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Novel “resect and analysis” approach for T2 colorectal cancer with use of artificial intelligence

医学 接收机工作特性 结直肠癌 癌胚抗原 置信区间 阶段(地层学) 结肠切除术 淋巴结 淋巴血管侵犯 癌症 外科 放射科 转移 内科学 古生物学 生物
作者
Katsuro Ichimasa,Kenta Nakahara,Shin‐ei Kudo,Masashi Misawa,Michael Bretthauer,Shoji Shimada,Yusuke Takehara,Shunpei Mukai,Yuta Kouyama,Hideyuki Miyachi,Naruhiko Sawada,Kensaku Mori,Fumio Ishida,Yuichi Mori
出处
期刊:Gastrointestinal Endoscopy [Elsevier BV]
卷期号:96 (4): 665-672.e1 被引量:25
标识
DOI:10.1016/j.gie.2022.04.1305
摘要

Because of a lack of reliable preoperative prediction of lymph node involvement in early-stage T2 colorectal cancer (CRC), surgical resection is the current standard treatment. This leads to overtreatment because only 25% of T2 CRC patients turn out to have lymph node metastasis (LNM). We assessed a novel artificial intelligence (AI) system to predict LNM in T2 CRC to ascertain patients who can be safely treated with less-invasive endoscopic resection such as endoscopic full-thickness resection and do not need surgery.We included 511 consecutive patients who had surgical resection with T2 CRC from 2001 to 2016; 411 patients (2001-2014) were used as a training set for the random forest-based AI prediction tool, and 100 patients (2014-2016) were used to validate the AI tool performance. The AI algorithm included 8 clinicopathologic variables (patient age and sex, tumor size and location, lymphatic invasion, vascular invasion, histologic differentiation, and serum carcinoembryonic antigen level) and predicted the likelihood of LNM by receiver-operating characteristics using area under the curve (AUC) estimates.Rates of LNM in the training and validation datasets were 26% (106/411) and 28% (28/100), respectively. The AUC of the AI algorithm for the validation cohort was .93. With 96% sensitivity (95% confidence interval, 90%-99%), specificity was 88% (95% confidence interval, 80%-94%). In this case, 64% of patients could avoid surgery, whereas 1.6% of patients with LNM would lose a chance to receive surgery.Our proposed AI prediction model has a potential to reduce unnecessary surgery for patients with T2 CRC with very little risk. (Clinical trial registration number: UMIN 000038257.).
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