Cancer histology reveals disease progression and associated molecular processes, and contains rich phenotypic information that is predictive of outcome. In this paper, we developed a computational approach based on deep learning to predict the overall survival and molecular subtypes of glioma patients from microscopic images of tissue biopsies, reflecting measures of microvascular proliferation, mitotic activity, nuclear atypia, and the presence of necrosis. Whole-slide images from 663 unique patients [IDH: 333 IDH-wildtype, 330 IDH-mutants, 1p/19q: 201 1p/19q non-codeleted, 129 1p/19q codeleted] were obtained from TCGA. Sub-images that were free of artifacts and that contained viable tumor with descriptive histologic characteristics were extracted, which were further used for training and testing a deep neural network. The output layer of the network was configured in two different ways: (i) a final Cox model layer to output a prediction of patient risk, and (ii) a final layer with sigmoid activation function, and stochastic gradient decent based optimization with binary cross-entropy loss. Both survival prediction and molecular subtype classification produced promising results using our model. The c-statistic was estimated to be 0.82 (p-value=4.8x10-5) between the risk scores of the proposed deep learning model and overall survival, while accuracies of 88% (area under the curve [AUC]=0.86) were achieved in the detection of IDH mutational status and 1p/19q codeletion. These findings suggest that the deep learning techniques can be applied to microscopic images for objective, accurate, and integrated prediction of outcome for glioma patients. The proposed marker may contribute to (i) stratification of patients into clinical trials, (ii) patient selection for targeted therapy, and (iii) personalized treatment planning.
Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma Grading
The University of Hong Kong（香港大学）；City University of Hong Kong（香港城市大学）；The Hang Seng University of Hong Kong（香港恒生大学）；Stanford University（斯坦福大学）；Centre for Artificial Intelligence and Robotics (CAIR), HKISI-CAS（中国科学院香港人工智能与机器人中心）
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network
Washington University in St. Louis（圣路易斯华盛顿大学）；Mallinckrodt Institute of Radiology（马林克罗特放射研究所）；Washington University School of Medicine（华盛顿大学医学院）；Institute for Informatics（信息学研究所）
异柠檬酸脱氢酶（IDH）突变和1 p/19 q共缺失状态是胶质瘤的重要预后标志。目前，它们是使用侵入性程序确定的。我们的目标是开发基于人工智能的方法来非侵入性地确定MRI中的这些分子改变。为此，2648例胶质瘤患者的术前MRI扫描（II-IV级）从华盛顿大学医学院收集（WUSM; n = 835）和公开可用的数据集，即脑肿瘤分割（BraTS; n = 378），LGG 1 p/19 q（n = 159），Ivy胶质母细胞瘤Atlas项目（Ivy GAP; n = 41）、癌症基因组图谱（TCGA; n = 461）和伊拉斯谟神经胶质瘤数据库（EGD; n = 774）。提出了一种2.5D混合卷积神经网络，通过利用来自MR扫描的成像特征和来自临床记录和肿瘤位置的先验知识特征，同时定位肿瘤并对其分子状态进行分类。模型在一个内部（TCGA）和两个外部（WUSM和EGD）测试集上进行了测试。对于IDH，表现最好的模型在内部、WUSM和EGD测试集上分别实现了0.925、0.874、0.933的受试者操作特征下的面积（AUROC）和0.899、0.702、0.853的精确-召回曲线下的面积（AUPRC）。对于1 p/19 q，最佳模型在这三个数据分割上分别实现了0.782，0.754，0.842的AUROC和0.588，0.713，0.782的AUPRC。该模型对未知数据的高准确性展示了其泛…
Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）；Hannover Medical School（汉诺威医学院）；Medical Center - University of Freiburg（弗莱堡大学医学中心）；RWTH Aachen University Hospital（亚琛工业大学医院）；Klinikum Bremen-Mitte（不来梅中部医院）；Leibniz Institute for Prevention Research and Epidemiology - BIPS（莱布尼茨预防研究与流行病学研究所）；Bremen Cancer Registry（不来梅癌症登记处）；Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB（弗劳恩霍夫光电、系统技术及图像处理研究所IOSB）；German Heart Center Berlin（柏林德国心脏中心）
Brain Tumor Classification on MRI in Light of Molecular Markers
Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA Department of Electrical \& Computer Engineering, College of Engineering, Northeastern University, Boston, MA, USA University of Maryland, Baltimore County, MD, USA；Princeton University, Princeton NJ 08544, USA, , WWW home page: http://users/ iekeland/web/welcome.html Universit\' e de Paris-Sud, Laboratoire d'Analyse Num\' e rique, B\ a timent 425, F-91405 Orsay Cedex, France
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
Else Kroener Fresenius Center for Digital Health（埃尔瑟·克勒纳·弗雷森纽斯数字健康中心）；TUD Dresden University of Technology（德累斯顿工业大学）；University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院及大学医院）；University of Applied Sciences of Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州））；IBM Research Europe（IBM欧洲研究院）；Centre de Recherche des Cordeliers（科尔得利研究所）；Sorbonne Université（索邦大学）；Université Paris Cité（巴黎城市大学）；Assistance Publique-Hôpitaux de Paris（巴黎公共医疗援助中心）；Université Paris-Est Créteil（巴黎东部克雷泰伊大学）；University Hospital RWTH Aachen（亚琛工业大学附属医院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；German Cancer Consortium (DKTK)（德国癌症联盟）；University Hospital Heidelberg（海德堡大学医院）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；University of Leeds（利兹大学）
Multiplex Imaging Analysis in Pathology: a Comprehensive Review on Analytical Approaches and Digital Toolkits
Weill Cornell Medicine（威尔康奈尔医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；University of Pisa（比萨大学）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Massachusetts Institute of Technology（麻省理工学院）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学博德研究所）
Visual Prompt Engineering for Vision Language Models in Radiology
Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany（德国癌症研究中心医学图像计算部）；Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany（海德堡大学数学与计算机科学学院）；Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, Germany（海德堡大学医学学院）
Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging
Carl Zeiss AG（卡尔蔡司股份公司）；University of Padua（帕多瓦大学）；University of Applied Sciences Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州高等专业学院））；Carl Zeiss Meditec AG（卡尔蔡司医疗科技股份公司）；IMEC（比利时微电子研究中心）；University Hospitals Leuven（鲁汶大学医院）；KU Leuven（鲁汶大学）
A Generative Foundation Model for Multimodal Histopathology
Department of Radiation Oncology, Stanford University School of Medicine（斯坦福大学医学院放射肿瘤学系）；Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine（斯坦福大学医学院精神病学与行为科学系）；Department of Statistics and Data Science, Yale University（耶鲁大学统计与数据科学系）；Department of Computer Science, Stanford University（斯坦福大学计算机科学系）；Department of Electrical Engineering, Stanford University（斯坦福大学电气工程系）；Department of Biomedical Data Science, Stanford University（斯坦福大学生物医学数据科学系）；Department of Radiology, Stanford University（斯坦福大学放射学系）；Center for Artificial Intelligence in Medicine and Imaging, Stanford University（斯坦福大学医学与影像人工智能中心）
Generating crossmodal gene expression from cancer histopathology improves multimodal AI predictions
School of Mathematical & Computational Sciences, Indian Association for the Cultivation of Science（数学与计算科学学院，印度科学培养协会）；The Alan Turing Institute（艾伦·图灵研究所）；Comprehensive Cancer Center, King’s College London（国王学院综合癌症中心）；Department of Computer Science and Engineering, Jadavpur University（计算机科学与工程系，贾瓦德pur大学）；MRC Biostatistics Unit, University of Cambridge（剑桥大学医学研究委员会生物统计学单位）；Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University（生物统计学、生物信息学与生物数学系，杰斐逊大学）；UCL Cancer Institute, Dept of Medical Physics & Biomedical Engineering, University College London（伦敦大学学院癌症研究所，医学物理与生物医学工程系）
Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis
Department of Radiation Oncology, TUM University Hospital, Munich, Germany（放射肿瘤科，慕尼黑技术大学医院，德国）；Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM)（人工智能在医疗和健康领域的主任，慕尼黑技术大学）；Chair for AI for Image-Guided Diagnosis and Therapy, Technical University of Munich (TUM)（人工智能在影像引导诊断和治疗领域的主任，慕尼黑技术大学）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML），德国慕尼黑）；Department of Computing, Imperial College London, London, UK（计算系，伦敦帝国学院，英国伦敦）；Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany（德国转化癌症研究联盟（DKTK），慕尼黑分部，德国慕尼黑）
Restoration of marker occluded hematoxylin and eosin stained whole slide histology images using generative adversarial networks
Image Data Analytics, Informatics IT, Merck \& Co., Inc., West Point, PA 19486, USA Image Data Analytics, Informatics IT, Merck \& Co., Inc., Rahway, NJ 07065, USA
Assessment of algorithms for mitosis detection in breast cancer histopathology images
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；Pathology Department, University Medical Center Utrecht（乌得勒支大学医学中心病理系）；BME Department, Case Western Reserve University（凯斯西储大学生物医学工程系）；MindLab, National University of Colombia（哥伦比亚国立大学思维实验室）；Department of Applied Mathematics and Computer Science, Technical University of Denmark（丹麦技术大学应用数学与计算机科学系）；IDSIA, USI-SUPSI（瑞士应用科技大学智能系统与数据分析研究所）；Department of Computer Engineering, Işık University（伊希克大学计算机工程系）；MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology（巴黎高等矿业学院PSL研究大学生物计算中心）；Institut Curie（居里研究所）；INSERM U900（法国国家健康与医学研究院U900）；Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology（台湾科技大学生物医学工程研究所）；Corporate R&D Center, Panasonic Healthcare Co., Ltd.（松下医疗保健公司企业研发中心）；School of Computing, Engineering and Physical Sciences, University of Central Lancashire（中央兰开夏大学计算、工程与物理科学学院）；I3S, UMR 7271 UNS-CNRS, Nice Sophia-Antipolis University（尼斯索菲亚安蒂波利斯大学I3S联合研究实验室）；CVSSP, University of Surrey（萨里大学视觉、声音和感知安全研究中心）；Department of Computer Science and Sheffield institute for Translational Medicine, University of Sheffield（谢菲尔德大学计算机科学与谢菲尔德转化医学研究所）；Department of Computer Science, University of Warwick（华威大学计算机系）；Department of Computer Science & Engineering, College of Engineering Qatar University（卡塔尔大学工程学院计算机科学与工程系）；The Michael Letcher Department of Cellular Pathology, Princess Alexandra Hospital NHS Trust（公主亚历山医院NHS信托迈克尔·莱切尔细胞病理学系）
Debiasing Counterfactuals In the Presence of Spurious Correlations
MILA (Quebec AI institute)（魁北克人工智能研究院）；Montreal Neurological Institute, McGill University（麦吉尔大学蒙特利尔神经学研究所）；The Alan Turing Institute（艾伦·图灵研究所）；Center for Intelligent Machines, McGill University（麦吉尔大学智能机器中心）；Institute for Digital Communications, School of Engineering, University of Edinburgh（爱丁堡大学工程学院数字通信研究所）
