei Wang, Zhe Jin, Jingjing You, Yang Guo, Lu Zhang, Wenhui Huang, Fei Wang, Xianzhi Liu, Dongming Yan, Jingliang Cheng, Jing Yan, Bin Zhang, Shuixing Zhang, Bin Zhang · 发表于：npj Precision Oncology · 年份：2024 · DOI：10.1038/s41698-024-00670-2 · 被引用次数：40 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Glioma Diagnosis and Treatment、Cell Image Analysis Techniques

Deep learning models have been developed for various predictions in glioma; yet, they were constrained by manual segmentation, task-specific design, or a lack of biological interpretation. Herein, we aimed to develop an end-to-end multi-task deep learning (MDL) pipeline that can simultaneously predict molecular alterations and histological grade (auxiliary tasks), as well as prognosis (primary task) in gliomas. Further, we aimed to provide the biological mechanisms underlying the model's predictions. We collected multiscale data including baseline MRI images from 2776 glioma patients across two private (FAHZU and HPPH, n = 1931) and three public datasets (TCGA, n = 213; UCSF, n = 410; and EGD, n = 222). We trained and internally validated the MDL model using our private datasets, and externally validated it using the three public datasets. We used the model-predicted deep prognosis score (DPS) to stratify patients into low-DPS and high-DPS subtypes. Additionally, a radio-multiomics analysis was conducted to elucidate the biological basis of the DPS. In the external validation cohorts, the MDL model achieved average areas under the curve of 0.892-0.903, 0.710-0.894, and 0.850-0.879 for predicting IDH mutation status, 1p/19q co-deletion status, and tumor grade, respectively. Moreover, the MDL model yielded a C-index of 0.723 in the TCGA and 0.671 in the UCSF for the prediction of overall survival. The DPS exhibits significant correlations with activated oncogenic pathways, immu...