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PAIP 2020: Microsatellite instability prediction in colorectal cancer

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资源类型：
Pubmed体系：
作者：
Kim Kyungmo[1] ; Lee Kyoungbun[2] ; Cho Sungduk[3] ; Kang Dong Un[4] ; Park Seongkeun[5] ; Kang Yunsook[5] ; Kim Hyunjeong[5] ; Choe Gheeyoung[6] ; Moon Kyung Chul[7] ; Lee Kyu Sang[6] ; Park Jeong Hwan[8] ; Hong Choyeon[2] ; Nateghi Ramin[9] ; Pourakpour Fattaneh[10] ; Wang Xiyue[11] ; Yang Sen[12] ; Jahromi Seyed Alireza Fatemi[13] ; Khani Aliasghar[13] ; Kim Hwa-Rang[14] ; Choi Doo-Hyun[14] ; Han Chang Hee[15] ; Kwak Jin Tae[16] ; Zhang Fan[17] ; Han Bing[17] ; Ho David Joon[18] ; Kang Gyeong Hoon[19,*1] ; Chun Se Young[20] ; Jeong Won-Ki[21] ; Park Peom[22] ; Choi Jinwook[23] ;
机构： [1]Interdisciplinary program in Bioengineering, Seoul National University, Seoul 110-799, Republic of Korea. [2]Department of Pathology, Seoul National University Hospital, Seoul, Republic of Korea. [3]Korea University, College of Informatics, Department of Computer Science and Engineering, Seoul, Republic of Korea. [4]Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea. [5]Department of Biomedical Engineering, Seoul National University Hospital, Seoul, Republic of Korea. [6]Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea, Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea. [7]Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea. [8]Department of Pathology, Seoul National University College of Medicine, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea. [9]Electrical and Electronics Engineering Department, Shiraz University of Technology, Shiraz, Iran. [10]Iranian Brain Mapping Biobank, National Brain Mapping Laboratory, Tehran, Iran. [11]College of Computer Science, Sichuan University, China. [12]College of Biomedical Engineering, Sichuan University, China, Tencent AI Lab, Shenzhen, China. [13]Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. [14]Graduate School of Electronic and Electrical Engineering, Kyungpook National University, Republic of Korea. [15]Department of Computer Science and Engineering, Sejong University, Seoul, Republic of Korea. [16]School of Electrical Engineering, Korea University, Seoul, Republic of Korea. [17]Research and Development Center, Canon Medical Systems (China) Co., Ltd, Beijing, China. [18]Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. [19]Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea, Laboratory of Epigenetics, Cancer Research Institute, Seoul National University College of Medicine, Republic of Korea. [20]Department of Electrical and Computer Engineering, INMC, Seoul National University, Seoul, Republic of Korea. [21]Korea University, College of Informatics, Department of Computer Science and Engineering, Seoul, Republic of Korea. [22]HuminTec, Suwon, Republic of Korea. [23]Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea.
出处：
DOI： 10.1016/j.media.2023.102886
ISSN：

关键词： Colon cancer MSI Digital pathology Segmentation

摘要：
Microsatellite instability (MSI) refers to alterations in the length of simple repetitive genomic sequences. MSI status serves as a prognostic and predictive factor in colorectal cancer. The MSI-high status is a good prognostic factor in stage II/III cancer, and predicts a lack of benefit to adjuvant fluorouracil chemotherapy in stage II cancer but a good response to immunotherapy in stage IV cancer. Therefore, determining MSI status in patients with colorectal cancer is important for identifying the appropriate treatment protocol. In the Pathology Artificial Intelligence Platform (PAIP) 2020 challenge, artificial intelligence researchers were invited to predict MSI status based on colorectal cancer slide images. Participants were required to perform two tasks. The primary task was to classify a given slide image as belonging to either the MSI-high or the microsatellite-stable group. The second task was tumor area segmentation to avoid ties with the main task. A total of 210 of the 495 participants enrolled in the challenge downloaded the images, and 23 teams submitted their final results. Seven teams from the top 10 participants agreed to disclose their algorithms, most of which were convolutional neural network-based deep learning models, such as EfficientNet and UNet. The top-ranked system achieved the highest F1 score (0.9231). This paper summarizes the various methods used in the PAIP 2020 challenge. This paper supports the effectiveness of digital pathology for identifying the relationship between colorectal cancer and the MSI characteristics.Copyright © 2023. Published by Elsevier B.V.

基金：
This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number HI18C0316).
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2023]版：
大类 | 1 区 医学
小类 | 1 区 计算机：人工智能 1 区 计算机：跨学科应用 1 区 工程：生物医学 1 区 核医学
最新[2023]版：
大类 | 1 区 医学
小类 | 1 区 计算机：人工智能 1 区 计算机：跨学科应用 1 区 工程：生物医学 1 区 核医学
第一作者：
第一作者机构： [1]Interdisciplinary program in Bioengineering, Seoul National University, Seoul 110-799, Republic of Korea.
共同第一作者：
通讯作者：
通讯机构： [19]Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea, Laboratory of Epigenetics, Cancer Research Institute, Seoul National University College of Medicine, Republic of Korea. [*1]103 Daehak-ro, Jongno-gu, Seoul, 03080, South KOREA.
推荐引用方式(GB/T 7714)：
Kim Kyungmo,Lee Kyoungbun,Cho Sungduk,et al.PAIP 2020: Microsatellite instability prediction in colorectal cancer[J].Medical image analysis.2023,89:102886.doi:10.1016/j.media.2023.102886.
APA：
Kim Kyungmo,Lee Kyoungbun,Cho Sungduk,Kang Dong Un,Park Seongkeun...&Choi Jinwook.(2023).PAIP 2020: Microsatellite instability prediction in colorectal cancer.Medical image analysis,89,
MLA：
Kim Kyungmo,et al."PAIP 2020: Microsatellite instability prediction in colorectal cancer".Medical image analysis 89.(2023):102886

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