Indrani Bhattacharya, Arun Seetharaman, Wei Shao, Rewa Sood, Christian A. Kunder, Richard E. Fan, Simon John Christoph Soerensen, Jeffrey B. Wang, Pejman Ghanou… 展开作者
Indrani Bhattacharya, Arun Seetharaman, Wei Shao, Rewa Sood, Christian A. Kunder, Richard E. Fan, Simon John Christoph Soerensen, Jeffrey B. Wang, Pejman Ghanouni, Nikola C. Teslovich, James D. Brooks, Geoffrey A. Sonn, Mirabela Rusu
Magnetic Resonance Imaging (MRI) is widely used for screening and staging prostate cancer. However, many prostate cancers have subtle features which are not easily identifiable on MRI, resulting in missed diagnoses and alarming variability in radiologist interpretation. Machine learning models have been developed in an effort to improve cancer identification, but current models localize cancer using MRI-derived features, while failing to consider the disease pathology characteristics observed on resected tissue. In this paper, we propose CorrSigNet, an automated two-step model that localizes prostate cancer on MRI by capturing the pathology features of cancer. First, the model learns MRI signatures of cancer that are correlated with corresponding histopathology features using Common Representation Learning. Second, the model uses the learned correlated MRI features to train a Convolutional Neural Network to localize prostate cancer. The histopathology images are used only in the first step to learn the correlated features. Once learned, these correlated features can be extracted from MRI of new patients (without histopathology or surgery) to localize cancer. We trained and validated our framework on a unique dataset of 75 patients with 806 slices who underwent MRI followed by prostatectomy surgery. We tested our method on an independent test set of 20 prostatectomy patients (139 slices, 24 cancerous lesions, 1.12M pixels) and achieved a per-pixel sensitivity of 0.81, specificity of 0.71, AUC of 0.86 and a per-lesion AUC of $0.96 \pm 0.07$, outperforming the current state-of-the-art accuracy in predicting prostate cancer using MRI.
Facebook（脸书）；Stanford University School of Medicine（斯坦福大学医学院）；Stanford University（斯坦福大学）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人医疗保健系统）
Prostate cancer is the second deadliest cancer for American men. While Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted biopsies for prostate cancer diagnosis, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements. Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model architecture, but also in the ground truth labeling strategies used for model training. In this study, we…
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）
Leveraging Clinical Text and Class Conditioning for 3D Prostate MRI Generation
University of Toronto（多伦多大学）；Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital（西奈山医院卢嫩菲尔德-塔南鲍姆研究所）；KITE Research Institute, Toronto Rehabilitation Institute, University Health Network（大学健康网络多伦多康复研究所KITE研究所）；Vector Institute（Vector研究所）；Princess Margaret Hospital（玛格丽特公主医院）；Sinai Health System（西奈健康系统）
AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons
BAMF Health（BAMF健康公司）；National Institute of Health（美国国立卫生研究院）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究实验室）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
KOAL: Knowledge-Driven Prostate Cancer Grading with Ordinal-Aware Learning
School of Computer Science, Sichuan University（四川大学计算机学院）；China Mobile (Suzhou) Software Technology Company Limited（中国移动（苏州）软件技术有限公司）；Shanghai Changhai Hospital, Naval Medical University（上海长海医院，海军医学大学）；Renji Hospital, Shanghai Jiao Tong University School of Medicine（仁济医院，上海交通大学医学院）
PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings
Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA（计算病理学部，病理与实验室医学部，印第安纳大学医学院，印第安纳波利斯，印第安纳州，美国）；Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA（印第安纳大学Melvin和Bren Simon综合癌症中心，印第安纳波利斯，印第安纳州，美国）；Luddy School of Informatics, Computing, and Engineering, Indiana University, IN, USA（卢迪信息学、计算与工程学院，印第安纳大学，印第安纳州，美国）；Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA（放射学与成像科学部，印第安纳大学医学院，印第安纳波利斯，印第安纳州，美国）；Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA（生物统计学与健康数据科学部，印第安纳大学医学院，印第安纳波利斯，印第安纳州，美国）
PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT and Radiology Report Dataset for Medical Imaging Research
PET Center, Huashan Hospital, Fudan University（复旦大学附属华山医院PET中心）；Shanghai Academy of Artificial Intelligence for Science（上海科学智能研究院）；Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；Shanghai Universal Medical Imaging Diagnostic Center（上海环球医学影像诊断中心）；Human Phenome Institute, Fudan University（复旦大学人类表型组研究院）；School of Data Science, Fudan University（复旦大学数据科学学院）
Impact of Clinical Image Quality on Efficient Foundation Model Finetuning
UCL Hawkes Insitute, University College London Dept. of Medical Physics \& Biomedical Engineering, University College London Division of Surgery \& Interventional Science, University College London Centre of Medical Imaging, University College London Department of Radiology, UCLH NHS Foundation Trust Department of Urology, UCLH NHS Foundation Trust Department of Computer Science, University College London
SUNY Buffalo（纽约州立大学布法罗分校）；Medical College of Wisconsin（威斯康星医学院）；Washington University School of Medicine（华盛顿大学医学院）；University of California, Davis Medical Center（加州大学戴维斯医学中心）
Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of pathology, we have created an intuitive interface which utilizes the commonly used whole slide image (WSI) viewer, Aperio ImageScope (Leica Biosystems Imaging, Inc.), for the annotation and display of neural network predictions on WSIs. Leveraging this, we propose the use of a human-in-the-loop strategy to reduce the burden of WSI annotation. We …
Magnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis. It can spare men with a normal exam from undergoing invasive biopsy while making biopsies more accurate in men with lesions suspicious for cancer. Yet, the subtle differences between cancer and confounding conditions, render the interpretation of MRI challenging. The tissue collected from patients that undergo pre-surgical MRI and radical prostatectomy provides a unique opportunity to correlate histopathology images of the entire prostate with MRI in order to accurately map the extent of prostate cancer …
Recent advances in medical imaging techniques have led to significant improvements in the management of prostate cancer (PCa). In particular, multi-parametric MRI (mp-MRI) continues to gain clinical acceptance as the preferred imaging technique for non-invasive detection and grading of PCa. However, the machine learning-based diagnosis systems for PCa are often constrained by the limited access to accurate lesion ground truth annotations for training. The performance of the machine learning system is highly dependable on both quality and quantity of lesion annotations associated with histopath…
