Utility of artificial intelligence with deep learning of hematoxylin and eosin-stained whole slide images to predict lymph node metastasis in T1 colorectal cancer using endoscopically resected specimens; prediction of lymph node metastasis in T1 colorectal cancer.

Song, Joo Hye; Hong, Yiyu; Kim, Eun Ran; Kim, Seok-Hyung; Sohn, Insuk

Song, Joo Hye; Hong, Yiyu; Kim, Eun Ran; Kim, Seok-Hyung; Sohn, Insuk.

Affiliation

Song JH; Department of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
Hong Y; Department of R&D Center, Arontier Co., Ltd, Seoul, Republic of Korea.
Kim ER; Department of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. er.kim@samsung.com.
Kim SH; Department of Pathology, Samsung Medical Center, Seoul, Republic of Korea.
Sohn I; Department of R&D Center, Arontier Co., Ltd, Seoul, Republic of Korea.

J Gastroenterol ; 57(9): 654-666, 2022 09.

Article in En | MEDLINE | ID: mdl-35802259

Subject(s)

Colorectal Neoplasms; Deep Learning; Artificial Intelligence; Colorectal Neoplasms/pathology; Colorectal Neoplasms/surgery; Eosine Yellowish-(YS); Hematoxylin; Humans; Lymph Nodes/pathology; Lymphatic Metastasis/pathology; Retrospective Studies; Risk Factors

Key words

Artificial intelligence; Lymph node metastasis; Prediction; T1 colorectal cancer; Whole slide image

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Colorectal Neoplasms / Deep Learning Type of study: Etiology_studies / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: J Gastroenterol Journal subject: GASTROENTEROLOGIA Year: 2022 Document type: Article

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