Hepatocellular carcinoma is the most spread primary liver cancer across the world ($\sim$80\% of the liver tumors). The gold standard for HCC diagnosis is liver biopsy. However, in the clinical routine, expert radiologists provide a visual diagnosis by interpreting hepatic CT-scans according to a standardized protocol, the LI-RADS, which uses five radiological criteria with an associated decision tree. In this paper, we propose an automatic approach to predict histology-proven HCC from CT images in order to reduce radiologists' inter-variability. We first show that standard deep learning methods fail to accurately predict HCC from CT-scans on a challenging database, and propose a two-step approach inspired by the LI-RADS system to improve the performance. We achieve improvements from 6 to 18 points of AUC with respect to deep learning baselines trained with different architectures. We also provide clinical validation of our method, achieving results that outperform non-expert radiologists and are on par with expert ones.
A Hierarchical Transformer Encoder to Improve Entire Neoplasm Segmentation on Whole Slide Image of Hepatocellular Carcinoma
Université Paris Cité（巴黎西岱大学）；University of Applied Sciences of Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州高等专业学院））；French University Institute (IUF)（法国大学研究院）
针对肝细胞癌 WSI 全肿瘤分割难题，提出分层 Transformer 架构 HiTrans，在 4096×4096 图像块上学习全局依赖并提升分割性能。
Chang Gung Memorial Hospital（长庚纪念医院）；Chang Gung University（长庚大学）；PAII Inc.（PAII公司）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
Hepatocellular carcinoma (HCC) can be potentially discovered from abdominal computed tomography (CT) studies under varied clinical scenarios, e.g., fully dynamic contrast enhanced (DCE) studies, non-contrast (NC) plus venous phase (VP) abdominal studies, or NC-only studies. We develop a flexible three-dimensional deep algorithm, called hetero-phase volumetric detection (HPVD), that can accept any combination of contrast-phase inputs and with adjustable sensitivity depending on the clinical purpose. We trained HPVD on 771 DCE CT scans to detect HCCs and tested on external 164 positives and 206 …
BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma
Department of Radiology \& Biomedical Imaging, Department of Biomedical Engineering, Department of Electrical Engineering, Department of Statistics \& Data Science Yale University, New Haven, CT, 06510, USA
Mask-guided Data Augmentation for Multiparametric MRI Generation with a Rare Hepatocellular Carcinoma
Universidad Industrial de Santander（桑坦德工业大学）；University of Toulouse（图卢兹大学）；Université Paul Sabatier Toulouse 3（图卢兹第三大学（保罗·萨巴蒂埃大学））；Bicêtre University Hospital（比塞特尔大学医院）；Assistance publique des hôpitaux de Paris（巴黎公共援助医院）；Université Claude Bernard Lyon 1（里昂第一大学（克洛德·贝尔纳里昂第一大学））
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）
Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images
Visvesvaraya National Institute of Technology（维斯瓦拉亚国家理工学院）；AIIMS Nagpur（那格浦尔全印度医学科学研究所）；Indian Institute of Technology Mandi（曼迪印度理工学院）；Universitat de Barcelona（巴塞罗那大学）
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）
A Retrospective Systematic Study on Hierarchical Sparse Query Transformer-assisted Ultrasound Screening for Early Hepatocellular Carcinoma
Northwestern Polytechnical University（西北工业大学）；The First Affiliated Hospital of Sun Yat-Sen University（中山大学附属第一医院）；The Seventh Affiliated Hospital of Sun Yat-Sen University（中山大学附属第七医院）；The First Affiliated Hospital of Guangxi Medical University（广西医科大学附属第一医院）；Xi’an Jiaotong University（西安交通大学）
Yale University（耶鲁大学）；University of Washington（华盛顿大学）
This work proposes a pipeline to predict treatment response to intra-arterial therapy of patients with Hepatocellular Carcinoma (HCC) for improved therapeutic decision-making. Our graph neural network model seamlessly combines heterogeneous inputs of baseline MR scans, pre-treatment clinical information, and planned treatment characteristics and has been validated on patients with HCC treated by transarterial chemoembolization (TACE). It achieves Accuracy of $0.713 \pm 0.075$, F1 of $0.702 \pm 0.082$ and AUC of $0.710 \pm 0.108$. In addition, the pipeline incorporates uncertainty estimation to…
Technische Universität Berlin（柏林工业大学）；Aignostics GmbH（Aignostics 公司）；Charité Universitätsmedizin Berlin（柏林夏里特医学院）；Berlin Institute of Health at Charité – Universitätsmedizin Berlin（柏林健康研究所（夏里特医学院））；University Hospital Düsseldorf（杜塞尔多夫大学医院）；Ludwig-Maximilians-Universität München（慕尼黑大学）；German Cancer Research Center（德国癌症研究中心）
In this paper, we present a deep learning segmentation approach to classify and quantify the two most prevalent primary liver cancers - hepatocellular carcinoma and intrahepatic cholangiocarcinoma - from hematoxylin and eosin (H&E) stained whole slide images. While semantic segmentation of medical images typically requires costly pixel-level annotations by domain experts, there often exists additional information which is routinely obtained in clinical diagnostics but rarely utilized for model training. We propose to leverage such weak information from patient diagnoses by deriving complementa…
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The First Affiliated Hospital of Jinan University（暨南大学附属第一医院）
Automatic segmentation of hepatocellular carcinoma (HCC) in Digital Subtraction Angiography (DSA) videos can assist radiologists in efficient diagnosis of HCC and accurate evaluation of tumors in clinical practice. Few studies have investigated HCC segmentation from DSA videos. It shows great challenging due to motion artifacts in filming, ambiguous boundaries of tumor regions and high similarity in imaging to other anatomical tissues. In this paper, we raise the problem of HCC segmentation in DSA videos, and build our own DSA dataset. We also propose a novel segmentation network called DSA-LT…
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Whole-slide imaging which is a method of scanning glass slides have been employed for diagnosis of HCC. Using high resolution Whole-slide images is infeasible for Convolutional Neural Network applications. Hence tiling the Whole-slide images is a common methodology for assigning Convolutional Neural Networks for classification and segmentation. Determination of the tile size affects the performance of the algorithms since small field of view can not capture the information on a larger scale and large field of …
A baseline for machine-learning-based hepatocellular carcinoma diagnosis using multi-modal clinical data
Heidelberg University（海德堡大学）；Mannheim Institute for Intelligent Systems in Medicine（曼海姆医学智能系统研究所）；UMM Mannheim（曼海姆大学医学中心）；Mannheim Medical Faculty（曼海姆医学院）；Clinic for Radiology and Nuclear Medicine（放射科与核医学科）；Interdisciplinary Center for Scientific Computing（跨学科科学计算中心）；Central Institute for Computer Engineering（中央计算机工程研究所）；Heidelberg Center for Model-Based AI, Data Analysis and Modeling in Medicine（海德堡基于模型的人工智能、医学数据分析与建模中心）；School of Information Engineering, Nanjing University of Finance and Economics（南京财经大学信息工程学院）
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局资讯通信研究院）；National University of Singapore（新加坡国立大学）；Singapore General Hospital（新加坡中央医院）；National Cancer Centre（新加坡国立癌症中心）
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide. Understanding the underlying gene mutations in HCC provides great prognostic value for treatment planning and targeted therapy. Radiogenomics has revealed an association between non-invasive imaging features and molecular genomics. However, imaging feature identification is laborious and error-prone. In this paper, we propose an end-to-end deep learning framework for mutation prediction in APOB, COL11A1 and ATRX genes using multiphasic CT scans. Con…
Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine（浙江大学医学院附属杭州市第一人民医院）；Syracuse University（雪城大学）；Zhejiang University（浙江大学）；The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院）
This study aimed to develop a classifier of prognosis after resection or liver transplantation (LT) for HCC by directly analysing the ubiquitously available histological images using deep learning based neural networks. Nucleus map set was used to train U-net to capture the nuclear architectural information. Train set included the patients with HCC treated by resection and has a distinct outcome. LT set contained patients with HCC treated by LT. Train set and its nuclear architectural information extracted by U-net were used to train MobileNet V2 based classifier (MobileNetV2_HCC_Class), purpo…
Zhejiang University（浙江大学）；Stevens Institute of Technology（斯蒂文斯理工学院）；Zhejiang Lab（之江实验室）；Zhejiang University of Technology（浙江工业大学）；Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究中心）
The microvascular invasion (MVI) is a major prognostic factor in hepatocellular carcinoma, which is one of the malignant tumors with the highest mortality rate. The diagnosis of MVI needs discovering the vessels that contain hepatocellular carcinoma cells and counting their number in each vessel, which depends heavily on experiences of the doctor, is largely subjective and time-consuming. However, there is no algorithm as yet tailored for the MVI detection from pathological images. This paper collects the first pathological liver image dataset containing 522 whole slide images with labels of v…
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…
Sun Yat-sen University（中山大学）；The First Affiliated Hospital, Sun Yat-sen University（中山大学附属第一医院）；Infervision Inc.（推想医疗科技股份有限公司）
Hepatocellular carcinoma (HCC) is the second most frequent cause of malignancy-related death and is one of the diseases with the highest incidence in the world. Because the liver is the only organ in the human body that is supplied by two major vessels: the hepatic artery and the portal vein, various types of malignant tumors can spread from other organs to the liver. And due to the liver masses' heterogeneous and diffusive shape, the tumor lesions are very difficult to be recognized, thus automatic lesion detection is necessary for the doctors with huge workloads. To assist doctors, this work…
Hepato-LLaVA: An Expert MLLM with Sparse Topo-Pack Attention for Hepatocellular Pathology Analysis on Whole Slide Images
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）；Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（pathology department, 国家癌症中心/国家癌症临床研究中心/癌症医院, 中国医学科学院和北京协和医学院）
Eastern Hepatobiliary Surgery Hospital（东方肝胆外科医院）；Ping An Technology（平安科技）；Georgia Institute of Technology（佐治亚理工学院）；PAII Inc.（PAII公司）
Objectives: to propose a fully-automatic computer-aided diagnosis (CAD) solution for liver lesion characterization, with uncertainty estimation. Methods: we enrolled 400 patients who had either liver resection or a biopsy and was diagnosed with either hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma, or secondary metastasis, from 2006 to 2019. Each patient was scanned with T1WI, T2WI, T1WI venous phase (T2WI-V), T1WI arterial phase (T1WI-A), and DWI MRI sequences. We propose a fully-automatic deep CAD pipeline that localizes lesions from 3D MRI studies using key-slice parsing an…
A Digital Pathology Resource for Liver Cancer Quantification with Datasets, Benchmarks, and Tools
Department of Pathology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China（北京清华长庚医院病理科，临床医学学院，清华大学医学院，清华大学，北京，中国）；Shenzhen International Graduate School, Tsinghua University, Shenzhen, China（深圳国际研究生院，清华大学，深圳，中国）
Triple-Phase Sequential Fusion Network for Hepatobiliary Phase Liver MRI Synthesis
Yu-Yue Pathology Research Center, Jinfeng Laboratory（于越病理研究中心，锦峰实验室）；Department of Clinical Laboratory Medicine, The First Affiliated Hospital of Army Medical University（临床实验室医学部，陆军军医大学第一附属医院）；Laboratory of Intelligent Collaborative Computing, University of Electronic Science and Technology of China（智能协同计算实验室，电子科技大学）
REACT-KD: Region-Aware Cross-modal Topological Knowledge Distillation for Interpretable Medical Image Classification
Department of Health Technology and Informatics, Hong Kong Polytechnic University（健康科技与信息技术系，香港理工大学）；Department of Nuclear Medicine and PET, Hong Kong Sanatorium and Hospital（核医学与PET部门，香港疗养院及医院）
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）
Diagnosis of pulmonary lesions from computed tomography (CT) is important but challenging for clinical decision making in lung cancer related diseases. Deep learning has achieved great success in computer aided diagnosis (CADx) area for lung cancer, whereas it suffers from label ambiguity due to the difficulty in the radiological diagnosis. Considering that invasive pathological analysis serves as the clinical golden standard of lung cancer diagnosis, in this study, we solve the label ambiguity issue via a large-scale radio-pathomics dataset containing 5,134 radiological CT images with patholo…
University of Innsbruck（因斯布鲁克大学）；Medical University of Innsbruck（因斯布鲁克医科大学）
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults, and the most common cause of death of people suffering from cirrhosis. The segmentation of liver lesions in CT images allows assessment of tumor load, treatment planning, prognosis and monitoring of treatment response. Manual segmentation is a very time-consuming task and in many cases, prone to inaccuracies and automatic tools for tumor detection and segmentation are desirable. In this paper, we compare two network architectures, one that is composed of one neural network and manages the segmentation tas…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Lenovo Research（联想研究院）
Liver tumor segmentation plays an important role in hepatocellular carcinoma diagnosis and surgical planning. In this paper, we propose a novel Semantic Feature Attention Network (SFAN) for liver tumor segmentation from Computed Tomography (CT) volumes, which exploits the impact of both low-level and high-level features. In the SFAN, a Semantic Attention Transmission (SAT) module is designed to select discriminative low-level localization details with the guidance of neighboring high-level semantic information. Furthermore, a Global Context Attention (GCA) module is proposed to effectively fus…
Johns Hopkins University（约翰斯·霍普金斯大学）；PAII Inc.（PAII公司）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Ping An Technology（平安科技）；Shanghai Jiao Tong University（上海交通大学）
The boundary of tumors (hepatocellular carcinoma, or HCC) contains rich semantics: capsular invasion, visibility, smoothness, folding and protuberance, etc. Capsular invasion on tumor boundary has proven to be clinically correlated with the prognostic indicator, microvascular invasion (MVI). Investigating tumor boundary semantics has tremendous clinical values. In this paper, we propose the first and novel computational framework that disentangles the task into two components: spatial vertex localization and sequential semantic classification. (1) A HCC tumor segmentor is built for tumor mask …
The Chinese University of Hong Kong（香港中文大学）；Imsight Medical Technology, Inc.（伊赛智能医疗科技有限公司）
Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2D and 3D FCNs, serve as the back-bone in many volumetric image segmentation. However, 2D convolutions can not fully leverage the spatial information along the third dimension while 3D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we …
Methodological Explainability Evaluation of an Interpretable Deep Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating Counterfactual Explanations and Layerwise Relevance Propagation: A Prospective In Silico Trial
Maastricht University（马斯特里赫特大学）；Maastricht University Medical Center+（马斯特里赫特大学医学中心+）；The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院）；Guizhou University（贵州大学）；Université Libre de Bruxelles（布鲁塞尔自由大学）
Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images
Division of Computing and Data Science, Ruđer Bošković Institute（鲁德·波斯科维奇研究所计算与数据科学部）；Sitnik Ventures UG（Sitnik Ventures公司）；Department of Pathology and Cytology, Dubrava University Hospital（杜布拉瓦大学医院病理科与细胞学部）；Clinical Division for Pathology and Cytology, Clinical Hospital Center Zagreb（扎格雷布临床医院中心病理科与细胞学部）；Division of Molecular Medicine, Ruđer Bošković Institute（鲁德·波斯科维奇研究所分子医学部）
Technische Universit\"at M\"unchen, Image-Based Biomedical Modeling Group, Arccisstrasse 21, 80333 Munich Technische Universit\"at M\"unchen, Institute for diagnostic；interventional Radiology, Ismaninger Str. 22, 81675 Munich + Ludwig-Maximilians-Universit\"at, German Center for Vertigo
Automatic non-invasive assessment of hepatocellular carcinoma (HCC) malignancy has the potential to substantially enhance tumor treatment strategies for HCC patients. In this work we present a novel framework to automatically characterize the malignancy of HCC lesions from DWI images. We predict HCC malignancy in two steps: As a first step we automatically segment HCC tumor lesions using cascaded fully convolutional neural networks (CFCN). A 3D neural network (SurvivalNet) then predicts the HCC lesions' malignancy from the HCC tumor segmentation. We formulate this task as a classification prob…
AMPLIFAI: A Multiphase CT Dataset for Benchmarking Clinical Reasoning in LI-RADS Assessment of Liver Lesions
University of Maryland, College Park（马里兰大学帕克分校）；University of Maryland School of Medicine（马里兰大学医学院）；University of Maryland Institute for Health Computing（马里兰大学健康计算研究所）；University of Maryland Medical System（马里兰大学医学系统）
PAII Inc.（PAII 公司）；Vanderbilt University（范德堡大学）；University of Texas, Arlington（德克萨斯大学阿灵顿分校）；Chang Gung Memorial Hospital（长庚纪念医院）；PingAn Technology（平安科技）
Non-invasive radiological-based lesion characterization and identification, e.g., to differentiate cancer subtypes, has long been a major aim to enhance oncological diagnosis and treatment procedures. Here we study a specific population of human subjects, with the hope of reducing the need for invasive surgical biopsies of liver cancer patients, which can cause many harmful side-effects. To this end, we propose a fully-automated and multi-stage liver tumor characterization framework designed for dynamic contrast computed tomography (CT). Our system comprises four sequential processes of tumor …
Stanford University（斯坦福大学）；Varian Medical Systems（瓦里安医疗系统公司）；Huiyihuiying Medical Co（慧医慧影医疗公司）；Google Inc（谷歌公司）
Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. While various algorithms for delineating organ-at-risks (OARs) and tumor targets have been proposed, automatic segmentation of livers and liver tumors remains intractable due to their low tissue contrast with respect to the surrounding organs and their deformable shape in CT images. The U-Net has gained increasing popularity recently for ima…
A Hierarchical Geometry-guided Transformer for Histological Subtyping of Primary Liver Cancer
Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, China（病理科、南京鼓楼医院、南京大学附属医院）；Department of Biostatistics, School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109-2029（生物统计学系、公共卫生学院）
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）
