Computer-aided cancer survival risk prediction plays an important role in the timely treatment of patients. This is a challenging weakly supervised ordinal regression task associated with multiple clinical factors involved such as pathological images, genomic data and etc. In this paper, we propose a new training method, multimodal object-level contrast learning, for cancer survival risk prediction. First, we construct contrast learning pairs based on the survival risk relationship among the samples in the training sample set. Then we introduce the object-level contrast learning method to train the survival risk predictor. We further extend it to the multimodal scenario by applying cross-modal constrast. Considering the heterogeneity of pathological images and genomics data, we construct a multimodal survival risk predictor employing attention-based and self-normalizing based nerural network respectively. Finally, the survival risk predictor trained by our proposed method outperforms state-of-the-art methods on two public multimodal cancer datasets for survival risk prediction.
Supervised Machine Learning for Breast Cancer Risk Factors Analysis and Survival Prediction
LYRICA Laboratory（LYRICA实验室）；School of Information Sciences（信息科学学院）；Laboratoire Pathologie Humaine, Biomedecine et Environnement（人类病理、生物医学与环境实验室）；Faculty of Medicine and Pharmacy（医学与药学院）
Predicting Breast Cancer Survival: A Survival Analysis Approach Using Log Odds and Clinical Variables
Federal College of Animal Health and Production Technology（联邦动物卫生与生产技术学院）；Universidad Mayor de San Andres（圣安德烈斯大学）；National University of Computer & Emerging Sciences-FAST（国立计算机与新兴科学大学（FAST大学））；University of Khartoum（喀土穆大学）；University of Jazan（贾赞大学）；New York Institute of Technology（纽约理工学院）；Khalifa Gull Nawaz Teaching Hospital（哈利法·古尔·纳瓦兹教学医院）
SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与通信工程学院）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）
A Retrospective Analysis using Deep-Learning Models for Prediction of Survival Outcome and Benefit of Adjuvant Chemotherapy in Stage II/III Colorectal Cancer
University of Science and Technology of China（中国科学技术大学）；UNSW Sydney（悉尼新南威尔士大学）；Genmab Inc.（Genmab公司）
Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery
Big Data Institute, University of Oxford（牛津大学大数据研究所）；Trinity St. James’s Cancer Institute, Trinity College Dublin and St. James’s Hospital（都柏林三一学院圣詹姆斯癌症研究所及圣詹姆斯医院）；Nuffield Department of Surgery, University of Oxford（牛津大学纳菲尔德外科系）；Young Investigator Division, European Society for Diseases of the Esophagus（欧洲食管疾病学会青年研究者部）
Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Leeds Teaching Hospitals NHS Trust（利兹教学医院国民医疗服务信托）；University of Leeds（利兹大学）；Linköping University（林雪平大学）
Deep learning-based survival prediction for multiple cancer types using histopathology images
Google Health（谷歌健康）；Advanced Clinical；Tempus Labs Inc（Tempus实验室公司）
诊断时的预后信息对癌症治疗和监测具有重要意义。虽然癌症分期，组织病理学评估，分子特征和临床变量可以提供有用的预后见解，改善风险分层仍然是一个活跃的研究领域。我们开发了一个深度学习系统（DLS）来预测癌症基因组图谱（TCGA）中10种癌症类型的疾病特异性生存率。我们使用了一种没有像素级注释的弱监督方法，并测试了三种不同的生存损失函数。使用来自3，664例病例的9，086张载玻片开发了DLS，并使用来自1，216例病例的3，009张载玻片进行了评价。在包括所有10种癌症的组合队列的多变量Cox回归分析中，在校正癌症类型、分期、年龄和性别后，DLS与疾病特异性生存率显著相关（风险比为1.58，95%CI 1.28-1.70，p<0.0001）。在每种癌症调整的亚组分析中，DLS仍然是10种癌症中5种癌症的生存率的重要预测因素。与包括分期、年龄和性别的基线模型相比，模型的c指数在合并队列中显示出绝对3.7%的改善（95% CI 1.0-6.5）。此外，我们的模型在各个癌症阶段对患者进行了分层，特别是II期（p=0.025）和III期（p<0.001）。通过开发和评估多种癌症类型的预后模型，这项工作是探索使用深度学习和组织病理学图像直接预测临床结果的最全面的研究之一。我们的分析表明，这种方法有可能在多种癌症类型，甚至在特定的病理阶段提供预后信息。然而，鉴于临床事件的数量相对较少，我们观察到了…
CoC: Chain-of-Cancer based on Cross-Modal Autoregressive Traction for Survival Prediction
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Xi'an Jiaotong University（西安交通大学）；The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong University of Science and Technology（香港科技大学）
Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Meta Platforms Inc.（元平台公司）
Guiding Treatment Strategies: The Role of Adjuvant Anti-Her2 Neu Therapy and Skin/Nipple Involvement in Local Recurrence-Free Survival in Breast Cancer Patients
Patient level simulation and reinforcement learning to discover novel strategies for treating ovarian cancer
Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine（纽约大学格罗斯曼医学院维尔切克生物医学科学研究生院）；NYU Langone Health System（纽约大学朗格尼医疗系统）；Merck & Co., Inc（默克公司）；New York University（纽约大学）
EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers
Saw Swee Hock School of Public Health, National University of Singapore（新加坡国立大学陈笃生公共卫生学院）；Université de Rouen Normandie（鲁昂诺曼底大学）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究院）
Bipartite Patient-Modality Graph Learning with Event-Conditional Modelling of Censoring for Cancer Survival Prediction
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University（湖南省级生物信息学重点实验室，计算机科学与工程学院，中南大学）；Xinjiang Engineering Research Center of Big Data and Intelligent Software, School of Software, Xinjiang University（新疆大数据与智能软件工程研究中心，软件学院，新疆大学）
Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet（卡罗林斯卡研究所医学流行病学与生物统计学系）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Division of Oncology, Department of Clinical Sciences Lund, Lund University（卢德大学临床科学系肿瘤学系）；Oxford Suzhou Centre for Advanced Research, University of Oxford（牛津大学苏州先进研究中心）
