Shin-Ei Kudo1, Katsuro Ichimasa2, Benjamin Villard3, Yuichi Mori4, Masashi Misawa2, Shoichi Saito5, Kinichi Hotta6, Yutaka Saito7, Takahisa Matsuda8, Kazutaka Yamada9, Toshifumi Mitani10, Kazuo Ohtsuka11, Akiko Chino5, Daisuke Ide5, Kenichiro Imai6, Yoshihiro Kishida6, Keiko Nakamura8, Yasumitsu Saiki9, Masafumi Tanaka9, Shu Hoteya10, Satoshi Yamashita10, Yusuke Kinugasa12, Masayoshi Fukuda11, Toyoki Kudo2, Hideyuki Miyachi2, Fumio Ishida2, Hayato Itoh3, Masahiro Oda3, Kensaku Mori3. 1. Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan. Electronic address: kudos@med.showa-u.ac.jp. 2. Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan. 3. Graduate School of Informatics, Nagoya University, Nagoya, Japan. 4. Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan; Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway. 5. Department of Gastroenterology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan. 6. Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan. 7. Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan. 8. Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan; Cancer Screening Center, National Cancer Center Hospital, Tokyo, Japan. 9. Department of Surgery, Coloproctology Center Takano Hospital, Kumamoto, Japan. 10. Department of Gastroenterology, Toranomon Hospital, Tokyo, Japan. 11. Department of Endoscopy, Tokyo Medical and Dental University, Tokyo, Japan. 12. Department of Gastrointestinal Surgery, Tokyo Medical and Dental University, Tokyo, Japan.
Abstract
BACKGROUND & AIMS: In accordance with guidelines, most patients with T1 colorectal cancers (CRC) undergo surgical resection with lymph node dissection, despite the low incidence (∼10%) of metastasis to lymph nodes. To reduce unnecessary surgical resections, we used artificial intelligence to build a model to identify T1 colorectal tumors at risk for metastasis to lymph node and validated the model in a separate set of patients. METHODS: We collected data from 3134 patients with T1 CRC treated at 6 hospitals in Japan from April 1997 through September 2017 (training cohort). We developed a machine-learning artificial neural network (ANN) using data on patients' age and sex, as well as tumor size, location, morphology, lymphatic and vascular invasion, and histologic grade. We then conducted the external validation on the ANN model using independent 939 patients at another hospital during the same period (validation cohort). We calculated areas under the receiver operator characteristics curves (AUCs) for the ability of the model and US guidelines to identify patients with lymph node metastases. RESULTS: Lymph node metastases were found in 319 (10.2%) of 3134 patients in the training cohort and 79 (8.4%) of /939 patients in the validation cohort. In the validation cohort, the ANN model identified patients with lymph node metastases with an AUC of 0.83, whereas the guidelines identified patients with lymph node metastases with an AUC of 0.73 (P < .001). When the analysis was limited to patients with initial endoscopic resection (n = 517), the ANN model identified patients with lymph node metastases with an AUC of 0.84 and the guidelines identified these patients with an AUC of 0.77 (P = .005). CONCLUSIONS: The ANN model outperformed guidelines in identifying patients with T1 CRCs who had lymph node metastases. This model might be used to determine which patients require additional surgery after endoscopic resection of T1 CRCs. UMIN Clinical Trials Registry no: UMIN000038609.
BACKGROUND & AIMS: In accordance with guidelines, most patients with T1 colorectal cancers (CRC) undergo surgical resection with lymph node dissection, despite the low incidence (∼10%) of metastasis to lymph nodes. To reduce unnecessary surgical resections, we used artificial intelligence to build a model to identify T1 colorectal tumors at risk for metastasis to lymph node and validated the model in a separate set of patients. METHODS: We collected data from 3134 patients with T1 CRC treated at 6 hospitals in Japan from April 1997 through September 2017 (training cohort). We developed a machine-learning artificial neural network (ANN) using data on patients' age and sex, as well as tumor size, location, morphology, lymphatic and vascular invasion, and histologic grade. We then conducted the external validation on the ANN model using independent 939 patients at another hospital during the same period (validation cohort). We calculated areas under the receiver operator characteristics curves (AUCs) for the ability of the model and US guidelines to identify patients with lymph node metastases. RESULTS: Lymph node metastases were found in 319 (10.2%) of 3134 patients in the training cohort and 79 (8.4%) of /939 patients in the validation cohort. In the validation cohort, the ANN model identified patients with lymph node metastases with an AUC of 0.83, whereas the guidelines identified patients with lymph node metastases with an AUC of 0.73 (P < .001). When the analysis was limited to patients with initial endoscopic resection (n = 517), the ANN model identified patients with lymph node metastases with an AUC of 0.84 and the guidelines identified these patients with an AUC of 0.77 (P = .005). CONCLUSIONS: The ANN model outperformed guidelines in identifying patients with T1 CRCs who had lymph node metastases. This model might be used to determine which patients require additional surgery after endoscopic resection of T1 CRCs. UMIN Clinical Trials Registry no: UMIN000038609.
Authors: M Wagner; A Schulze; S Bodenstedt; L Maier-Hein; S Speidel; F Nickel; F Berlth; B P Müller-Stich; Peter Grimminger Journal: Chirurg Date: 2022-01-24 Impact factor: 0.955
Authors: Ji Hyun Ahn; Min Seob Kwak; Hun Hee Lee; Jae Myung Cha; Hyun Phil Shin; Jung Won Jeon; Jin Young Yoon Journal: Front Oncol Date: 2021-03-25 Impact factor: 6.244