Non-invasive Liver Fibrosis Screening on CT Images using Radiomics
University of Toronto（多伦多大学）；The Hospital for Sick Children（病童医院）；Vector Institute（矢量研究所）；University Health Network（大学健康网络）；Princess Margaret Cancer Centre（玛格丽特公主癌症中心）；Techna Institute（Techna研究所）
Jay J. Yoo, Khashayar Namdar, Sean Carey, Sandra E. Fischer, Chris McIntosh, Farzad Khalvati, Patrik Rogalla
Objectives: To develop and evaluate a radiomics machine learning model for detecting liver fibrosis on CT of the liver.
Methods: For this retrospective, single-centre study, radiomic features were extracted from Regions of Interest (ROIs) on CT images of patients who underwent simultaneous liver biopsy and CT examinations. Combinations of contrast, normalization, machine learning model, and feature selection method were determined based on their mean test Area Under the Receiver Operating Characteristic curve (AUC) on randomly placed ROIs. The combination and selected features with the highest AUC were used to develop a final liver fibrosis screening model.
Results: The study included 101 male and 68 female patients (mean age = 51.2 years $\pm$ 14.7 [SD]). When averaging the AUC across all combinations, non-contrast enhanced (NC) CT (AUC, 0.6100; 95% CI: 0.5897, 0.6303) outperformed contrast-enhanced CT (AUC, 0.5680; 95% CI: 0.5471, 0.5890). The combination of hyperparameters and features that yielded the highest AUC was a logistic regression model with inputs features of maximum, energy, kurtosis, skewness, and small area high gray level emphasis extracted from non-contrast enhanced NC CT normalized using Gamma correction with $γ$ = 1.5 (AUC, 0.7833; 95% CI: 0.7821, 0.7845), (sensitivity, 0.9091; 95% CI: 0.9091, 0.9091).
Conclusions: Radiomics-based machine learning models allow for the detection of liver fibrosis with reasonable accuracy and high sensitivity on NC CT. Thus, these models can be used to non-invasively screen for liver fibrosis, contributing to earlier detection of the disease at a potentially curable stage.
Does Machine Learning Outperform Traditional Fibrosis Scores in Predicting Liver Cirrhosis Risk? A Longitudinal EHR-Based Study
Center for Health Systems Innovation, Oklahoma State University（俄克拉荷马州立大学健康系统创新中心）；Department of Management Science and Information Systems, Oklahoma State University（俄克拉荷马州立大学管理科学与信息系统系）；Department of Health Services Administration, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校健康服务管理系）；Division of Gastroenterology and Hepatology, Department of Medicine, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校消化内科与肝病科）；College of Medicine, The University of Oklahoma Health Campus（俄克拉荷马大学健康校园医学院）；Department of Family and Community Medicine, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校家庭与社区医学系）
Multi-modal Liver Segmentation and Fibrosis Staging Using Real-world MRI Images
Multiscale X-ray Imaging (MXI) Lab, Department of Mechanical Engineering, University College London（多尺度X射线成像实验室，机械工程系，伦敦大学学院）；Centre for Clinical Brain Sciences, University of Edinburgh（临床脑科学中心，爱丁堡大学）；MRC Weatherall Institute of Molecular Medicine, University of Oxford（MRC韦伯尔分子医学研究所，牛津大学）
Rutgers University（罗格斯大学）；Rutgers Robert Wood Johnson Medical School（罗格斯罗伯特·伍德·约翰逊医学院）
Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver diseases (CLD) which can progress to liver cancer. The severity and treatment of NAFLD is determined by NAFLD Activity Scores (NAS)and liver fibrosis stage, which are usually obtained from liver biopsy. However, biopsy is invasive in nature and involves risk of procedural complications. Current methods to predict the fibrosis and NAS scores from noninvasive CT images rely heavily on either a large annotated dataset or transfer learning using pretrained networks. However, the availability of a large anno…
PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Vanderbilt University（范德堡大学）；PingAn Technology（平安科技）
Ultrasound (US) is a critical modality for diagnosing liver fibrosis. Unfortunately, assessment is very subjective, motivating automated approaches. We introduce a principled deep convolutional neural network (CNN) workflow that incorporates several innovations. First, to avoid overfitting on non-relevant image features, we force the network to focus on a clinical region of interest (ROI), encompassing the liver parenchyma and upper border. Second, we introduce global heteroimage fusion (GHIF), which allows the CNN to fuse features from any arbitrary number of images in a study, increasing its…
Liver Fibrosis Quantification and Analysis: The LiQA Dataset and Baseline Method
School of Data Science, Fudan University（复旦大学数据科学学院）；Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究院）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑启发智能科学技术研究院）；Department of Radiology, Shanghai Public Health Clinical Center（上海公共卫生临床中心放射科）；Department of Computer Science, Information Technology University（信息技术大学计算机科学系）；Department of Computer Science, Lahore University of Management Sciences（拉合尔管理科学大学计算机科学系）
Semi-supervised Liver Segmentation and Patch-based Fibrosis Staging with Registration-aided Multi-parametric MRI
1 Intelligent Modelling \& Analysis Group (IMA), 2 Lab for Uncertainty in Data；Decision Making (LUCID), \ of Computer Science, University of Nottingham, UK 3 Nottingham Biomedical Research Centre (BRC), \ of Medicine, University of Nottingham, UK；Intelligent Modelling \& Analysis Group (IMA), School of Computer Science, University of Nottingham, UK Lab for Uncertainty in Data；Decision Making (LUCID), School of Computer Science, University of Nottingham, UK Nottingham Biomedical Research Centre (BRC), School of Medicine, University of Nottingham, UK
Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳市大数据研究院）
Accurate analysis of the fibrosis stage plays very important roles in follow-up of patients with chronic hepatitis B infection. In this paper, a deep learning framework is presented for automatically liver fibrosis prediction. On contrary of previous works, our approach can take use of the information provided by multiple ultrasound images. An indicator-guided learning mechanism is further proposed to ease the training of the proposed model. This follows the workflow of clinical diagnosis and make the prediction procedure interpretable. To support the training, a dataset is well-collected whic…
Computer Science Dept. Rutgers University（罗格斯大学计算机科学系）；Division of Gastroenterology and Hepatology Rutgers Robert Wood Johnson Medical School（罗格斯罗伯特·伍德·约翰逊医学院胃肠病学和肝病学分部）
Non-Alcoholic Fatty Liver Disease (NAFLD) is becoming increasingly prevalent in the world population. Without diagnosis at the right time, NAFLD can lead to non-alcoholic steatohepatitis (NASH) and subsequent liver damage. The diagnosis and treatment of NAFLD depend on the NAFLD activity score (NAS) and the liver fibrosis stage, which are usually evaluated from liver biopsies by pathologists. In this work, we propose a novel method to automatically predict NAS score and fibrosis stage from CT data that is non-invasive and inexpensive to obtain compared with liver biopsy. We also present a meth…
Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong（香港中文大学深圳大数据研究院）；Huazhong University of Science and Technology（华中科技大学）；Shenzhen Third People’s Hospital（深圳市第三人民医院）
With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD). Due to the noisy data, expensive annotations of US images, the application of Artificial Intelligence (AI) assisting approaches encounters a bottleneck. Besides, the use of mono-modal US data limits the further improve of the classification results. In this work, we innovatively propose a multi-modal fusion network with active learning (MMFN-AL) for ALFD to exploit the information of multiple modalities, eliminate the noisy data and red…
MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging
School of Data Science, Fudan University（复旦大学数据科学学院）；Shanghai Public Health Clinical Center, Fudan University（复旦大学附属公共卫生临床中心）；University of Oxford（牛津大学）；National Institute of Health Data Science, Peking University（北京大学健康数据科学研究院）；Institute of Medical Technology, Peking University（北京大学医学技术研究院）；State Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能国家重点实验室）
Precise Liver Tumor Segmentation in CT Using a Hybrid Deep Learning-Radiomics Framework
School of Information Science & Engineering, Shandong Normal University（信息科学与工程学院，山东师范大学）；Tajik State University of Law, Business and Politics（塔吉克国立法律、商业与政治大学）
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）
TMI-CLNet: Triple-Modal Interaction Network for Chronic Liver Disease Prognosis From Imaging, Clinical, and Radiomic Data Fusion
Hangzhou Dianzi University（杭州电子科技大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Information Technology（深圳信息职业技术学院）；Shenzhen University（深圳大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark
School of Data Science, Fudan University, Shanghai, China（复旦大学数据科学学院）；Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China（复旦大学上海公共卫生临床中心放射科）；Department of Electrical and Computer Engineering, Northwestern University, Evanston, USA（西北大学电气与计算机工程系）；Shanghai Key Laboratory of Flexible Medical Robotics, Tongren Hospital, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China（上海柔性医疗机器人重点实验室）；School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China（上海交通大学生物医学工程学院）；School of Computer Science, University of Nottingham, Nottingham, UK（诺丁汉大学计算机科学学院）；Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China（上海交通大学生物医学工程学院医疗机器人研究所）；College of Computer Science and Technology, Huaqiao University, Xiamen, China（华侨大学计算机科学与技术学院）；School of Electronic Information (School of Artificial Intelligence), Northwest University, Xi'an, China（西北大学电子信息学院（人工智能学院））；Department of Mechanical Engineering, University College London, London, UK（伦敦大学学院机械工程系）；Institute of Neuroscience and Cardiovascular Research, University of Edinburgh, Edinburgh, UK（爱丁堡大学神经科学与心血管研究学院）；CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, China（中国科学院纳米科学卓越中心）；School of Control Science and Engineering, Shandong University, Jinan, China（山东大学控制科学与工程学院）；School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, UK（伯明翰大学工程学院）
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）
Predicting Patient Survival with Airway Biomarkers using nn-Unet/Radiomics
Normandie Univ., INSA Rouen, LITIS, 76801, Saint Etienne du Rouvray, France（诺曼底大学）；Normandie Univ., UNICAEN, ENSICAEN, CNRS, GREYC, 14000 Caen, France（诺曼底大学）；Nuclear Medicine Department, Henri Becquerel Cancer Center, Rouen, France（贝克勒尔癌症中心核医学部）；Radiotherapy Department, Henri Becquerel Cancer Center, Rouen, France（贝克勒尔癌症中心放射治疗部）；Siemens Healthineers（西门子医疗）
A Polarization and Radiomics Feature Fusion Network for the Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma
University College London（伦敦大学学院）；Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；Fujian Medical University Cancer Hospital（福建医科大学肿瘤医院）；Fujian Cancer Hospital（福建省肿瘤医院）
University of Toronto（多伦多大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Quantitative medical image computing (radiomics) has been widely applied to build prediction models from medical images. However, overfitting is a significant issue in conventional radiomics, where a large number of radiomic features are directly used to train and test models that predict genotypes or clinical outcomes. In order to tackle this problem, we propose an unsupervised learning pipeline composed of an autoencoder for representation learning of radiomic features and a Gaussian mixture model based on minimum message length criterion for clustering. By incorporating probabilistic modeli…
University of Toronto（多伦多大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）；Centre Hospitalier Universitaire de Lyon（里昂大学医院中心）
Colorectal cancer is one of the most common and lethal cancers and colorectal cancer liver metastases (CRLM) is the major cause of death in patients with colorectal cancer. Multifocality occurs frequently in CRLM, but is relatively unexplored in CRLM outcome prediction. Most existing clinical and imaging biomarkers do not take the imaging features of all multifocal lesions into account. In this paper, we present an end-to-end autoencoder-based multiple instance neural network (AMINN) for the prediction of survival outcomes in multifocal CRLM patients using radiomic features extracted from cont…
AI-Driven Non-Invasive Detection and Staging of Steatosis in Fatty Liver Disease Using a Novel Cascade Model and Information Fusion Techniques
University of Tehran（德黑兰大学）；York University（约克大学）；Northeastern University（东北大学）；Antalya Bilim University（安塔利亚比尔姆大学）；Research Institute for Gastroenterology and Liver Diseases（胃肠病学和肝病学研究所）；Shahid Beheshti University of Medical Sciences（沙希德贝赫什提医科大学）；University of Waterloo（滑铁卢大学）
Shandong Normal University（山东师范大学）；Western University（韦仕敦大学）
Simultaneous segmentation and detection of liver tumors (hemangioma and hepatocellular carcinoma (HCC)) by using multi-modality non-contrast magnetic resonance imaging (NCMRI) are crucial for the clinical diagnosis. However, it is still a challenging task due to: (1) the HCC information on NCMRI is invisible or insufficient makes extraction of liver tumors feature difficult; (2) diverse imaging characteristics in multi-modality NCMRI causes feature fusion and selection difficult; (3) no specific information between hemangioma and HCC on NCMRI cause liver tumors detection difficult. In this stu…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shandong University（山东大学）；Beijing University of Chinese Medicine（北京中医药大学）
Università degli Studi di Genova（热那亚大学）；IRCCS San Martino Hospital（IRCCS圣马蒂诺医院）
Due to the need for quick and effective treatments for liver diseases, which are among the most common health problems in the world, staging fibrosis through non-invasive and economic methods has become of great importance. Taking inspiration from diagnostic laparoscopy, used in the past for hepatic diseases, in this paper ultrasound images of the liver are studied, focusing on a specific region of the organ where the Glisson's capsule is visible. In ultrasound images, the Glisson's capsule appears in the shape of a line which can be extracted via classical methods in literature. By making use…
Pathobiological Dictionary Defining Pathomics and Texture Features: Addressing Understandable AI Issues in Personalized Liver Cancer; Dictionary Version LCP1.0
University of British Columbia（不列颠哥伦比亚大学）；BC Cancer Research Institute（BC癌症研究所）；TECVICO Corp.（TECVICO公司）；Tehran University of Medical Sciences（德黑兰医科大学）；Motamed Cancer Institute（莫塔梅德癌症研究所）；University of Iowa Carver College of Medicine（爱荷华大学卡弗医学院）；Iran University of Medical Sciences（伊朗医科大学）；Université Laval（拉瓦尔大学）
Heterogeneous Image-based Classification Using Distributional Data Analysis
MD Anderson Cancer Center（MD安德森癌症中心）；UT Southwestern Medical Center（西南医学中心）；UC Davis Comprehensive Cancer Center（加州大学戴维斯分校综合癌症中心）；Baylor College of Medicine（贝勒医学院）
