2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Guangzhou First People’s Hospital, School of Medicine, South China University of Technology（华南理工大学医学院附属广州市第一人民医院）；Guizhou Provincial People’s Hospital（贵州省人民医院）；Henan Cancer Hospital（河南省肿瘤医院）；Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences（广东省人民医院、广东省医学科学院）；Beihang University（北京航空航天大学）
Lingwei Meng, Di Dong, Xin Chen, Mengjie Fang, Rongpin Wang, Jing Li, Zaiyi Liu, Jie Tian
备注：
Published in IEEE Journal of Biomedical and Health Informatics
Objective: Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially for heterogeneous GC. We comprehensively compared 2D and 3D radiomic features' representation and discrimination capacity regarding GC, via three tasks.
Methods: Four-center 539 GC patients were retrospectively enrolled and divided into the training and validation cohorts. From 2D or 3D regions of interest (ROIs) annotated by radiologists, radiomic features were extracted respectively. Feature selection and model construction procedures were customed for each combination of two modalities (2D or 3D) and three tasks. Subsequently, six machine learning models (Model_2D^LNM, Model_3D^LNM; Model_2D^LVI, Model_3D^LVI; Model_2D^pT, Model_3D^pT) were derived and evaluated to reflect modalities' performances in characterizing GC. Furthermore, we performed an auxiliary experiment to assess modalities' performances when resampling spacing is different.
Results: Regarding three tasks, the yielded areas under the curve (AUCs) were: Model_2D^LNM's 0.712 (95% confidence interval, 0.613-0.811), Model_3D^LNM's 0.680 (0.584-0.775); Model_2D^LVI's 0.677 (0.595-0.761), Model_3D^LVI's 0.615 (0.528-0.703); Model_2D^pT's 0.840 (0.779-0.901), Model_3D^pT's 0.813 (0.747-0.879). Moreover, the auxiliary experiment indicated that Models_2D are statistically more advantageous than Models3D with different resampling spacings.
Conclusion: Models constructed with 2D radiomic features revealed comparable performances with those constructed with 3D features in characterizing GC.
Significance: Our work indicated that time-saving 2D annotation would be the better choice in GC, and provided a related reference to further radiomics-based researches.
School of Mathematics (Zhuhai), Sun Yat-sen University（中山大学（珠海）数学学院）
Rapid diagnosis of gastric cancer is a great challenge for clinical doctors. Dramatic progress of computer vision on gastric cancer has been made recently and this review focuses on advances during the past five years. Different methods for data generation and augmentation are presented, and various approaches to extract discriminative features compared and evaluated. Classification and segmentation techniques are carefully discussed for assisting more precise diagnosis and timely treatment. For classification, various methods have been developed to better proceed specific images, such as imag…
Northeastern University（东北大学）；Cancer Hospital, China Medical University（中国医科大学肿瘤医院）；Liaoning Cancer Hospital and Institute（辽宁省肿瘤医院和研究所）；Heilongjiang University（黑龙江大学）；Stevens Institute of Technology（斯蒂文斯理工学院）；Shengjing Hospital, China Medical University（中国医科大学盛京医院）；University of Luebeck（吕贝克大学）
Background and Objective: Gastric cancer has turned out to be the fifth most common cancer globally, and early detection of gastric cancer is essential to save lives. Histopathological examination of gastric cancer is the gold standard for the diagnosis of gastric cancer. However, computer-aided diagnostic techniques are challenging to evaluate due to the scarcity of publicly available gastric histopathology image datasets. Methods: In this paper, a noble publicly available Gastric Histopathology Sub-size Image Database (GasHisSDB) is published to identify classifiers' performance. Specificall…
Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Peking University（北京大学）；Hupan Lab（湖畔实验室）；Changsha Institute for Computing and Digital Economy（长沙计算与数字经济研究院）；Guangdong Province People’s Hospital（广东省人民医院）；The First Affiliated Hospital of Zhejiang University（浙江大学医学院附属第一医院）
University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）
Prostate cancer is the most diagnosed form of cancer in Canadian men, and is the third leading cause of cancer death. Despite these statistics, prognosis is relatively good with a sufficiently early diagnosis, making fast and reliable prostate cancer detection crucial. As imaging-based prostate cancer screening, such as magnetic resonance imaging (MRI), requires an experienced medical professional to extensively review the data and perform a diagnosis, radiomics-driven methods help streamline the process and has the potential to significantly improve diagnostic accuracy and efficiency, and thu…
Northeastern University（东北大学）；Cancer Hospital, China Medical University（中国医科大学肿瘤医院）；Liaoning Cancer Hospital and Institute（辽宁省肿瘤医院暨研究所）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）；University of Luebeck（吕贝克大学）
Gastric cancer is the fifth most common cancer in the world. At the same time, it is also the fourth most deadly cancer. Early detection of cancer exists as a guide for the treatment of gastric cancer. Nowadays, computer technology has advanced rapidly to assist physicians in the diagnosis of pathological pictures of gastric cancer. Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning. The complementarity of sub-size pathology image classifiers when machine performance is insufficient…
While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results in very good prognosis and as such makes early detection crucial. While radiomics have shown considerable promise as a powerful diagnostic tool for significantly improving oncological diagnostic accuracy and efficiency, current radiomics-driven methods have largely rely on pre-defined, hand-crafted quantitative features, which can greatly limit the ability to fully characterize unique cancer phenotype that distinguish…
Background and Objective: Non-invasively predicting the risk of cancer metastasis before surgery plays an essential role in determining optimal treatment methods for cancer patients (including who can benefit from neoadjuvant chemotherapy). Although developing radiomics based machine learning (ML) models has attracted broad research interest for this purpose, it often faces a challenge of how to build a highly performed and robust ML model using small and imbalanced image datasets. Methods: In this study, we explore a new approach to build an optimal ML model. A retrospective dataset involving…
University of Waterloo（滑铁卢大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）；University of Toronto（多伦多大学）
Radiomics has proven to be a powerful prognostic tool for cancer detection, and has previously been applied in lung, breast, prostate, and head-and-neck cancer studies with great success. However, these radiomics-driven methods rely on pre-defined, hand-crafted radiomic feature sets that can limit their ability to characterize unique cancer traits. In this study, we introduce a novel discovery radiomics framework where we directly discover custom radiomic features from the wealth of available medical imaging data. In particular, we leverage novel StochasticNet radiomic sequencers for extractin…
Sunnybrook Health Sciences Centre（森尼布鲁克健康科学中心）；University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）
Lung cancer is the leading cause for cancer related deaths. As such, there is an urgent need for a streamlined process that can allow radiologists to provide diagnosis with greater efficiency and accuracy. A powerful tool to do this is radiomics: a high-dimension imaging feature set. In this study, we take the idea of radiomics one step further by introducing the concept of discovery radiomics for lung cancer prediction using CT imaging data. In this study, we realize these custom radiomic sequencers as deep convolutional sequencers using a deep convolutional neural network learning architectu…
Concordia University（康考迪亚大学）；University of Toronto（多伦多大学）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）
Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of medical images through internal/external communication systems, have resulted in a recent surge of significant interest in "Radiomics". Radiomics is an emerging and relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic models, and is expected to become a critical component for integration of …
McGill University（麦吉尔大学）；McGill University Health Centre（麦吉尔大学健康中心）；Hôpital général juif（犹太综合医院）；Centre hospitalier universitaire de Sherbrooke（舍布鲁克大学医学中心）；Centre hospitalier de l’Université de Montréal（蒙特利尔大学医学中心）；Hôpital Maisonneuve-Rosemont（梅松纳夫-罗斯蒙特医院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Brigham and Women’s Hospital（布列根和妇女医院）；Harvard Medical School（哈佛医学院）；University of Michigan（密歇根大学）
Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and cli…
Maastricht University Medical Centre+（马斯特里赫特大学医学中心+）
Radiomics is an active area of research focusing on high throughput feature extraction from medical images with a wide array of applications in clinical practice, such as clinical decision support in oncology. However, noise in low dose computed tomography (CT) scans can impair the accurate extraction of radiomic features. In this article, we investigate the possibility of using deep learning generative models to improve the performance of radiomics from low dose CTs. We used two datasets of low dose CT scans -NSCLC Radiogenomics and LIDC-IDRI - as test datasets for two tasks - pre-treatment s…
Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection
Department of Software Engineering, Bahria University（巴尔ia大学软件工程系）；Global College of Engineering and Technology（全球工程与技术学院）；Software Engineering & Business Incubation Center, Bahria University（软件工程与企业孵化中心，巴尔ia大学）
The University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）
Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regions. Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on manual segmentation. Unfortunately, without segmentation masks, these methods will take the whole image as input, such that makes them difficult to foc…
The Chinese University of Hong Kong（香港中文大学）；the Sixth Affiliated Hospital of Sun Yat-sen University（中山大学附属第六医院）；Imsight Medical Technology Co., Ltd.（艾迈医疗科技有限公司）
The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis. However, due to the large scale of WSIs and various sizes of the abnormal area, how to select informative regions and analyze them are quite challenging during the automatic diagnosis process. The multi-instance learning based on the most discriminative instances can be of great benefit for whole slide gastric image diagnosis. In this paper, we design a recalibrated multi-instance deep learning method (RMDL) to address this challenging problem. We first select the discriminative instances, and then u…
An Integrated AI-Enabled System Using One Class Twin Cross Learning (OCT-X) for Early Gastric Cancer Detection
Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；Department of Biomedical Engineering, The Chinese University of Hong Kong（香港中文大学生物医学工程系）；Department of Neurology, David Geffen School of Medicine, University of California（加州大学洛杉矶分校神经医学系）；Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院）；Center for Brain-Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University（清华大学脑启发计算研究中心）；Department of Gastroenterology, Affiliated Hospital of Hebei University of Engineering（河北工程大学附属医院消化内科）；Institute of Artificial Intelligence, Chongqing Technology and Business University（重庆理工大学人工智能学院）；Cancer Centre, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau（澳门大学医学翻译中心癌症中心）；MoE Frontiers Science Center for Precision Oncology, University of Macau（澳门教育暨青年事务局精准肿瘤学前沿科学中心）；The director of the Institute of Clinical Medicine, The First People’s Hospital of Foshan（佛山第一人民医院临床医学研究所主任）；Hebei Key Laboratory of Medical Data Science, Institute of Biomedical Informatics, School of Medicine, Hebei University of Engineering（河北工程大学医学数据科学重点实验室）；The Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University（清华大学北京信息科学与技术国家研究中心）
University of British Columbia（不列颠哥伦比亚大学）；BC Cancer Research Institute（不列颠哥伦比亚癌症研究所）；Geneva University Hospital（日内瓦大学医院）
Radiomics features extract quantitative information from medical images, towards the derivation of biomarkers for clinical tasks, such as diagnosis, prognosis, or treatment response assessment. Different image discretization parameters (e.g. bin number or size), convolutional filters, segmentation perturbation, or multi-modality fusion levels can be used to generate radiomics features and ultimately signatures. Commonly, only one set of parameters is used; resulting in only one value or flavour for a given RF. We propose tensor radiomics (TR) where tensors of features calculated with multiple …
Indian Statistical Institute（印度统计学院）；University of South Florida（南佛罗里达大学）
Prediction of Overall Survival (OS) of brain cancer patients from multi-modal MRI is a challenging field of research. Most of the existing literature on survival prediction is based on Radiomic features, which does not consider either non-biological factors or the functional neurological status of the patient(s). Besides, the selection of an appropriate cut-off for survival and the presence of censored data create further problems. Application of deep learning models for OS prediction is also limited due to the lack of large annotated publicly available datasets. In this scenario we analyse th…
HWA-UNETR: Hierarchical Window Aggregate UNETR for 3D Multimodal Gastric Lesion Segmentation
School of Computer Science and Engineering（计算机科学与工程学院）；School of Future Technology（未来技术学院）；South China University of Technology（华南理工大学）；Department of Medical Imaging（医学影像科）；Guangzhou Institute of Cancer Research（广州癌症研究所）；the Affiliated Cancer Hospital（附属肿瘤医院）；Guangzhou Medical University（广州医学院）；Department of Radiology（放射科）；Meizhou People’s Hospital（梅州市人民医院）；School of Computer Science and Technology（计算机科学与技术学院）；Hainan University（海南大学）；Department of Oncology Institute（肿瘤研究所）
Case Western Reserve University（凯斯西储大学）；Picture Health（Picture Health公司）；Stony Brook University（石溪大学）；University Hospitals Cleveland Medical Center（克利夫兰大学医院医学中心）；Yale School of Medicine（耶鲁医学院）；The Cleveland Clinic Foundation (CCF)（克利夫兰诊所基金会（CCF））；New York University (NYU) Langone Medical Center（纽约大学（NYU）朗格尼医学中心）；Louis Stokes Cleveland Veterans Medical Center（路易斯·斯托克斯克利夫兰退伍军人医学中心）
Purpose: Tumor-associated vasculature differs from healthy blood vessels by its chaotic architecture and twistedness, which promotes treatment resistance. Measurable differences in these attributes may help stratify patients by likely benefit of systemic therapy (e.g. chemotherapy). In this work, we present a new category of radiomic biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancers, imaging modalities, and treatment regimens. Experimental Design: We segmented tumor vessels and …
Magnetic resonance delta radiomics to track radiation response in lung tumors receiving stereotactic MRI-guided radiotherapy
Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Maastricht University（马斯特里赫特大学）
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…
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）
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines
The Hospital for Sick Children (SickKids)（SickKids医院（病童医院））；SickKids Research Institute（SickKids研究所）；University of Toronto（多伦多大学）；University Hospital Augsburg（奥格斯堡大学医院）；Institute of Medical Science, University of Toronto（多伦多大学医学科学研究所）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Department of Mechanical and Industrial Engineering, University of Toronto（多伦多大学机械与工业工程系）；Vector Institute（向量研究院）
University of Toronto（多伦多大学）；Lunenfeld-Tanenbaum Research Institute（卢嫩菲尔德-塔南鲍姆研究所）；Sinai Health System（西奈健康系统）；McMaster University（麦克马斯特大学）；Hamilton Health Sciences（汉密尔顿健康科学）；Juravinski Hospital and Cancer Centre（朱拉文斯基医院及癌症中心）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited. Convolutional neural networks (CNNs) have been shown to outperform these feature-based models in computer vision tasks. However, training a CNN from scratch needs a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-b…
In recent years, deep learning has successfully been applied to automate a wide variety of tasks in diagnostic histopathology. However, fast and reliable localization of small-scale regions-of-interest (ROI) has remained a key challenge, as discriminative morphologic features often occupy only a small fraction of a gigapixel-scale whole-slide image (WSI). In this paper, we propose a sparse WSI analysis method for the rapid identification of high-power ROI for WSI-level classification. We develop an evaluation framework inspired by the early classification literature, in order to quantify the t…
Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations
Brigham and Women's Hospital（布莱根妇女医院）；Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；PixelMed Publishing（PixelMed出版公司）；Massachusetts General Hospital（麻省总医院）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Maastricht University（马斯特里赫特大学）
École de Technologie Supérieure, Université du Québec（魁北克大学高等技术学院）；Normandie Univ（诺曼底大学）；UNIROUEN（鲁昂大学）；UNIHAVRE（勒阿弗尔大学）；INSA Rouen（鲁昂国立应用科学学院）
Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients. Radiomics is a recent medical imaging field that has shown during the past few years to be promising for achieving this personalization. However, a recent study shows that most of the state-of-the-art works in Radiomics fail to identify this problem as a multi-view learning task and that multi-view learning techniques are generally more efficient. In this work, we propose to further investigate the potential of one famil…
Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment
KAIST（韩国科学技术院）；Chungnam National University School of Medicine（忠南大学医学院）；Chungnam National University Hospital（忠南大学医院）；Chungnam National University Sejong Hospital（忠南大学世宗医院）
本文提出基于两阶段混合 ViT 与多尺度自注意力的 AI 系统，实现胃癌病理五分类并匹配治疗指南，在多中心切片中验证可提升灵敏度并缩短筛查时间。
Shanghai General Hospital（上海市第一人民医院）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；SenseTime Research（商汤科技研究院）
Gastric cancer is one of the most common cancers, which ranks third among the leading causes of cancer death. Biopsy of gastric mucosa is a standard procedure in gastric cancer screening test. However, manual pathological inspection is labor-intensive and time-consuming. Besides, it is challenging for an automated algorithm to locate the small lesion regions in the gigapixel whole-slide image and make the decision correctly.To tackle these issues, we collected large-scale whole-slide image dataset with detailed lesion region annotation and designed a whole-slide image analyzing framework consi…
Technical University of Munich（慕尼黑工业大学）；University of Zurich（苏黎世大学）；Southern University of Science and Technology（南方科技大学）；ETH Zurich（苏黎世联邦理工学院）；Macau University of Science and Technology（澳门科技大学）；Klinikum rechts der Isar（伊萨尔河右岸医院）
Radiomic representations can quantify properties of regions of interest in medical image data. Classically, they account for pre-defined statistics of shape, texture, and other low-level image features. Alternatively, deep learning-based representations are derived from supervised learning but require expensive annotations from experts and often suffer from overfitting and data imbalance issues. In this work, we address the challenge of learning representations of 3D medical images for an effective quantification under data imbalance. We propose a \emph{self-supervised} representation learning…
Image-based brain cancer prediction models, based on radiomics, quantify the radiologic phenotype from magnetic resonance imaging (MRI). However, these features are difficult to reproduce because of variability in acquisition and preprocessing pipelines. Despite evidence of intra-tumor phenotypic heterogeneity, the spatial diversity between different slices within an MRI scan has been relatively unexplored using such methods. In this work, we propose a deep anchor attention aggregation strategy with a Vision Transformer to predict survival risk for brain cancer patients. A Deep Anchor Attentio…
Northeastern University（东北大学）；China Medical University（中国医科大学）；University of Luebeck（吕贝克大学）
The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. In this paper, based on the study of computer aided diagnosis system, graph based features are applied to gastric cancer histopathology microscopic image analysis, and a classifier is used to classify gastric cancer cells from benign cells. Firstly, image segmentation is performed, and after finding the region, cell nuclei are extracted using the k-means method, the minimum spanning tree (MST) is drawn, and graph …
University of Toronto（多伦多大学）；Sunnybrook Research Institute（森尼布鲁克研究所）
Cancer stage is a large determinant of patient prognosis and management in many cancer types, and is often assessed using medical imaging modalities, such as CT and MRI. These medical images contain rich information that can be explored to stratify patients within each stage group to further improve prognostic algorithms. Although the majority of cancer deaths result from metastatic and multifocal disease, building imaging biomarkers for patients with multiple tumors has been a challenging task due to the lack of annotated datasets and standard study framework. In this paper, we process two pu…
Beijing University of Posts and Telecommunications（北京邮电大学）；Center for Research on Intelligent Perception and Computing, NLPR, CASIA（中国科学院自动化研究所模式识别国家重点实验室）；Shandong University of Science and Technology（山东科技大学）；Artificial Intelligence Research, CAS (CAS-AIR)（中国科学院人工智能研究院）
In recent years, deep convolutional neural networks have made significant advances in pathology image segmentation. However, pathology image segmentation encounters with a dilemma in which the higher-performance networks generally require more computational resources and storage. This phenomenon limits the employment of high-accuracy networks in real scenes due to the inherent high-resolution of pathological images. To tackle this problem, we propose CoCo DistillNet, a novel Cross-layer Correlation (CoCo) knowledge distillation network for pathological gastric cancer segmentation. Knowledge di…
Wuhan Children’s Hospital affiliated with Tongji Medical College（同济医学院附属武汉儿童医院）；Huazhong University of Science and Technology（华中科技大学）
Neuroblastoma is one of the most common cancers in infants, and the initial diagnosis of this disease is difficult. At present, the MYCN gene amplification (MNA) status is detected by invasive pathological examination of tumor samples. This is time-consuming and may have a hidden impact on children. To handle this problem, we adopt multiple machine learning (ML) algorithms to predict the presence or absence of MYCN gene amplification. The dataset is composed of retrospective CT images of 23 neuroblastoma patients. Different from previous work, we develop the algorithm without manually-segmente…
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric Cancer
The Hong Kong University of Science and Technology（香港科技大学）；The Sixth Affiliated Hospital of Sun Yat-sen University（中山大学附属第六医院）；The First Affiliated Hospital of Kunming Medical University（昆明医科大学第一附属医院）；Sichuan Province Cancer Hospital, University of Electronic Science and Technology of China（电子科技大学四川省肿瘤医院）；The Chinese University of Hong Kong（香港中文大学）
Microsatellite instability (MSI) is a tumor phenotype whose diagnosis largely impacts patient care in colorectal cancers (CRC), and is associated with response to immunotherapy in all solid tumors. Deep learning models detecting MSI tumors directly from H&E stained slides have shown promise in improving diagnosis of MSI patients. Prior deep learning models for MSI detection have relied on neural networks pretrained on ImageNet dataset, which does not contain any medical image. In this study, we leverage recent advances in self-supervised learning by training neural networks on histology images…
Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices
Division AI4Health, Carl von Ossietzky Universität Oldenburg（奥尔登堡卡尔·冯·奥西茨基大学AI4Health部门）；University Institute for Diagnostic and Interventional Radiology, Klinikum Oldenburg AöR（奥尔登堡医院大学诊断与介入放射学研究所）
Gastric diffuse-type adenocarcinoma represents a disproportionately high percentage of cases of gastric cancers occurring in the young, and its relative incidence seems to be on the rise. Usually it affects the body of the stomach, and presents shorter duration and worse prognosis compared with the differentiated (intestinal) type adenocarcinoma. The main difficulty encountered in the differential diagnosis of gastric adenocarcinomas occurs with the diffuse-type. As the cancer cells of diffuse-type adenocarcinoma are often single and inconspicuous in a background desmoplaia and inflammation, i…
We proposed a novel convolutional restricted Boltzmann machine CRBM-based radiomic method for predicting pathologic complete response (pCR) to neoadjuvant chemotherapy treatment (NACT) in breast cancer. The method consists of extracting semantic features from CRBM network, and pCR prediction. It was evaluated on the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of 57 patients and using the area under the receiver operating characteristic curve (AUC). Traditional radiomics features and the semantic features learned from CRBM network were extracted from the images acquired …
PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT and Radiology Report Dataset for Medical Imaging Research
PET Center, Huashan Hospital, Fudan University（复旦大学附属华山医院PET中心）；Shanghai Academy of Artificial Intelligence for Science（上海科学智能研究院）；Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；Shanghai Universal Medical Imaging Diagnostic Center（上海环球医学影像诊断中心）；Human Phenome Institute, Fudan University（复旦大学人类表型组研究院）；School of Data Science, Fudan University（复旦大学数据科学学院）
ICTEAM（ICTEAM研究所）；Université catholique de Louvain（鲁汶天主教大学）；Indian Institute of Technology Ropar（印度理工学院罗巴尔分校）
The importance of radiomics features for predicting patient outcome is now well-established. Early study of prognostic features can lead to a more efficient treatment personalisation. For this reason new radiomics features obtained through mathematical morphology-based operations are proposed. Their study is conducted on an open database of patients suffering from Nonsmall Cells Lung Carcinoma (NSCLC). The tumor features are extracted from the CT images and analyzed via PCA and a Kaplan-Meier survival analysis in order to select the most relevant ones. Among the 1,589 studied features, 32 are …
Glioblastoma, an aggressive brain cancer, is amongst the most lethal of all cancers. Expression of the O6-methylguanine-DNA-methyltransferase (MGMT) gene in glioblastoma tumor tissue is of clinical importance as it has a significant effect on the efficacy of Temozolomide, the primary chemotherapy treatment administered to glioblastoma patients. Currently, MGMT methylation is determined through an invasive brain biopsy and subsequent genetic analysis of the extracted tumor tissue. In this work, we present novel Bayesian classifiers that make probabilistic predictions of MGMT methylation status …
Herein we propose a deep learning-based approach for the prediction of lung lesion response based on radiomic features extracted from clinical CT scans of patients in non-small cell lung cancer trials. The approach starts with the classification of lung lesions from the set of primary and metastatic lesions at various anatomic locations. Focusing on the lung lesions, we perform automatic segmentation to extract their 3D volumes. Radiomic features are then extracted from the lesion on the pre-treatment scan and the first follow-up scan to predict which lesions will shrink at least 30% in diamet…
