An Automated Radiomics Framework for Postoperative Survival Prediction in Colorectal Liver Metastases using Preoperative MRI
Department of Medical Biophysics, University of Toronto（多伦多大学医学生物物理学系）；Physical Sciences Platform, Sunnybrook Research Institute（阳光医疗研究学院物理科学平台）；UCL Cancer Institute, University College London（伦敦大学学院癌症研究所）；Systems and Biomedical Engineering Department, Cairo University（开罗大学系统与生物医学工程系）；Department of Surgery, University of Toronto（多伦多大学外科医学系）；Department of Laboratory Medicine and Pathobiology, University of Toronto（多伦多大学实验室医学与病理学系）；Sunnybrook Health Sciences Centre（阳光健康科学中心）；Department of Medical Imaging, University of Toronto（多伦多大学医学影像学系）
Muhammad Alberb, Jianan Chen, Hossam El-rewaidy, Paul Karanicolas, Arun Seth, Yutaka Amemiya, Anne Martel, Helen Cheung
While colorectal liver metastasis (CRLM) is potentially curable via hepatectomy, patient outcomes remain highly heterogeneous. Postoperative survival prediction is necessary to avoid non-beneficial surgeries and guide personalized therapy. In this study, we present an automated AI-based framework for postoperative CRLM survival prediction using pre- and post-contrast MRI. We performed a retrospective study of 227 CRLM patients who had gadoxetate-enhanced MRI prior to curative-intent hepatectomy between 2013 and 2020. We developed a survival prediction framework comprising an anatomy-aware segmentation pipeline followed by a radiomics pipeline. The segmentation pipeline learns liver, CRLMs, and spleen segmentation from partially-annotated data, leveraging promptable foundation models to generate pseudo-labels. To support this pipeline, we propose SAMONAI, a prompt propagation algorithm that extends Segment Anything Model to 3D point-based segmentation. Predicted pre- and post-contrast segmentations are then fed into our radiomics pipeline, which extracts per-tumor features and predicts survival using SurvAMINN, an autoencoder-based multiple instance neural network for time-to-event survival prediction. SurvAMINN jointly learns dimensionality reduction and survival prediction from right-censored data, emphasizing high-risk metastases. We compared our framework against established methods and biomarkers using univariate and multivariate Cox regression. Our segmentation pipeline achieves median Dice scores of 0.96 (liver) and 0.93 (spleen), driving a CRLM segmentation Dice score of 0.78 and a detection F1-score of 0.79. Accurate segmentation enables our radiomics pipeline to achieve a survival prediction C-index of 0.69. Our results show the potential of integrating segmentation algorithms with radiomics-based survival analysis to deliver accurate and automated CRLM outcome prediction.
Automated Prediction of Postoperative Pancreatic Fistula Using Preoperative Computed Tomography
Department of Surgery, Mayo Clinic, Rochester, MN, USA（梅奥诊所外科部，罗切斯特，明尼苏达州，美国）；Department of Surgery, University of Auckland, Auckland, NZ（奥克兰大学外科部，奥克兰，新西兰）；Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA（健康照护科学中心，梅奥诊所，罗切斯特，明尼苏达州，美国）；Department of Artificial Intelligence（人工智能部）
The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)
ViT3Flow: A Test-Time Training Transformer MeanFlow for Postoperative Radiograph Synthesis in Scoliosis
The University of Hong Kong（香港大学）；The University of Hong Kong-Shenzhen Hospital（香港大学深圳医院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）
Accurate segmentation of brain resection cavities (RCs) aids in postoperative analysis and determining follow-up treatment. Convolutional neural networks (CNNs) are the state-of-the-art image segmentation technique, but require large annotated datasets for training. Annotation of 3D medical images is time-consuming, requires highly-trained raters, and may suffer from high inter-rater variability. Self-supervised learning strategies can leverage unlabeled data for training. We developed an algorithm to simulate resections from preoperative magnetic resonance images (MRIs). We performed self-sup…
Surgical resections are the most prevalent curative treatment for primary liver cancer. Tumors located in critical positions are known to complexify liver resections (LR). While experienced surgeons in specialized medical centers may have the necessary expertise to accurately anticipate LR complexity, and prepare accordingly, an objective method able to reproduce this behavior would have the potential to improve the standard routine of care, and avoid intra- and postoperative complications. In this article, we propose CoRe, an automated medical image processing pipeline for the prediction of p…
Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma by Integrating Multimodal Ultrasound and Clinical Data: A Multicenter Study
Shenzhen University（深圳大学）；Peking University Shenzhen Hospital（北京大学深圳医院）；Chinese PLA General Hospital（中国人民解放军总医院）
Zhejiang University（浙江大学）；University of Notre Dame（圣母大学）
Surgery is the only viable treatment for cataract patients with visual acuity (VA) impairment. Clinically, to assess the necessity of cataract surgery, accurately predicting postoperative VA before surgery by analyzing multi-view optical coherence tomography (OCT) images is crucially needed. Unfortunately, due to complicated fundus conditions, determining postoperative VA remains difficult for medical experts. Deep learning methods for this problem were developed in recent years. Although effective, these methods still face several issues, such as not efficiently exploring potential relations …
THA-Flow Generative Model: Prosthesis Geometry Prediction from Preoperative CT
Changzhou Jinse Medical Information Technology Co., Ltd.（常州金色医疗信息技术有限公司）；Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属第九人民医院）
PreSight: Preoperative Outcome Prediction for Parkinson's Disease via Region-Prior Morphometry and Patient-Specific Weighting
Beijing Jiaotong University（北京交通大学）；Nanyang Technological University（南洋理工大学）；Institute of Medical Technology, Peking University（北京大学医学技术研究院）；Beijing Tiantan Hospital, Capital Medical University（北京天坛医院）；University Health Network, University of Toronto（多伦多大学健康网络）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州研究院）
Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting
SINTEF Digital（挪威科技工业研究院数字分院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学（NTNU））；St. Olavs hospital, Trondheim University Hospital（特隆赫姆大学医院圣奥拉夫斯医院）
Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer
Department of Radiology, Antoni van Leeuwenhoek-Netherlands Cancer Institute, Amsterdam, The Netherlands（放射科，Antoni van Leeuwenhoek荷兰癌症研究所，阿姆斯特丹，荷兰）；University of Amsterdam（阿姆斯特丹大学）；AI Technology for Life, Department of Information and Computing Sciences, Department of Biology, Utrecht University（AI技术生命，信息与计算科学系，生物学系，乌得勒支大学）；GROW Oncology, Maastricht University（GROW肿瘤学，马斯特里赫特大学）；Department of Oncology, University of Cambridge（肿瘤科，剑桥大学）；Cancer Research UK Cambridge Centre, University of Cambridge（英国癌症研究会剑桥中心，剑桥大学）；Early Cancer Institute, University of Cambridge（早期癌症研究所，剑桥大学）；Cambridge University Hospitals NHS Foundation Trust（剑桥大学医院 NHS 基础信托）
Harvard University（哈佛大学）；University of Central Florida（中佛罗里达大学）
Lung cancer is the leading cause of cancer related mortality by a significant margin. While new technologies, such as image segmentation, have been paramount to improved detection and earlier diagnoses, there are still significant challenges in treating the disease. In particular, despite an increased number of curative resections, many postoperative patients still develop recurrent lesions. Consequently, there is a significant need for prognostic tools that can more accurately predict a patient's risk for recurrence. In this paper, we explore the use of convolutional neural networks (CNNs) fo…
Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases
Islamic Azad University（伊斯兰阿扎德大学）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；The University of Texas MD Anderson Cancer Center（德克萨斯大学 MD 安德森癌症中心）
USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification
Shenzhen Research Institute of Big Data（深圳市大数据研究院）；Zhejiang University of Finance and Economics（浙江财经大学）；Anhui University of Finance and Economics（安徽财经大学）；The Second Affiliated Hospital of Chinese University of Hong Kong (Longgang District People’s Hospital of Shenzhen)（香港中文大学第二附属医院（深圳市龙岗区人民医院））
Vision Transformers for Preoperative CT-Based Prediction of Histopathologic Chemotherapy Response Score in High-Grade Serous Ovarian Carcinoma
European Institute of Oncology, IEO, IRCCS（欧洲肿瘤研究所）；Politecnico di Milano（米兰理工大学）；INESC TEC, Faculty of Engineering, University of Porto（波尔图大学工程学院INESC TEC）；University of Cambridge（剑桥大学）；Cancer Research UK Cambridge Centre（英国癌症研究中心剑桥中心）；Cancer Research UK Cambridge Institute（英国癌症研究中心剑桥研究所）；Cambridge University Hospitals NHS Foundation Trust（剑桥大学医院NHS基金会信托）；Mayo Clinic（梅奥诊所）；Early Cancer Institute, University of Cambridge（剑桥大学早期癌症研究所）；Danube Private University（多瑙私立大学）；University of Milan（米兰大学）；Università Cattolica del Sacro Cuore（圣心天主教大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
PhysSFI-Net: Physics-informed Geometric Learning of Skeletal and Facial Interactions for Orthognathic Surgical Outcome Prediction
National Center for Stomatology & National Clinical Research Center for Oral Diseases（国家口腔医学中心&国家口腔疾病临床医学研究中心）；Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology（上海市口腔医学重点实验室&上海市口腔医学研究所）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Mathematics and Statistics, Lanzhou University（兰州大学数学与统计学院）；Faculty of Dentistry, The University of Hong Kong（香港大学牙医学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）
Preoperative brain tumor imaging: models and software for segmentation and standardized reporting
SINTEF Digital（挪威科技工业研究院数字研究所）；Norwegian University of Science and Technology（挪威科技大学）；St. Olavs hospital, Trondheim University Hospital（特隆赫姆大学医院圣奥拉夫医院）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）；Sahlgrenska Academy, University of Gothenburg（哥德堡大学萨尔格伦斯卡学院）；Oslo University Hospital（奥斯陆大学医院）；Amsterdam University Medical Centers, Vrije Universiteit（阿姆斯特丹自由大学阿姆斯特丹大学医学中心）；Cancer Center Amsterdam, Brain Tumor Center, Amsterdam University Medical Centers（阿姆斯特丹大学医学中心阿姆斯特丹癌症中心脑肿瘤中心）
SAMba-UNet: SAM2-Mamba UNet for Cardiac MRI in Medical Robotic Perception
University of Electronic Science and Technology of China（电子科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；School of Computer Science, Peking University（北京大学计算机学院）
Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble
Sheikh Zayed Institute for Pediatric Surgical Innovation（Sheikh Zayed儿童外科创新研究所）；Children’s National Hospital（儿童医院）；University of Washington（华盛顿大学）；Universidad Politécnica de Madrid（马德里理工大学）；CIBER-BBN；ISCIII；School of Medicine and Health Sciences（医学与健康科学学院）
