Cancer survival prediction is an active area of research that can help prevent unnecessary therapies and improve patient's quality of life. Gene expression profiling is being widely used in cancer studies to discover informative biomarkers that aid predict different clinical endpoint prediction. We use multiple modalities of data derived from RNA deep-sequencing (RNA-seq) to predict survival of cancer patients. Despite the wealth of information available in expression profiles of cancer tumors, fulfilling the aforementioned objective remains a big challenge, for the most part, due to the paucity of data samples compared to the high dimension of the expression profiles. As such, analysis of transcriptomic data modalities calls for state-of-the-art big-data analytics techniques that can maximally use all the available data to discover the relevant information hidden within a significant amount of noise. In this paper, we propose a pipeline that predicts cancer patients' survival by exploiting the structure of the input (manifold learning) and by leveraging the unlabeled samples using Laplacian support vector machines, a graph-based semi supervised learning (GSSL) paradigm. We show that under certain circumstances, no single modality per se will result in the best accuracy and by fusing different models together via a stacked generalization strategy, we may boost the accuracy synergistically. We apply our approach to two cancer datasets and present promising results. We maintain that a similar pipeline can be used for predictive tasks where labeled samples are expensive to acquire.
Breast cancer is the most frequently reported cancer type among the women around the globe and beyond that it has the second highest female fatality rate among all cancer types. Despite all the progresses made in prevention and early intervention, early prognosis and survival prediction rates are still unsatisfactory. In this paper, we propose a novel type of perceptron called L-Perceptron which outperforms all the previous supervised learning methods by reaching 97.42 \% and 98.73 \% in terms of accuracy and sensitivity, respectively in Wisconsin Breast Cancer dataset. Experimental results on…
University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔机器智能研究所（Amii））
Cancer is one of the leading cause of death, worldwide. Many believe that genomic data will enable us to better predict the survival time of these patients, which will lead to better, more personalized treatment options and patient care. As standard survival prediction models have a hard time coping with the high-dimensionality of such gene expression (GE) data, many projects use some dimensionality reduction techniques to overcome this hurdle. We introduce a novel methodology, inspired by topic modeling from the natural language domain, to derive expressive features from the high-dimensional …
Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Prediction of survival for cancer patients is an open area of research. However, many of these studies focus on datasets with a large number of patients. We present a novel method that is specifically designed to address the challenge of data scarcity, which is often the case for cancer datasets. Our method is able to use unlabeled data to improve classification by adopting a semi-supervised training approach to learn an ensemble classifier. The results of applying our method to three cancer datasets show the promise of semi-supervised learning for prediction of cancer survival.
University of Toronto（多伦多大学）；University Health Network（大学健康网络）；Princess Margaret Cancer Centre（玛格丽特公主癌症中心）
Accurate survival prediction is crucial for development of precision cancer medicine, creating the need for new sources of prognostic information. Recently, there has been significant interest in exploiting routinely collected clinical and medical imaging data to discover new prognostic markers in multiple cancer types. However, most of the previous studies focus on individual data modalities alone and do not make use of recent advances in machine learning for survival prediction. We present Deep-CR MTLR -- a novel machine learning approach for accurate cancer survival prediction from multi-mo…
Indian Institute of Technology Bombay（印度理工学院孟买分校）；Case Western Reserve University（凯斯西储大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Survival models are used in various fields, such as the development of cancer treatment protocols. Although many statistical and machine learning models have been proposed to achieve accurate survival predictions, little attention has been paid to obtain well-calibrated uncertainty estimates associated with each prediction. The currently popular models are opaque and untrustworthy in that they often express high confidence even on those test cases that are not similar to the training samples, and even when their predictions are wrong. We propose a Bayesian framework for survival models that no…
Survival analysis has been developed and applied in the number of areas including manufacturing, finance, economics and healthcare. In healthcare domain, usually clinical data are high-dimensional, sparse and complex and sometimes there exists few amount of time-to-event (labeled) instances. Therefore building an accurate survival model from electronic health records is challenging. With this motivation, we address this issue and provide a new survival analysis framework using deep learning and active learning with a novel sampling strategy. First, our approach provides better representation w…
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use these models to explore the use of geographical features in predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2012. Specifically, we compare model performance using a newly defined metric -- area between the curves (ABC) -- to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predicti…
Tongue cancer is a common oral cavity malignancy that originates in the mouth and throat. Much effort has been invested in improving its diagnosis, treatment, and management. Surgical removal, chemotherapy, and radiation therapy remain the major treatment for tongue cancer. The survival of patients determines the treatment effect. Previous studies have identified certain survival and risk factors based on descriptive statistics, ignoring the complex, nonlinear relationship among clinical and demographic variables. In this study, we utilize five cutting-edge machine learning models and clinical…
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to learn relationships between attributes (physical and immunological) and the resulting tumour stage and survival. Results for conventional machine learning approaches can be considered poor, especially for predicting tumour stages for the most important types of cancer. This poor performa…
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）
Deep learning has shown remarkable results for image analysis and is expected to aid individual treatment decisions in health care. To achieve this, deep learning methods need to be promoted from the level of mere associations to being able to answer causal questions. We present a scenario with real-world medical images (CT-scans of lung cancers) and simulated outcome data. Through the sampling scheme, the images contain two distinct factors of variation that represent a collider and a prognostic factor. We show that when this collider can be quantified, unbiased individual prognosis predictio…
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use such models to explore different geographical feature representations in the context of predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2013. Specifically, we compare model performance using "area between the curves" (ABC) to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predict…
University of Birmingham（伯明翰大学）；University of Melbourne（墨尔本大学）；RMIT University（皇家墨尔本理工大学）
Survival risk prediction using gene expression data is important in making treatment decisions in cancer. Standard neural network (NN) survival analysis models are black boxes with lack of interpretability. More interpretable visible neural network (VNN) architectures are designed using biological pathway knowledge. But they do not model how pathway structures can change for particular cancer types. We propose a novel Mutated Pathway VNN or MPVNN architecture, designed using prior signaling pathway knowledge and gene mutation data-based edge randomization simulating signal flow disruption. As …
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）
Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learni…
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）
Most early-stage colorectal cancer (CRC) patients can be cured by surgery alone, and only certain high-risk early-stage CRC patients benefit from adjuvant chemotherapies. However, very few validated biomarkers are available to accurately predict survival benefit from postoperative chemotherapy. We developed a novel deep-learning algorithm (CRCNet) using whole-slide images from Molecular and Cellular Oncology (MCO) to predict survival benefit of adjuvant chemotherapy in stage II/III CRC. We validated CRCNet both internally through cross-validation and externally using an independent cohort from…
Survival month for non-small lung cancer patients depend upon which stage of lung cancer is present. Our aim is to identify smoking specific gene expression biomarkers in the prognosis of lung cancer patients. In this paper, we introduce the network elastic net, a generalization of network lasso that allows for simultaneous clustering and regression on graphs. In Network elastic net, we consider similar patients based on smoking cigarettes per year to form the network. We then further find the suitable cluster among patients based on coefficients of genes having different survival month struct…
Cancer survival prediction is important for developing personalized treatments and inducing disease-causing mechanisms. Multi-omics data integration is attracting widespread interest in cancer research for providing information for understanding cancer progression at multiple genetic levels. Many works, however, are limited because of the high dimensionality and heterogeneity of multi-omics data. In this paper, we propose a novel method to integrate multi-omics data for cancer survival prediction, called Stacked AutoEncoder-based Survival Prediction Neural Network (SAEsurv-net). In the cancer …
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Integrating cross-department multi-modal data (e.g., radiological, pathological, genomic, and clinical data) is ubiquitous in brain cancer diagnosis and survival prediction. To date, such an integration is typically conducted by human physicians (and panels of experts), which can be subjective and semi-quantitative. Recent advances in multi-modal deep learning, however, have opened a door to leverage such a process to a more objective and quantitative manner. Unfortunately, the prior arts of using four modalities on brain cancer survival prediction are limited by a "complete modalities" settin…
More than 144 000 Australians were diagnosed with cancer in 2019. The majority will first present to their GP symptomatically, even for cancer for which screening programs exist. Diagnosing cancer in primary care is challenging due to the non-specific nature of cancer symptoms and its low prevalence. Understanding the epidemiology of cancer symptoms and patterns of presentation in patient's medical history from primary care data could be important to improve earlier detection and cancer outcomes. As past medical data about a patient can be incomplete, irregular or missing, this creates additio…
Google Health（谷歌健康）；Advanced Clinical；Tempus Labs Inc（Tempus实验室公司）
Prognostic information at diagnosis has important implications for cancer treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic insights, improving risk stratification remains an active research area. We developed a deep learning system (DLS) to predict disease specific survival across 10 cancer types from The Cancer Genome Atlas (TCGA). We used a weakly-supervised approach without pixel-level annotations, and tested three different survival loss functions. The DLS was developed using 9,086 slid…
In this study, we present a hybrid CNN-RNN approach to investigate long-term survival of subjects in a lung cancer screening study. Subjects who died of cardiovascular and respiratory causes were identified whereby the CNN model was used to capture imaging features in the CT scans and the RNN model was used to investigate time series and thus global information. The models were trained on subjects who underwent cardiovascular and respiratory deaths and a control cohort matched to participant age, gender, and smoking history. The combined model can achieve an AUC of 0.76 which outperforms human…
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）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；IBM Research（IBM研究院）；VinUniversity（文大）
Effective understanding of a disease such as cancer requires fusing multiple sources of information captured across physical scales by multimodal data. In this work, we propose a novel feature embedding module that derives from canonical correlation analyses to account for intra-modality and inter-modality correlations. Experiments on simulated and real data demonstrate how our proposed module can learn well-correlated multi-dimensional embeddings. These embeddings perform competitively on one-year survival classification of TCGA-BRCA breast cancer patients, yielding average F1 scores up to 58…
Indian Statistical Institute（印度统计学院）；University of South Florida（南佛罗里达大学）
Prediction of Overall Survival (OS) of brain cancer patients from multi-modal MRI is a challenging field of research. Most of the existing literature on survival prediction is based on Radiomic features, which does not consider either non-biological factors or the functional neurological status of the patient(s). Besides, the selection of an appropriate cut-off for survival and the presence of censored data create further problems. Application of deep learning models for OS prediction is also limited due to the lack of large annotated publicly available datasets. In this scenario we analyse th…
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）
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）
Emory University（埃默里大学）；Georgia Institute of Technology（佐治亚理工学院）
Genomics are rapidly transforming medical practice and basic biomedical research, providing insights into disease mechanisms and improving therapeutic strategies, particularly in cancer. The ability to predict the future course of a patient's disease from high-dimensional genomic profiling will be essential in realizing the promise of genomic medicine, but presents significant challenges for state-of-the-art survival analysis methods. In this abstract we present an investigation in learning genomic representations with neural networks to predict patient survival in cancer. We demonstrate the a…
Guiding Treatment Strategies: The Role of Adjuvant Anti-Her2 Neu Therapy and Skin/Nipple Involvement in Local Recurrence-Free Survival in Breast Cancer Patients
University of Nottingham Malaysia Campus（诺丁汉大学马来西亚校区）；University of Nottingham（诺丁汉大学）；City Hospital, Nottingham（诺丁汉市立医院）
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build on existing research on clustering and machine learning facets of this data to demonstrate a role for an ensemble approach to highlighting patients with clearer prognosis parameters. Results for survival prediction using 3 different approaches are shown for a subset of the data which is most difficu…
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）
The prognosis for patients with epithelial ovarian cancer remains dismal despite improvements in survival for other cancers. Treatment involves multiple lines of chemotherapy and becomes increasingly heterogeneous after first-line therapy. Reinforcement learning with real-world outcomes data has the potential to identify novel treatment strategies to improve overall survival. We design a reinforcement learning environment to model epithelial ovarian cancer treatment trajectories and use model free reinforcement learning to investigate therapeutic regimens for simulated patients.
There exists unexplained diverse variation within the predefined colon cancer stages using only features either from genomics or histopathological whole slide images as prognostic factors. Unraveling this variation will bring about improved in staging and treatment outcome, hence motivated by the advancement of Deep Neural Network libraries and different structures and factors within some genomic dataset, we aggregate atypical patterns in histopathological images with diverse carcinogenic expression from mRNA, miRNA and DNA Methylation as an integrative input source into an ensemble deep neura…
Tianjin University（天津大学）；CSIRO Data61（澳大利亚联邦科学与工业研究组织Data61）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Shandong University（山东大学）
Ovarian cancer is one of the most serious cancers that threaten women around the world. Epithelial ovarian cancer (EOC), as the most commonly seen subtype of ovarian cancer, has rather high mortality rate and poor prognosis among various gynecological cancers. Survival analysis outcome is able to provide treatment advices to doctors. In recent years, with the development of medical imaging technology, survival prediction approaches based on pathological images have been proposed. In this study, we designed a deep framework named EOCSA which analyzes the prognosis of EOC patients based on patho…
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）
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
For Head and Neck Cancers (HNC) patient management, automatic gross tumor volume (GTV) segmentation and accurate pre-treatment cancer recurrence prediction are of great importance to assist physicians in designing personalized management plans, which have the potential to improve the treatment outcome and quality of life for HNC patients. In this paper, we developed an automated primary tumor (GTVp) and lymph nodes (GTVn) segmentation method based on combined pre-treatment positron emission tomography/computed tomography (PET/CT) scans of HNC patients. We extracted radiomics features from the …
University of Iowa（爱荷华大学）；Stanford University（斯坦福大学）
Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography-computed tomography (PET/CT) images have predictive power on NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neu…
School of Electrical and Computer Engineering, Purdue University（普渡大学电气与计算机工程学院）；Department of Medicine, Indiana University School of Medicine（印第安纳大学医学院医学系）；School of Electrical and Computer Engineering, Indiana University - Purdue University Indianapolis（印第安纳大学-普渡大学印第安纳波利斯分校电气与计算机工程学院）
One of the central goals in precision health is the understanding and interpretation of high-dimensional biological data to identify genes and markers associated with disease initiation, development, and outcomes. Though significant effort has been committed to harness gene expression data for multiple analyses while accounting for time-to-event modeling by including survival times, many traditional analyses have focused separately on non-negative matrix factorization (NMF) of the gene expression data matrix and survival regression with Cox proportional hazards model. In this work, Cox proport…
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）
School of Information Technology（信息技术学院）；University of Cincinnati（辛辛那提大学）
Lung cancer is the leading cause of death among different types of cancers. Every year, the lives lost due to lung cancer exceed those lost to pancreatic, breast, and prostate cancer combined. The survival rate for lung cancer patients is very low compared to other cancer patients due to late diagnostics. Thus, early lung cancer diagnostics is crucial for patients to receive early treatments, increasing the survival rate or even becoming cancer-free. This paper proposed a deep-learning model for early lung cancer prediction and diagnosis from Computed Tomography (CT) scans. The proposed mode a…
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）；Med-X Research Institute, Shanghai Jiao Tong University（上海交通大学Med-X研究院）
