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イタバシ ミチオ
Itabashi Michio
板橋 道朗 所属 医学部 医学科（東京女子医科大学病院） 職種 特任教授 |
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| 論文種別 | 原著 |
| 言語種別 | 英語 |
| 査読の有無 | 査読なし |
| 表題 | Development of a Diagnostic Artificial Intelligence Tool for Lateral Lymph Node Metastasis in Advanced Rectal Cancer |
| 掲載誌名 | 正式名：Diseases of the colon and rectum 略 称：Dis Colon Rectum ISSNコード：15300358/00123706 |
| 掲載区分 | 国外 |
| 巻・号・頁 | 66(12),pp.e1246-e1253 |
| 著者・共著者 | OZAKI Kosuke,KUROSE Yusuke,KAWAI Kazushige,KOBAYASHI Hirotoshi,ITABASHI Michio,HASHIGUCHI Yojiro,MIURA Takuya,SHIOMI Akio,HARADA Tatsuya,AJIOKA Yoichi |
| 発行年月 | 2023/12 |
| 概要 | BACKGROUND:Metastatic lateral lymph node dissection can improve survival in patients with rectal adenocarcinoma, with or without chemoradiotherapy. However, the optimal imaging diagnostic criteria for lateral lymph node metastases remain undetermined.OBJECTIVE:To develop a lateral lymph node metastasis diagnostic artificial intelligence tool using deep learning, for patients with rectal adenocarcinoma who underwent radical surgery and lateral lymph node dissection.DESIGN:Retrospective study.SETTINGS:Multicenter study.PATIENTS:A total of 209 patients with rectal adenocarcinoma, who underwent radical surgery and lateral lymph node dissection at 15 participating hospitals, were enrolled in the study and allocated to training (n = 139), test (n = 17), or validation (n = 53) cohorts.MAIN OUTCOME MEASURES:In the neoadjuvant treatment group, images taken before pretreatment were classified as baseline images and those taken after pretreatment as presurgery images. In the upfront surgery group, presurgery images were classified as both baseline and presurgery images. We constructed 2 types of artificial intelligence, using baseline and presurgery images, by inputting the patches from these images into ResNet-18, and we assessed their diagnostic accuracy.RESULTS:Overall, 124 patients underwent surgery alone, 52 received neoadjuvant chemotherapy, and 33 received chemoradiotherapy. The number of resected lateral lymph nodes in the training, test, and validation cohorts was 2418, 279, and 850, respectively. The metastatic rates were 2.8%, 0.7%, and 3.7%, respectively. In the validation cohort, the precision-recall area under the curve was 0.870 and 0.963 for the baseline and presurgery images, respectively. Although both baseline and presurgery images provided good accuracy for diagnosing lateral lymph node metastases, the accuracy of presurgery images was better than that of baseline images.LIMITATIONS:The number of cases is small.CONCLUSIONS:An artificial intelligence tool is |
| DOI | 10.1097/DCR.0000000000002719 |
| PMID | 37260284 |