{"title":"Pre-operative Overall Survival Prediction of Diffuse Glioma Enhanced by Longitudinal Data.","authors":"Zhenyu Tang, Jiannan Li, Jingliang Cheng, Zhi-Cheng Li, Zhenyu Zhang, Jing Yan","doi":"10.1109/JBHI.2025.3550937","DOIUrl":null,"url":null,"abstract":"<p><p>Many pre-operative overall survival (OS) prediction methods have been proposed to assist personalized treatment of diffuse glioma for better prognosis. Most of them utilize pre-operative data, while post-operative data, which contains essential prognosis-related information (e.g., surgical outcomes and lesion evolution) is neglected, hindering prediction accuracy. However, incorporating post-operative data could make OS prediction inapplicable at pre-operative stage, affecting clinical utility. To address this contradiction, in this paper, we propose an effective framework that leverages longitudinal data (pre- and post-operative data) to enhance pre-operative OS prediction. Specifically, two OS prediction networks are built in a knowledge distillation framework. One is the teacher network trained with longitudinal data, and the other is the student network relying solely on pre-operative data. Distillation of deep features is conducted to align the performance of the student network with that of the teacher network. Moreover, mass effect and its distillation are adopted to incorporate lesion evolution information, further enhancing prediction performance. Based on our framework, the student network can leverage essential post-operative information without compromising its applicability at pre-operative stage. Experiments on both in-house and public datasets demonstrate that the student network outperforms all state-of-the-art methods under evaluation with statistical significance. Further ablation study reveals that distillation of mass effect and deep features play positive roles in OS prediction. Moreover, new prognosis-related factors are discovered by comparing the student network with and without distillation. Codes are available at https://github.com/LiJiannan2000/OSPred.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7000,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Biomedical and Health Informatics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/JBHI.2025.3550937","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Many pre-operative overall survival (OS) prediction methods have been proposed to assist personalized treatment of diffuse glioma for better prognosis. Most of them utilize pre-operative data, while post-operative data, which contains essential prognosis-related information (e.g., surgical outcomes and lesion evolution) is neglected, hindering prediction accuracy. However, incorporating post-operative data could make OS prediction inapplicable at pre-operative stage, affecting clinical utility. To address this contradiction, in this paper, we propose an effective framework that leverages longitudinal data (pre- and post-operative data) to enhance pre-operative OS prediction. Specifically, two OS prediction networks are built in a knowledge distillation framework. One is the teacher network trained with longitudinal data, and the other is the student network relying solely on pre-operative data. Distillation of deep features is conducted to align the performance of the student network with that of the teacher network. Moreover, mass effect and its distillation are adopted to incorporate lesion evolution information, further enhancing prediction performance. Based on our framework, the student network can leverage essential post-operative information without compromising its applicability at pre-operative stage. Experiments on both in-house and public datasets demonstrate that the student network outperforms all state-of-the-art methods under evaluation with statistical significance. Further ablation study reveals that distillation of mass effect and deep features play positive roles in OS prediction. Moreover, new prognosis-related factors are discovered by comparing the student network with and without distillation. Codes are available at https://github.com/LiJiannan2000/OSPred.
人们提出了许多术前总生存期(OS)预测方法,以辅助弥漫性胶质瘤的个性化治疗,改善预后。这些方法大多利用术前数据,而包含重要预后相关信息(如手术结果和病变演变)的术后数据却被忽视,从而影响了预测的准确性。然而,纳入术后数据可能会使 OS 预测不适用于术前阶段,从而影响临床实用性。为了解决这一矛盾,我们在本文中提出了一个有效的框架,利用纵向数据(术前和术后数据)来增强术前 OS 预测。具体来说,我们在知识提炼框架中构建了两个 OS 预测网络。一个是利用纵向数据训练的教师网络,另一个是仅依靠术前数据的学生网络。对深度特征进行蒸馏,使学生网络的性能与教师网络的性能保持一致。此外,我们还采用了质量效应及其蒸馏方法来纳入病变演变信息,从而进一步提高预测性能。基于我们的框架,学生网络可以利用重要的术后信息,而不影响其在术前阶段的适用性。在内部和公共数据集上进行的实验表明,学生网络在统计意义上优于所有接受评估的最先进方法。进一步的消融研究表明,质量效应和深度特征的提炼在 OS 预测中发挥了积极作用。此外,通过比较有蒸馏和无蒸馏的学生网络,还发现了与预后相关的新因素。代码见 https://github.com/LiJiannan2000/OSPred。
期刊介绍:
IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.