Timely cancer reporting data are required in order to understand the impact of cancer, inform public health resource planning and implement cancer policy especially in Sub Saharan Africa where the reporting lag is behind world averages. Unstructured pathology reports, which contain tumor specific data, are the main source of information collected by cancer registries. Due to manual processing and labelling of pathology reports using the International Classification of Disease for oncology (ICD-O) codes, by human coders employed by cancer registries, has led to a considerable lag in cancer reporting. We present a hierarchical deep learning classification method that employs convolutional neural network models to automate the classification of 1813 anonymized breast cancer pathology reports with applicable ICD-O morphology codes across 9 classes. We demonstrate that the hierarchical deep learning classification method improves on performance in comparison to a flat multiclass CNN model for ICD-O morphology classification of the same reports.
Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts
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）
Automatic Classification of Pathology Reports using TF-IDF Features
Kimia Lab, University of Waterloo（滑铁卢大学Kimia实验室）；University of Waterloo（滑铁卢大学）；Kimia Lab（Kimia实验室）；Canada（加拿大）；Kimia Lab, University of Waterloo, Canada（滑铁卢大学Kimia实验室，加拿大）
病理报告可以说是医学中最重要的文件之一，其中包含有关患者活检样本视觉发现的解释信息。每份病理学报告在患者治疗后的保存期最长可达20年。癌症登记处处理和编码大量的自由文本病理报告，用于监测世界各地的癌症和肿瘤疾病。尽管它们拥有极其宝贵的信息，但病理学报告并没有以任何系统的方式用于促进计算病理学。因此，在这项研究中，我们研究了自动化机器学习技术，以识别/预测病理报告的主要诊断（基于ICD-0代码）。我们进行了实验，从报告中提取TF-IDF特征，并使用三种不同的方法进行分类-SVM，XGBoost和Logistic回归。我们构建了一个新的数据集，其中包含1，949份病理学报告，分为37个ICD-0类别，收集自四个不同的主要部位，即肺，肾，胸腺和睾丸。这些报告在从NCI Genomic Data Commons公共数据集收集PDF文件后手动转录成文本格式。我们随后通过删除OCR软件产生的不相关文本工件来预处理报告。在TF-IDF特征向量上，XGBoost分类器的最高分类准确率为92%，线性SVM的最高分类准确率为87%。此外，研究表明，TF-IDF向量适合突出显示报告中的重要关键词，这有助于癌症研究和诊断工作流程。结果令人鼓舞地证明了机器学习方法用于病理报告分类和编码的潜力。
DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks
University of Augsburg（奥格斯堡大学）；University Hospital Augsburg（奥格斯堡大学医院）；BioM Biotech Cluster Development GmbH（BioM生物技术集群发展有限公司）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）
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）
Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading
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）
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；College of Biomedical Engineering, Sichuan University（四川大学生物医学工程学院）；AI Lab, Tencent（腾讯AI实验室）；The Fourth Hospital of Hebei Medical University（河北医科大学第四医院）；Massachusetts General Hospital（麻省总医院）；Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；Harvard Medical School（哈佛医学院）；Children’s Hospital of Chongqing Medical University（重庆医科大学附属儿童医院）；The Affiliated Yantai Yuhuangding Hospital of Qingdao University（青岛大学附属烟台毓璜顶医院）
Colorectal Cancer Histopathological Grading using Multi-Scale Federated Learning
Department of Computer Science and Engineering, European University of Bangladesh（欧洲 bangladesh 大学 计算机科学与工程系）；Department of Computer Science and Engineering , BRAC University,Dhaka, Bangladesh（brac 大学 计算机科学与工程系）；Department of Computer Science and Engineering , Daffodil International University, Dhaka, Bangladesh（daffodil 国际大学 计算机科学与工程系）；Department of Institute of Information Technology, University of Dhaka, Bangladesh（达卡大学 信息科技学院）；Department of Computer Science and Engineering , BRAC University,Dhaka（brac 大学 计算机科学与工程系）；Ochsner Center for Outcomes Research, Ochsner Research, New Orleans, LA 70121, USA（ochsner 成果研究中心，ochsner 研究）
Biologic and Prognostic Feature Scores from Whole-Slide Histology Images Using Deep Learning
Stanford University School of Medicine（斯坦福大学医学院）；Center for Artificial Intelligence in Medicine & Imaging, Stanford Medical School（斯坦福医学院医学与成像人工智能中心）；Institute for Pathology and Cytology（病理与细胞学研究所）；University Hospital Bonn（波恩大学医院）；University Hospital Muenster（明斯特大学医院）
OncoPetNet: A Deep Learning based AI system for mitotic figure counting on H&E stained whole slide digital images in a large veterinary diagnostic lab setting
Mars Digital Technologies（玛氏数字科技）；Antech Diagnostics（Antech诊断公司）；Stanford University（斯坦福大学）
