文献类型：期刊文献

英文题名：SAMMS: Multi-modality Deep Learning with the Foundation Model for the Prediction of Cancer Patient Survival

作者：Zhu, Wen[1,2]; Chen, Yiwen[3]; Nie, Shanling[4]; Yang, Hai[1]

机构：[1] East China University of Science and Technology, Department of Computer Science and Engineering, Shanghai, China; [2] Shanghai Key Laboratory of Computer Software Evaluating and Testing, Shanghai, China; [3] National University of Singapore, Center for Continuing and Lifelong Education, Singapore; [4] The University of Sydney, Faculty of Engineering, Australia

年份：2023

起止页码：3662

外文期刊名：Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

收录：EI(收录号：20240715559952)

语种：英文

摘要：Cancer survival prediction is pivotal in tailoring individualized treatment strategies and guiding clinician decision-making. Yet, existing methodologies grapple with efficiently harnessing the intricate distribution of medical data spanning various modalities. In response, we present SAMMS, an advanced multi-omics multimodal deep learning framework tailored for survival prediction. SAMMS leverages the robust image segmentation model, "Segment Anything"to adeptly characterize pathological images. This prowess is further enhanced by integrating multi-omics data and clinical insights, facilitating holistic modeling across a diverse modal spectrum. The framework weaves a modality-specific subnetwork with a cross-modality common subnetwork, meticulously capturing intra-modality nuances and inter-modality correlations. SAMMS eclipsed its contemporaries by delivering remarkable performance on TCGA's LGG and KIRC tumor datasets. A battery of analyses underscored SAMMS's unparalleled capability to distill multifaceted insights from multimodal datasets, yielding richer and more integrative multimodal representations. Such strides promise significant advancements in cancer survival analytics, bolstering the precision and efficacy of patient-centric treatments, disease oversight, and clinical decision processes. ? 2023 IEEE.