Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer (vol 50, pg 230, 2017)

被引:124
作者
Ichimasa, Katsuro
Kudo, Shin-ei
Mori, Yuichi
Misawa, Masashi
Matsudaira, Shingo
Kouyama, Yuta
Baba, Toshiyuki
Hidaka, Eiji
Wakamura, Kunihiko
Hayashi, Takemasa
Kudo, Toyoki
Ishigaki, Tomoyuki
Yagawa, Yusuke
Nakamura, Hiroki
Takeda, Kenichi
Haji, Amyn
Hamatani, Shigeharu
Mori, Kensaku
Ishida, Fumio
Miyachi, Hideyuki
机构
[1] Digestive Disease Center, Showa University, Northern Yokohama Hospital, 35-1 Chigasaki-chuo, Tsuzuki, Yokohama
[2] King's Institute of Therapeutic Endoscopy, King's College Hospital, London
[3] Department of Pathology, Jikei University, School of Medicine, Tokyo
[4] Information and Communications, Nagoya University, Nagoya
[5] Miyachi Clinic, Kakogawa
基金
日本学术振兴会;
关键词
D O I
10.1055/s-0043-122385
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
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.
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页码:C2 / C2
页数:1
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  • [1] Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer (vol 50, pg 230, 2017)
    Ichimasa, Katsuro
    Kudo, Shin-ei
    Mori, Yuichi
    Misawa, Masashi
    Matsudaira, Shingo
    Kouyama, Yuta
    Baba, Toshiyuki
    Hidaka, Eiji
    Wakamura, Kunihiko
    Hayashi, Takemasa
    Kudo, Toyoki
    Ishigaki, Tomoyuki
    Yagawa, Yusuke
    Nakamura, Hiroki
    Takeda, Kenichi
    Haji, Amyn
    Hamatani, Shigeharu
    Mori, Kensaku
    Ishida, Fumio
    Miyachi, Hideyuki
    [J]. ENDOSCOPY, 2018, 50 (03) : C2 - C2