Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer
Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer
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DOI：
10.1055/s-0043-122385
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发表时间：
2018-03-01
期刊：
影响因子：
9.3
通讯作者：
Miyachi, Hideyuki
中科院分区：
文献类型：
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作者：
Ichimasa, Katsuro;Kudo, Shin-ei;Miyachi, Hideyuki
背景和研究目的T1期结直肠癌（CRC）内镜切除术后决定是否进行额外手术是困难的，因为术前预测淋巴结转移（LNM）是有问题的。我们调查了人工智能是否可以预测LNM的存在，从而最大限度地减少对额外surgery.Patients and methods的需要，对2001 - 2016年手术切除的690例连续T1 CRC患者的数据进行了回顾性分析。我们根据日期将患者分为两组：590名患者的数据用于人工智能模型的机器学习，其余100名患者用于模型验证。人工智能模型分析了45个临床病理因素，然后预测LNM的阳性或阴性。手术标本被用作LNM存在的金标准。人工智能模型通过计算预测LNM的敏感性、特异性和准确性进行验证，并将这些数据与美国、欧洲和日本的指南进行比较。结果所有模型的敏感性均为100%（95%置信区间[CI] 72%~ 100%）。人工智能模型与美国、欧洲和日本指南的特异性为66%（95%CI 56%至76%），44%（95% CI 34%至55%），0%（95%CI 0%至3%），和0%（95% CI 0%-3%）;准确性分别为69%（95% CI 59%-78%）、49%（95% CI 39%-59%）、9%（95% CI 4%-16%）和9%（95% CI 4%-16%）。由于误诊为LNM阴性患者而导致不必要的额外手术的发生率为：77%（95% CI 62%至89%），人工智能模型为85%。（95% CI 73%至93%; P < 0.001），91%（95% CI 84%至96%; P < 0.001）和91%（95%CI 84%至96%; P < 0.001）。结论与现行指南相比，人工智能显著减少了T1结直肠癌内镜切除术后不必要的额外手术，而不会丢失LNM阳性。
Background and study aims Decisions concerning additional surgery after endoscopic resection of T1 colorectal cancer (CRC) are difficult because preoperative prediction of lymph node metastasis (LNM) is problematic. We investigated whether artificial intelligence can predict LNM presence, thus minimizing the need for additional surgery.Patients and methods Data on 690 consecutive patients with T1 CRCs that were surgically resected in 2001 - 2016 were retrospectively analyzed. We divided patients into two groups according to date: data from 590 patients were used for machine learning for the artificial intelligence model, and the remaining 100 patients were included for model validation. The artificial intelligence model analyzed 45 clinicopathological factors and then predicted positivity or negativity for LNM. Operative specimens were used as the gold standard for the presence of LNM. The artificial intelligence model was validated by calculating the sensitivity, specificity, and accuracy for predicting LNM, and comparing these data with those of the American, European, and Japanese guidelines.Results Sensitivity was 100% (95% confidence interval [CI] 72% to 100 %) in all models. Specificity of the artificial intelligence model and the American, European, and Japanese guidelines was 66% (95%CI 56% to 76%), 44% (95 %CI 34% to 55%), 0% (95%CI 0% to 3%), and 0% (95 %CI 0% to 3%), respectively; and accuracy was 69% (95 %CI 59% to 78%), 49% (95 %CI 39% to 59 %), 9% (95%CI 4% to 16%), and 9% (95 %CI 4%-16 %), respectively. The rates of unnecessary additional surgery attributable to misdiagnosing LNM-negative patients as having LNM were: 77% (95 %CI 62% to 89 %) for the artificial intelligence model, and 85% (95 %CI 73% to 93%; P < 0.001), 91% (95 %CI 84% to 96%; P < 0.001), and 91% (95 %CI 84% to 96 %; P < 0.001) for the American, European, and Japanese guidelines, respectively.Conclusions Compared with current guidelines, artificial intelligence significantly reduced unnecessary additional surgery after endoscopic resection of T1 CRC without missing LNM positivity.