Detection of Perineural Invasion in Prostate Needle Biopsies with Deep Neural Networks
Karolinska Institutet（卡罗林斯卡学院）；Tampere University（坦佩雷大学）；University of Turku（图尔库大学）；S:t Göran Hospital（圣戈兰医院）；Aquesta Uropathology（阿奎斯塔泌尿病理中心）；University of Queensland（昆士兰大学）；University of Otago（奥塔哥大学）；Wellington School of Medicine and Health Sciences（惠灵顿医学与健康科学学院）；Aichi Medical University（爱知医科大学）
Peter Ström, Kimmo Kartasalo, Pekka Ruusuvuori, Henrik Grönberg, Hemamali Samaratunga, Brett Delahunt, Toyonori Tsuzuki, Lars Egevad, Martin Eklund
Background: The detection of perineural invasion (PNI) by carcinoma in prostate biopsies has been shown to be associated with poor prognosis. The assessment and quantification of PNI is; however, labor intensive. In the study we aimed to develop an algorithm based on deep neural networks to aid pathologists in this task.
Methods: We collected, digitized and pixel-wise annotated the PNI findings in each of the approximately 80,000 biopsy cores from the 7,406 men who underwent biopsy in the prospective and diagnostic STHLM3 trial between 2012 and 2014. In total, 485 biopsy cores showed PNI. We also digitized more than 10% (n=8,318) of the PNI negative biopsy cores. Digitized biopsies from a random selection of 80% of the men were used to build deep neural networks, and the remaining 20% were used to evaluate the performance of the algorithm.
Results: For the detection of PNI in prostate biopsy cores the network had an estimated area under the receiver operating characteristics curve of 0.98 (95% CI 0.97-0.99) based on 106 PNI positive cores and 1,652 PNI negative cores in the independent test set. For the pre-specified operating point this translates to sensitivity of 0.87 and specificity of 0.97. The corresponding positive and negative predictive values were 0.67 and 0.99, respectively. For localizing the regions of PNI within a slide we estimated an average intersection over union of 0.50 (CI: 0.46-0.55).
Conclusion: We have developed an algorithm based on deep neural networks for detecting PNI in prostate biopsies with apparently acceptable diagnostic properties. These algorithms have the potential to aid pathologists in the day-to-day work by drastically reducing the number of biopsy cores that need to be assessed for PNI and by highlighting regions of diagnostic interest.
Shiraz University of Technology（设拉子科技大学）；National Brain Mapping Laboratory（国家脑图谱实验室）
Perineural invasion (PNI) by malignant tumor cells has been reported as an independent indicator of poor prognosis in various cancers. Assessment of PNI in small nerves on glass slides is a labor-intensive task. In this study, we propose an algorithm to detect the perineural invasions in colon, prostate, and pancreas cancers based on a convolutional neural network (CNN).
Radboud University Medical Center（拉德堡德大学医学中心）；Radboud Institute for Health Sciences（拉德堡德健康科学研究所）；The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital (NKI-AVL)（荷兰癌症研究所-安东尼·范·列文虎克医院（NKI-AVL））；Laboratory of Pathology East Netherlands (LabPON)（荷兰东部病理实验室（LabPON））
The Gleason score is the most important prognostic marker for prostate cancer patients but suffers from significant inter-observer variability. We developed a fully automated deep learning system to grade prostate biopsies. The system was developed using 5834 biopsies from 1243 patients. A semi-automatic labeling technique was used to circumvent the need for full manual annotation by pathologists. The developed system achieved a high agreement with the reference standard. In a separate observer experiment, the deep learning system outperformed 10 out of 15 pathologists. The system has the pote…
Background: Transrectal ultrasound guided systematic biopsies of the prostate is a routine procedure to establish a prostate cancer diagnosis. However, the 10-12 prostate core biopsies only sample a relatively small volume of the prostate, and tumour lesions in regions between biopsy cores can be missed, leading to a well-known low sensitivity to detect clinically relevant cancer. As a proof-of-principle, we developed and validated a deep convolutional neural network model to distinguish between morphological patterns in benign prostate biopsy whole slide images from men with and without estab…
Northumbria University at Newcastle（纽卡斯尔诺森比亚大学）；College of Computer and Information Sciences, King Saud University（沙特国王大学计算机与信息科学学院）
Colorectal and prostate cancers are the most common types of cancer in men worldwide. To diagnose colorectal and prostate cancer, a pathologist performs a histological analysis on needle biopsy samples. This manual process is time-consuming and error-prone, resulting in high intra and interobserver variability, which affects diagnosis reliability. This study aims to develop an automatic computerized system for diagnosing colorectal and prostate tumors by using images of biopsy samples to reduce time and diagnosis error rates associated with human analysis. We propose a CNN model for classifyin…
Karolinska Institutet（卡罗林斯卡医学院）；Tampere University（坦佩雷大学）；Uppsala University（乌普萨拉大学）；University of Otago（奥塔哥大学）；Queen Mary University of London（伦敦玛丽女王大学）；Bostwick Laboratories（博斯特威克实验室）；University Health Network（大学健康网络）；Indiana University School of Medicine（印第安纳大学医学院）；Yale University School of Medicine（耶鲁大学医学院）；Medical College of Wisconsin（威斯康星医学院）；Royal Prince Alfred Hospital（阿尔弗雷德王子医院）；University of Sydney（悉尼大学）；University Hospital Bonn（波恩大学医院）；University of São Paulo Medical School（圣保罗大学医学院）；Cleveland Clinic（克利夫兰医学中心）；Southmead Hospital（索斯米德医院）；Taipei Veterans General Hospital（台北荣民总医院）；Aquesta Uropathology（Aquesta泌尿病理中心）；University of Queensland（昆士兰大学）；University of Toronto（多伦多大学）；Jikei University School of Medicine（慈惠会医科大学）；Aichi Medical University（爱知医科大学）；University Hospital of Wales（威尔士大学医院）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）；SciLifeLab（生命科学实验室）；S:t Göran Hospital（圣约兰医院）
Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-observer variability in grading can result in over- and undertreatment of prostate cancer. Artificial intelligence (AI) methods may alleviate these problems by assisting pathologists to reduce workload and harmonize grading. Methods: We digitized 6,682 needle biopsies from 976 participants in the population based STHLM3 diagnostic study to train deep neural networks for assessing prostate biopsies. The networks were …
Radboud University Medical Center（拉德堡德大学医学中心）；Radboud Institute for Health Sciences（拉德堡德健康科学研究所）；University of Yaounde 1（雅温得第一大学）；Salomão Zoppi Diagnostics/DASA（萨洛芒·佐皮诊断中心/DASA）；Istanbul Medipol University（伊斯坦布尔 Medipol 大学）；Public Health Agency of Catalonia（加泰罗尼亚公共卫生署）；Centre de Pathologie（病理中心）；Hopital Diaconat Mulhouse（米卢斯迪亚科纳医院）；Aix en Provence Hospital（艾克斯-普罗旺斯医院）；Histo Patologia Cirúrgica e Citologia（组织外科病理与细胞学中心）；Tata Medical Center（塔塔医疗中心）；labor team w ag（W 实验室有限公司）；Instituto Mário Penna（马里奥·彭纳研究所）；Medics Pathologie（梅迪克病理中心）；Antoni van Leeuwenhoek Hospital（安东尼·范·列文虎克医院）；Netherlands Cancer Institute（荷兰癌症研究所）；Laboratory of Pathology East Netherlands（荷兰东部病理实验室）；Center for Medical Image Science and Visualization（医学影像科学与可视化中心）；Linköping University（林雪平大学）
While the Gleason score is the most important prognostic marker for prostate cancer patients, it suffers from significant observer variability. Artificial Intelligence (AI) systems, based on deep learning, have proven to achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance,…
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…
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…
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）
Delft University of Technology（代尔夫特理工大学）；Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；Academic Medical Center（学术医学中心）
Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patterns automatically. To do so, convolutional neural network was trained to detect cribriform growth patterns on 128 prostate needle biopsies. Ensemble learning taking into account other tumor growth patterns during training was used to cope with heterogeneous and limited tumor tissue occurrences. ROC and FROC analyses were applied to assess network performance regarding detection of biopsies harboring cribriform growth pattern. The ROC analysis yie…
Universitat Politècnica de València（瓦伦西亚理工大学）；University Clinical Hospital of Valencia（瓦伦西亚大学临床医院）；University of Granada（格拉纳达大学）
The Gleason scoring system is the primary diagnostic and prognostic tool for prostate cancer. In recent years, with the development of digitisation devices, the use of computer vision techniques for the analysis of biopsies has increased. However, to the best of the authors' knowledge, the development of algorithms to automatically detect individual cribriform patterns belonging to Gleason grade 4 has not yet been studied in the literature. The objective of the work presented in this paper is to develop a deep-learning-based system able to support pathologists in the daily analysis of prostate…
Non-Invasive Detection of PROState Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI
University of Chile（智利大学）；Clínica Dávila（达维拉诊所）；Zhejiang University（浙江大学）；Hospital Clinic, Universitat de Barcelona（巴塞罗那大学医院诊所）；San Sebastián University（圣塞巴斯蒂安大学）
The diagnosis of prostate cancer faces a problem with overdiagnosis that leads to damaging side effects due to unnecessary treatment. Research has shown that the use of multi-parametric magnetic resonance images to conduct biopsies can drastically help to mitigate the overdiagnosis, thus reducing the side effects on healthy patients. This study aims to investigate the use of deep learning techniques to explore computer-aid diagnosis based on MRI as input. Several diagnosis problems ranging from classification of lesions as being clinically significant or not to the detection and segmentation o…
Radboud University Medical Center（拉德堡德大学医学中心）；Linköping University（林雪平大学）
Prostate cancer is the most prevalent cancer among men in Western countries, with 1.1 million new diagnoses every year. The gold standard for the diagnosis of prostate cancer is a pathologists' evaluation of prostate tissue. To potentially assist pathologists deep-learning-based cancer detection systems have been developed. Many of the state-of-the-art models are patch-based convolutional neural networks, as the use of entire scanned slides is hampered by memory limitations on accelerator cards. Patch-based systems typically require detailed, pixel-level annotations for effective training. How…
Prostate cancer is one of the main diseases affecting men worldwide. The Gleason scoring system is the primary diagnostic tool for prostate cancer. This is obtained via the visual analysis of cancerous patterns in prostate biopsies performed by expert pathologists, and the aggregation of the main Gleason grades in a combined score. Computer-aided diagnosis systems allow to reduce the workload of pathologists and increase the objectivity. Recently, efforts have been made in the literature to develop algorithms aiming the direct estimation of the global Gleason score at biopsy/core level with gl…
AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives
Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London（医学图像计算中心，医学物理与生物医学工程系，伦敦大学学院）；Div of Surgery & Interventional Sci, University College London（外科与介入科学部，伦敦大学学院）；School of Computer Science and Engineering, University of Westminster（计算机科学与工程学院，威斯敏斯特大学）
Massachusetts Institute of Technology（麻省理工学院）；Harvard Medical School（哈佛医学院）；Boston University School of Medicine（波士顿大学医学院）；VA Boston Healthcare（波士顿退伍军人医疗中心）
Histopathological diagnoses of tumors in tissue biopsy after Hematoxylin and Eosin (H&E) staining is the gold standard for oncology care. H&E staining is slow and uses dyes, reagents and precious tissue samples that cannot be reused. Thousands of native nonstained RGB Whole Slide Image (RWSI) patches of prostate core tissue biopsies were registered with their H&E stained versions. Conditional Generative Adversarial Neural Networks (cGANs) that automate conversion of native nonstained RWSI to computational H&E stained images were then trained. High similarities between computational and H&E dye…
Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRI
Centre for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Centre for Medical Imaging, University College London（伦敦大学学院医学影像中心）；National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）
