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Inside Cover: A novel in vivo Cerenkov luminescence image-guided surgery on primary and metastatic colorectal cancer (J. Biophotonics 3/2020)

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Journal of Biophotonics 2020年第3期13卷

作者： Zeyu Zhang Yawei Qu Yu Cao Xiaojing Shi Hongbo Guo Xiaojun Zhang Sheng Zheng Haifeng Liu Zhenhua Hu Jie Tian Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Gastroenterology the Third Medical Centre Chinese PLA General Hospital Beijing China Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Anorectal the Third medical Centre Chinese PLA General Hospital Beijing China University of Chinese Academy of Sciences Beijing China School of Information Sciences and Technology Northwest University Xi'an China Department of Nuclear Medicine Chinese PLA General Hospital Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine Beihang University Beijing China

This study presents a novel intraoperative in vivo imaging approach which harnessed Cerenkov luminescence (CL) to detect primary and metastatic colorectal cancer (CRC) using clinically approved radiopharmaceuticals. In the mice and swine experiments, the proposed approach effectively improved the effect of CRC surgery. The approach is believed to be promising for utilizing CL in open surgery. Further details can be found in the article by Zeyu Zhang, Yawei Qu, Yu Cao et al. ( e201960152 )

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Retraction Note: Evaluation on diabetic plantar pressure data-set employing auto-segmentation technologies

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Neural Computing and Applications 2024年第24期36卷 15219-15219页

作者： Chen, Hongwei Cao, Luying Li, Zairan Hemanth, D. Jude Wu, Lijun de Albuquerque, Victor Hugo C. Shi, Fuqian Department of Orthopedics Yiwu Central Hospital Wenzhou Medical University Wenzhou People’s Republic of China Institute of Digitized Medicine Wenzhou Medical University Wenzhou People’s Republic of China Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University Tianjin People’s Republic of China Department of ECE Karunya University Coimbatore India Programa de Pós-Graduação Em Informática Aplicada Universidade de Fortaleza Fortaleza Brazil

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Phototherapy: Metal–Organic‐Framework‐Derived Mesoporous Carbon Nanospheres Containing Porphyrin‐Like Metal Centers for Conformal Phototherapy (Adv. Mater. 38/2016)

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Advanced Materials 2016年第38期28卷

作者： Shunhao Wang Lu Shang Linlin Li Yingjie Yu Chongwei Chi Kun Wang Jie Zhang Run Shi Heyun Shen Geoffrey I. N. Waterhouse Sijin Liu Jie Tian Tierui Zhang Huiyu Liu Beijing Key Laboratory of Bioprocess Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing Laboratory of Biomedical Materials Beijing University of Chemical Technology Beijing 100029 P. R. China Key Laboratory of Photochemical Conversion and Optoelectronic Materials Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing 100190 P. R. China State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco‐Environmental Sciences Chinese Academy of Sciences Beijing 100085 P. R. China University of Chinese Academy of Sciences Beijing 100049 P. R. China Beijing Institute of Nanoenergy and Nanosystems National Center for Nanoscience and Technology (NCNST) Chinese Academy of Sciences Beijing 100083 P. R. China Department of Materials Science and Engineering State University of New York at Stony Brook Stony Brook NY 11790 USA Key Laboratory of Molecular Imaging The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 P. R. China School of Chemical Sciences The University of Auckland Auckland 1142 New Zealand

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Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease

arXiv

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arXiv 2023年

作者： Wang, Lan He, Ruiling Zhao, Lili Wang, Jia Geng, Zhengzi Ren, Tao Zhang, Guo Zhang, Peng Tang, Kaiqiang Gao, Chaofei Chen, Fei Zhang, Liting Zhou, Yonghe Li, Xin He, Fanbin Huan, Hui Wang, Wenjuan Liang, Yunxiao Tang, Juan Ai, Fang Wang, Tingyu Zheng, Liyun Zhao, Zhongwei Ji, Jiansong Liu, Wei Xu, Jiaojiao Liu, Bo Wang, Xuemei Zhang, Yao Yan, Qiong Lv, Muhan Chen, Xiaomei Zhang, Shuhua Wang, Yihua Liu, Yang Yin, Li Liu, Yanni Huang, Yanqing Liu, Yunfang Wang, Kun Su, Meiqin Bian, Li An, Ping Zhang, Xin Qian, Linxue Li, Shao Qi, Xiaolong Institute for TCM-X MOE Key Laboratory of Bioinformatics Bioinformatics Division BNRIST Department of Automation Tsinghua University Beijing China Department of Ultrasound Donggang Branch the First Hospital of Lanzhou University Lanzhou China The First Clinical Medical College of Lanzhou University Lanzhou China Department of Gastroenterology and Hepatology Tianjin Second People's Hospital Tianjin China Department of Ultrasound Beijing Friendship Hospital Capital Medical University Beijing China Department of Ultrasound The People's Hospital of Linxia Linxia China Department of Gastroenterology Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region Sichuan China Department of Gastroenterology The People's Hospital of Guangxi Zhuang Autonomous Region Nanning China Department of Control and Systems Engineering Nanjing University Nanjing China Department of Ultrasound The First Hospital of Lanzhou University Lanzhou China Department of Infectious Diseases The First Hospital of Lanzhou University Lanzhou China Department of Ultrasonography Tianjin Second People's Hospital Tianjin China Department of Infectious Disease Zigong First People's Hospital Zigong China Department of Ultrasound Zigong First People's Hospital Zigong China Department of Gastroscopy Zigong First People's Hospital Zigong China Department of Radiology Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research Fifth Affiliated Hospital of Wenzhou Medical University Lishui China Ultrasound Diagnosis Center Shaanxi Provincial People's Hospital Xi'An China Department of Ultrasound Ditan Hospital Capital Medical University Beijing China Department of Gastroenterology The Affiliated Hospital of Southwest Medical University Luzhou China Department of Ultrasound The Affiliated Hospital of Southwest Medical University Luzhou China Department of Ultrasound North China University of Science and Technology Affiliated Hospital Tangshan China Depar

Objective: Bleeding from gastroesophageal varices (GEV) is a medical emergency associated with high mortality. We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography (2D-SWE) of the liver and spleen to precisely assess the risk of GEV and high-risk gastroesophageal varices (HRV). Design: A prospective multicenter study was conducted in patients with compensated advanced chronic liver disease. 305 patients were enrolled from 12 hospitals, and finally 265 patients were included, with 1136 liver stiffness measurement (LSM) images and 1042 spleen stiffness measurement (SSM) images generated by 2D-SWE. We leveraged deep learning methods to uncover associations between image features and patient risk, and thus conducted models to predict GEV and HRV. Results: A multi-modality Deep Learning Risk Prediction model (DLRP) was constructed to assess GEV and HRV, based on LSM and SSM images, and clinical information. Validation analysis revealed that the AUCs of DLRP were 0.91 for GEV (95% CI 0.90 to 0.93, p Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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