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 with lower dimensions from clinical features using labeled (time-to-event) and unlabeled (censored) instances and then actively trains the survival model by labeling the censored data using an oracle. As a clinical assistive tool, we introduce a simple effective treatment recommendation approach based on our survival model. In the experimental study, we apply our approach on SEER-Medicare data related to prostate cancer among African-Americans and white patients. The results indicate that our approach outperforms significantly than baseline models.
Low risk prostate cancer patients enrolled in active surveillance (AS) programs commonly undergo biopsies on a frequent basis for examination of cancer progression. AS programs employ a fixed schedule of biopsies for all patients. Such fixed and frequent schedules, may schedule unnecessary biopsies for the patients. Since biopsies have an associated risk of complications, patients do not always comply with the schedule, which increases the risk of delayed detection of cancer progression. Motivated by the world's largest AS program, Prostate Cancer Research International Active Surveillance (PR…
The Cox proportional hazard model is one of the most popular tools in analyzing time-to-event data in public health studies. When outcomes observed in clinical data from different regions yield a varying pattern correlated with location, it is often of great interest to investigate spatially varying effects of covariates. In this paper, we propose a geographically weighted Cox regression model for sparse spatial survival data. In addition, a stochastic neighborhood weighting scheme is introduced at the county level. Theoretical properties of the proposed geographically weighted estimators are …
Background: Low-risk prostate cancer patients enrolled in active surveillance programs commonly undergo biopsies for examination of cancer progression. Biopsies are conducted as per a fixed and frequent schedule (e.g., annual biopsies). Since biopsies are burdensome, patients do not always comply with the schedule, which increases the risk of delayed detection of cancer progression. Objective: Our aim is to better balance the number of biopsies (burden) and the delay in detection of cancer progression (less is beneficial), by personalizing the decision of conducting biopsies. Data Sources: We …
Purpose. Patients with advanced cancer may undergo multiple lines of treatment, switching therapies as their disease progresses. Motivated by a study of metastatic prostate cancer, we develop a microsimulation framework to study therapy sequence. Methods. We propose a discrete-time state transition model to study two lines of anti-cancer therapy. Based on digitized published progression-free survival (PFS) and overall survival (OS) curves, we infer event types (progression or death), and estimate transition probabilities using cumulative incidence functions with competing risks. Our model inco…
In biomedical studies it is of substantial interest to develop risk prediction scores using high-dimensional data such as gene expression data for clinical endpoints that are subject to censoring. In the presence of well-established clinical risk factors, investigators often prefer a procedure that also adjusts for these clinical variables. While accelerated failure time (AFT) models are a useful tool for the analysis of censored outcome data, it assumes that covariate effects on the logarithm of time-to-event are linear, which is often unrealistic in practice. We propose to build risk predict…
Top Data Science Ltd.（顶级数据科学有限公司）；University of Helsinki（赫尔辛基大学）；Skåne University Hospital（斯科讷大学医院）；Helsinki University Hospital（赫尔辛基大学医院）
Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the help of transfer learning, classification and segmentation performance of neural network models have been further increased. However, due to the absence of large, extensively annotated, publicly available prostate histopathology datasets, several previous studies employ datasets from well-studied computer vision tasks such as ImageNet dataset. In this work, we propose a transfer learning scheme from breast histopath…
Research Computing Center at the University of Chicago（芝加哥大学研究计算中心）
Prostate cancer is one of the most common causes of cancer deaths in men. There is a growing demand for noninvasively and accurately diagnostic methods that facilitate the current standard prostate cancer risk assessment in clinical practice. Still, developing computer-aided classification tools in prostate cancer diagnostics from multiparametric magnetic resonance images continues to be a challenge. In this work, we propose a novel deep learning approach for automatic classification of prostate lesions in the corresponding magnetic resonance images by constructing a two-stage multimodal multi…
While current research has shown the importance of Multi-parametric MRI (mpMRI) in diagnosing prostate cancer (PCa), further investigation is needed for how to incorporate the specific structures of the mpMRI data, such as the regional heterogeneity and between-voxel correlation within a subject. This paper proposes a machine learning-based method for improved voxel-wise PCa classification by taking into account the unique structures of the data. We propose a multi-resolution modeling approach to account for regional heterogeneity, where base learners trained locally at multiple resolutions ar…
Prostate cancer was the third most common cancer in 2020 internationally, coming after breast cancer and lung cancer. Furthermore, in recent years prostate cancer has shown an increasing trend. According to clinical experience, if this problem is detected and treated early, there can be a high chance of survival for the patient. One task that helps diagnose prostate cancer is prostate segmentation from magnetic resonance imaging. Manual segmentation performed by clinical experts has its drawbacks such as: the high time and concentration required from observers; and inter- and intra-observer va…
FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；Siemens Medical Solutions USA（美国西门子医疗解决方案公司）；Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）
University of California, San Francisco（加州大学旧金山分校）；Duke University（杜克大学）
Early prostate cancer detection and staging from MRI are extremely challenging tasks for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their generalization capability both within- and across clinics. To enable this for prototype-stage algorithms, where the majority of existing research remains, in this paper we introduce a flexible federated learning framework for cross-site training, validation, and evaluation of deep prostate cancer detection algorithms. Our approach utilizes an abstracted rep…
Univ Lyon（里昂大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；INSA Lyon（里昂国立应用科学学院）；UCBL（里昂第一大学）；CREATIS（里昂影像与信息处理研究中心）；Hospices Civils de Lyon（里昂民用医院集团）；Hôpital Edouard Herriot（爱德华·埃里奥医院）；University of Sherbrooke（舍布鲁克大学）
Multiparametric magnetic resonance imaging (mp-MRI) has shown excellent results in the detection of prostate cancer (PCa). However, characterizing prostate lesions aggressiveness in mp-MRI sequences is impossible in clinical practice, and biopsy remains the reference to determine the Gleason score (GS). In this work, we propose a novel end-to-end multi-class network that jointly segments the prostate gland and cancer lesions with GS group grading. After encoding the information on a latent space, the network is separated in two branches: 1) the first branch performs prostate segmentation 2) th…
Lunenfeld-Tanenbaum Research Institute（卢嫩菲尔德-塔南鲍姆研究所）；Sinai Health System（西奈卫生系统）；Brigham and Women’s Hospital（布莱根妇女医院）；University of Toronto（多伦多大学）
Convolutional Neural Networks (CNNs) have been used for automated detection of prostate cancer where Area Under Receiver Operating Characteristic (ROC) curve (AUC) is usually used as the performance metric. Given that AUC is not differentiable, common practice is to train the CNN using a loss functions based on other performance metrics such as cross entropy and monitoring AUC to select the best model. In this work, we propose to fine-tune a trained CNN for prostate cancer detection using a Genetic Algorithm to achieve a higher AUC. Our dataset contained 6-channel Diffusion-Weighted MRI slices…
Location-based Radiology Report-Guided Semi-supervised Learning for Prostate Cancer Detection
National Cancer Institute（美国国家癌症研究所）；National Institutes of Health（美国国立卫生研究院）；Clinical Center, National Institutes of Health（美国国立卫生研究院临床中心）；Center for Cancer Research, National Cancer Institute（美国国家癌症研究所癌症研究中心）
Mask Enhanced Deeply Supervised Prostate Cancer Detection on B-mode Micro-Ultrasound
Stanford University（斯坦福大学）；The Catholic University of Korea（韩国天主教大学）；University of Alberta（阿尔伯塔大学）；University of California Los Angeles（加州大学洛杉矶分校）；Humanitas University（乌尼塔斯大学）；IRCCS Humanitas Research Hospital（IRCCS乌尼塔斯研究医院）；University of Florida（佛罗里达大学）；University of California San Diego（加州大学圣地亚哥分校）
We propose an unsupervised method using self-clustering convolutional adversarial autoencoders to classify prostate tissue as tumor or non-tumor without any labeled training data. The clustering method is integrated into the training of the autoencoder and requires only little post-processing. Our network trains on hematoxylin and eosin (H&E) input patches and we tested two different reconstruction targets, H&E and immunohistochemistry (IHC). We show that antibody-driven feature learning using IHC helps the network to learn relevant features for the clustering task. Our network achieves a F1 s…
University of Saskatchewan（萨斯喀彻温大学）；International Road Dynamics（国际道路动力公司）；National Institute of Technology, Trichy（国家理工学院特里奇分校）
Prostate cancer is a very common disease among adult men. One in seven Canadian men is diagnosed with this cancer in their lifetime. Super-Resolution (SR) can facilitate early diagnosis and potentially save many lives. In this paper, a robust and accurate model is proposed for prostate MRI SR. The model is trained on the Prostate-Diagnosis and PROSTATEx datasets. The proposed model outperformed the state-of-the-art prostate SR model in all similarity metrics with notable margins. A new task-specific similarity assessment is introduced as well. A classifier is trained for severe cancer detectio…
University of California, San Francisco（加州大学旧金山分校）；Duke University（杜克大学）；University of Dusseldorf（杜塞尔多夫大学）；University of Washington（华盛顿大学）
Non-invasive prostate cancer detection from MRI has the potential to revolutionize patient care by providing early detection of clinically-significant disease (ISUP grade group >= 2), but has thus far shown limited positive predictive value. To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classi…
Maastricht University Medical Centre+（马斯特里赫特大学医学中心+）；Netherlands Cancer Institute（荷兰癌症研究所）；Maastro Clinic（马斯特罗诊所）；Maastricht University Medical Center+（马斯特里赫特大学医学中心+）；Netherlands Comprehensive Cancer Organization（荷兰综合癌症组织）；Radboud university medical centre（拉德堡德大学医学中心）；Radboud university Medical Center（拉德堡德大学医学中心）；Zuyd University of Applied Sciences（南方应用科学大学）；Fontys University of Applied Sciences（方提斯应用科学大学）
While the 10-year survival rate for localized prostate cancer patients is very good (>98%), side effects of treatment may limit quality of life significantly. Erectile dysfunction (ED) is a common burden associated with increasing age as well as prostate cancer treatment. Although many studies have investigated the factors affecting erectile dysfunction (ED) after prostate cancer treatment, only limited studies have investigated whether ED can be predicted before the start of treatment. The advent of machine learning (ML) based prediction tools in oncology offers a promising approach to improv…
PR-NET: Leveraging Pathway Refined Network Structures for Prostate Cancer Patient Condition Prediction
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences（中国科学院重庆绿色智能技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Sansure Biotech Incorporation（圣湘生物科技股份有限公司）
Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；University of Utah（犹他大学）
Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides. The cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands. For diagnostic work-up, pathologists first locate glands in the whole biopsy core, and -- if they detect cancer -- they assign a Gleason grade. This time-consuming process is subject to errors and significant inter-observer variability, despite strict diagnostic criteria. This paper proposes an automated workflow that follows patholog…
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
University of Augsburg（奥格斯堡大学）；University Hospital Augsburg（奥格斯堡大学医院）；Neu-Ulm University of Applied Sciences（新乌尔姆应用科学大学）；Bavarian Cancer Research Center（巴伐利亚癌症研究中心）；BioM Biotech Cluster Development GmbH（BioM生物技术集群发展有限公司）
Prostate Cancer Classification Using Multimodal Feature Fusion and Explainable AI
Chittagong University of Engineering & Technology（吉大港工程技术大学）；Southern University Bangladesh（南孟加拉大学）；Rangamati Science and Technology University（兰加马蒂科学技术大学）；Kitami Institute of Technology（北见工业大学）；Chittagong Medical College（吉大港医学院）；University of Chittagong（吉大港大学）
Classification of Prostate Cancer in 3D Magnetic Resonance Imaging Data based on Convolutional Neural Networks
Institut für Medizinische Informatik, Universität zu Lübeck（吕贝克大学医学信息学研究所）；FUSE-AI GmbH, Hamburg（汉堡FUSE-AI有限公司）；Cantonal Hospital Aarau, Aarau (CH)（阿劳州立医院）
In this work, we propose a deep U-Net based model to tackle the challenging task of prostate cancer segmentation by aggressiveness in MRI based on weak scribble annotations. This model extends the size constraint loss proposed by Kervadec et al. 1 in the context of multiclass detection and segmentation task. This model is of high clinical interest as it allows training on prostate biopsy samples and avoids time-consuming full annotation process. Performance is assessed on a private dataset (219 patients) where the full ground truth is available as well as on the ProstateX-2 challenge database,…
DISC: Latent Diffusion Models with Self-Distillation from Separated Conditions for Prostate Cancer Grading
Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；The Catholic University of Korea College of Medicine（韩国天主教大学医学院）；Case Western Reserve University（凯斯西储大学）；Huntsman Cancer Institute, University of Utah Health（犹他大学健康亨茨曼癌症研究所）；University of Utah（犹他大学）
University of Electronic Science and Technology of China（电子科技大学）；University of Toronto（多伦多大学）；The Hospital for Sick Children (SickKids)（SickKids 儿童医院（病童医院））；Sinai Health System（西奈卫生系统）；University Health Network（大学健康网络）；Lunenfeld-Tanenbaum Research Institute（卢嫩菲尔德-塔南鲍姆研究所）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution. While different augmentation strategies and their combinations have been investigated for various computer vision tasks in the context of deep learning, a specific work in the domain of medical imaging is rare and to the best of our knowledge, there has been no dedicated work on exploring the effects of various augmentation methods on the performance of deep learning models in prostate cancer detect…
Learning Prostate Anatomy at Test Time for Cancer Detection in Micro-Ultrasound
University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（向量研究所）；Ordensklinikum Linz（林茨 Ordenklinikum 医院）；University of Alberta（阿尔伯塔大学）；Exact Imaging（Exact Imaging 公司）
DiagSet: a dataset for prostate cancer histopathological image classification
Diagnostyka Consilio Sp. z o.o.（诊断康西利奥有限公司）；AGH University of Science and Technology（AGH科技大学）；Andrzej Frycz Modrzewski Krakow University（安德烈·弗赖奇·莫德热夫斯基克拉科夫大学）
Universidad de Sevilla（塞维利亚大学）；Research Institute of Computer Engineering (I3US)（计算机工程研究院（I3US））
Prostate cancer (PCa) is one of the most commonly diagnosed cancer and one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. Artificial Intelligence algorithms have had a huge impact in medical image analysis, including digital histopathology, where Convolutional Neural Networks (CNNs) are used to provide a fast and accurate diagnosis, supporting experts in this task. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Due to the size of these images, neural ne…
Google Health（谷歌健康）；Medical University of Graz（格拉茨医科大学）；Advanced Clinical；University of Tennessee Health Science Center（田纳西大学健康科学中心）；Tempus Labs Inc（Tempus Labs公司）
Gleason grading of prostate cancer is an important prognostic factor but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether A.I. grading translates to better prognostication. In this study, we developed a system to predict prostate-cancer specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2,807 prostatectomy …
Technological University Dublin（都柏林理工大学）；University College Dublin（都柏林大学学院）；St. James Hospital（圣詹姆斯医院）；Trinity College Dublin（都柏林三一学院）；Beaumont Hospital（博蒙特医院）
The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained…
University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Universität Hamburg（汉堡大学）
Clinical decision support for histopathology image data mainly focuses on strongly supervised annotations, which offers intuitive interpretability, but is bound by expert performance. Here, we propose an explainable cancer relapse prediction network (eCaReNet) and show that end-to-end learning without strong annotations offers state-of-the-art performance while interpretability can be included through an attention mechanism. On the use case of prostate cancer survival prediction, using 14,479 images and only relapse times as annotations, we reach a cumulative dynamic AUC of 0.78 on a validatio…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
In post-operative radiotherapy for prostate cancer, the cancerous prostate gland has been surgically removed, so the clinical target volume (CTV) to be irradiated encompasses the microscopic spread of tumor cells, which cannot be visualized in typical clinical images such as computed tomography or magnetic resonance imaging. In current clinical practice, physicians segment CTVs manually based on their relationship with nearby organs and other clinical information, per clinical guidelines. Automating post-operative prostate CTV segmentation with traditional image segmentation methods has been a…
Purpose: We aimed to develop deep machine learning (DL) models to improve the detection and segmentation of intraprostatic lesions (IL) on bp-MRI by using whole amount prostatectomy specimen-based delineations. We also aimed to investigate whether transfer learning and self-training would improve results with small amount labelled data. Methods: 158 patients had suspicious lesions delineated on MRI based on bp-MRI, 64 patients had ILs delineated on MRI based on whole mount prostatectomy specimen sections, 40 patients were unlabelled. A non-local Mask R-CNN was proposed to improve the segmentat…
Computational Mapping of Reactive Stroma in Prostate Cancer Yields Interpretable, Prognostic Biomarkers
Biomedical AI Research Lab, University of California, Los Angeles（加州大学洛杉矶分校生物医学人工智能研究实验室，加州大学洛杉矶分校）；Department of Radiology, University of California, Los Angeles（加州大学洛杉矶分校放射科）；Bioinformatics Interdepartmental Program, University of California, Los Angeles（加州大学洛杉矶分校生物信息学跨学科项目）；Department of Bioengineering, University of California, Los Angeles（加州大学洛杉矶分校生物工程系）；Department of Pathology & Laboratory Medicine, University of California, Los Angeles（加州大学洛杉矶分校病理学与实验室医学系）
University of Shanghai for Science and Technology（上海理工大学）；Tongji University（同济大学）；National University of Singapore（新加坡国立大学）；Shanghai Fire Corps Hospital（上海消防总队医院）；Shanghai Tenth People’s Hospital（上海市第十人民医院）
Automatic segmentation of the prostate cancer from the multi-modal magnetic resonance images is of critical importance for the initial staging and prognosis of patients. However, how to use the multi-modal image features more efficiently is still a challenging problem in the field of medical image segmentation. In this paper, we develop a cross-modal self-attention distillation network by fully exploiting the encoded information of the intermediate layers from different modalities, and the extracted attention maps of different modalities enable the model to transfer the significant spatial inf…
Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures
Courant Institute of Mathematical Sciences, New York University（纽约大学库朗数学科学研究所）；Center for Data Science, New York University（纽约大学数据科学中心）；Center for Computational Astrophysics, Flatiron Institute（弗拉蒂隆研究所计算天体物理中心）；Center for Computational Neuroscience, Flatiron Institute（弗拉蒂隆研究所计算神经科学中心）；Cedars-Sinai Medical Center（西达赛奈医疗中心）；Department of Applied Mathematics and Theoretical Physics, University of Cambridge（剑桥大学应用数学与理论物理系）；Institute of Astronomy, University of Cambridge（剑桥大学天文研究所）；Department of Astrophysical Sciences, Princeton University（普林斯顿大学天体物理科学系）；Center for Computational Biology, Flatiron Institute（弗拉蒂隆研究所计算生物学中心）
Anomaly-Driven Approach for Enhanced Prostate Cancer Segmentation
University of Amsterdam（阿姆斯特丹大学）；Department of Radiology, The Netherlands Cancer Institute（荷兰癌症研究所放射科）；GROW Research Institute for Oncology and Reproduction, Maastricht University（马斯特里赫特大学肿瘤学与生殖医学研究学院）
Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification
KLS Gogte Institute of Technology（KLS戈格特理工学院）；University of Stavanger（斯塔万格大学）；Dr. M.S. Sheshgiri College of Engineering and Technology（M.S.谢什吉里博士工程技术学院）；JN Medical College（JN医学院）
Google AI Healthcare（谷歌人工智能医疗保健部门）；Naval Medical Center San Diego（圣地亚哥海军医疗中心）；Henry M. Jackson Foundation（亨利·M·杰克逊基金会）；University of Tennessee Health Science Center（田纳西大学健康科学中心）；University Health Network（大学健康网络）；University of Toronto（多伦多大学）；El Camino Hospital（埃尔卡米诺医院）
For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1,226 slides, and evaluated on an independent validation dataset of 331 slides, where the reference standard was established…
AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review
Soochow University（苏州大学）；Peking University First Hospital（北京大学第一医院）；Duke Kunshan University（昆山杜克大学）；Swinburne University of Technology（斯威本科技大学）；The Hong Kong Polytechnic University（香港理工大学）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able…
