PDF herunterladen
Endoscopy 2018; 50(03): 230-240
DOI: 10.1055/s-0043-122385
Original article
© Georg Thieme Verlag KG Stuttgart · New York

Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer

Katsuro Ichimasa
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Shin-ei Kudo
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Yuichi Mori
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Masashi Misawa
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Shingo Matsudaira
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Yuta Kouyama
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Toshiyuki Baba
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Eiji Hidaka
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Kunihiko Wakamura
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Takemasa Hayashi
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Toyoki Kudo
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Tomoyuki Ishigaki
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Yusuke Yagawa
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Hiroki Nakamura
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Kenichi Takeda
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Amyn Haji
2   King’s Institute of Therapeutic Endoscopy, King’s College Hospital, London, United Kingdom
,
Shigeharu Hamatani
3   Department of Pathology, Jikei University School of Medicine, Tokyo, Japan
,
Kensaku Mori
4   Information and Communications, Nagoya University, Nagoya, Japan
,
Fumio Ishida
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
,
Hideyuki Miyachi
1   Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
5   Miyachi Clinic, Kakogawa, Japan
› InstitutsangabenTRIAL REGISTRATION: Retrospective Study UMIN000026552 at http://www.umin.ac.jp.
Weitere Informationen

Publikationsverlauf

submitted 16. Mai 2017

accepted after revision 21. September 2017

Publikationsdatum:
22. Dezember 2017 (online)

Auch verfügbar auf
    Lizenzen und Reprints

Originalartikel zu diesem Erratum:

Correction: Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancerEndoscopy 2018; 50(03): C2-C2
DOI: 10.1055/s-0044-100290

Siehe auch:

Video Comment on Katsuro Ichimasa et al.Endoscopy 2018; 50(03): v12-v12
DOI: 10.1055/s-0044-101175

Abstract

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.

Supplemental material

 

  • References

  • 1 Overwater A, Kessels K, Elias SG. et al. Endoscopic resection of high-risk T1 colorectal carcinoma prior to surgical resection has no adverse effect on long-term outcomes. Gut 2016; DOI: 10.1136/gutjnl-2015-310961.
    CrossrefPubMedGoogle Scholar
  • 2 Bosch SL, Teerenstra S, de Wilt JH. et al. Predicting lymph node metastasis in pT1 colorectal cancer: a systematic review of risk factors providing rationale for therapy decisions. Endoscopy 2013; 45: 827-834
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 3 Miyachi H, Kudo SE, Ichimasa K. et al. Management of T1 colorectal cancers after endoscopic treatment based on the risk stratification of lymph node metastasis. J Gastroenterol Hepatol 2016; 31: 1126-1132
    CrossrefPubMedGoogle Scholar
  • 4 Nascimbeni R, Burgart LJ, Nivatvongs S. et al. Risk of lymph node metastasis in T1 carcinoma of the colon and rectum. Dis Colon Rectum 2002; 45: 200-206
    CrossrefPubMedGoogle Scholar
  • 5 Wada H, Shiozawa M, Katayama K. et al. Systematic review and meta-analysis of histopathological predictive factors for lymph node metastasis in T1 colorectal cancer. J Gastroenterol 2015; 50: 727-734
    CrossrefPubMedGoogle Scholar
  • 6 National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) Rectal Cancer (Version 1.2017). 2017 Available from: https://www.nccn.org/professionals/physician_gls/f_guidelines.asp
    PubMedGoogle Scholar
  • 7 National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) Colon Cancer (Version 1.2017). 2017 Available from: https://www.nccn.org/professionals/physician_gls/f_guidelines.asp
    PubMedGoogle Scholar
  • 8 Labianca R, Nordlinger B, Beretta GD. et al. Early colon cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2013; 24 (Suppl. 06) vi64-72
    PubMedGoogle Scholar
  • 9 Glimelius B, Tiret E, Cervantes A. et al. Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2013; 24 (Suppl. 06) vi81-88
    CrossrefPubMedGoogle Scholar
  • 10 Watanabe T, Muro K, Ajioka Y. et al. Japanese Society for Cancer of the Colon and Rectum (JSCCR) guidelines 2016 for the treatment of colorectal cancer. Int J Clin Oncol 2017; DOI: 10.1007/s10147-017-1101-6.
    CrossrefPubMedGoogle Scholar
  • 11 Tanaka S, Haruma K, Teixeira CR. et al. Endoscopic treatment of submucosal invasive colorectal carcinoma with special reference to risk factors for lymph node metastasis. J Gastroenterol 1995; 30: 710-717
    CrossrefPubMedGoogle Scholar
  • 12 Kobayashi H, Mochizuki H, Morita T. et al. Characteristics of recurrence after curative resection for T1 colorectal cancer: Japanese multicenter study. J Gastroenterol 2011; 46: 203-211
    CrossrefPubMedGoogle Scholar
  • 13 Coverlizza S, Risio M, Ferrari A. et al. Colorectal adenomas containing invasive carcinoma. Pathologic assessment of lymph node metastatic potential. Cancer 1989; 64: 1937-1947
    CrossrefPubMedGoogle Scholar
  • 14 Kitamura K, Taniguchi H, Yamaguchi T. et al. Clinical outcome of surgical treatment for invasive early colorectal cancer in Japan. Hepatogastroenterology 1997; 44: 108-115
    PubMedGoogle Scholar
  • 15 Tateishi Y, Nakanishi Y, Taniguchi H. et al. Pathological prognostic factors predicting lymph node metastasis in submucosal invasive (T1) colorectal carcinoma. Modern Pathol 2010; 23: 1068-1072
    CrossrefPubMedGoogle Scholar
  • 16 Iversen LH, Nielsen H, Pedersen L. et al. Seasonal variation in short-term mortality after surgery for colorectal cancer?. Colorectal Dis 2010; 12: e31-36
    CrossrefPubMedGoogle Scholar
  • 17 Paulson EC, Mitra N, Sonnad S. et al. National Cancer Institute designation predicts improved outcomes in colorectal cancer surgery. Ann Surg 2008; 248: 675-686
    PubMedGoogle Scholar
  • 18 Moons KG, Altman DG, Reitsma JB. et al. New guideline for the reporting of studies developing, validating, or updating a multivariable clinical prediction model: the TRIPOD statement. Adv Anat Pathol 2015; 22: 303-305
    CrossrefPubMedGoogle Scholar
  • 19 Kobayashi Y, Kudo SE, Miyachi H. et al. Clinical usefulness of pit patterns for detecting colonic lesions requiring surgical treatment. Int J Colorectal Dis 2011; 26: 1531-1540
    CrossrefPubMedGoogle Scholar
  • 20 Bosman FT, Carneiro F, Hruban RH, Theise ND. WHO classification of tumours of the digestive system, 4th edn. Lyon, France: 2010
    Google Scholar
  • 21 Beaton C, Twine CP, Williams GL. et al. Systematic review and meta-analysis of histopathological factors influencing the risk of lymph node metastasis in early colorectal cancer. Colorectal Dis 2013; 15: 788-797
    CrossrefPubMedGoogle Scholar
  • 22 Wada H, Shiozawa M, Sugano N. et al. Lymphatic invasion identified with D2-40 immunostaining as a risk factor of nodal metastasis in T1 colorectal cancer. Int J Clin Oncol 2013; 18: 1025-1031
    CrossrefPubMedGoogle Scholar
  • 23 Macias-Garcia F, Celeiro-Munoz C, Lesquereux-Martinez L. et al. A clinical model for predicting lymph node metastasis in submucosal invasive (T1) colorectal cancer. Int J Colorectal Dis 2015; 30: 761-768
    CrossrefPubMedGoogle Scholar
  • 24 Ichimasa K, Kudo SE, Miyachi H. et al. Patient gender as a factor associated with lymph node metastasis in T1 colorectal cancer: a systematic review and meta-analysis. Mol Clin Oncol 2017; 6: 517-524
    CrossrefPubMedGoogle Scholar
  • 25 Takeuchi Y, Uedo N, Ishihara R. et al. Efficacy of an endo-knife with a water-jet function (Flushknife) for endoscopic submucosal dissection of superficial colorectal neoplasms. Am J Gastroenterol 2010; 105: 314-322
    CrossrefPubMedGoogle Scholar
  • 26 Lee W, Lee D, Choi S. et al. Transanal endoscopic microsurgery and radical surgery for T1 and T2 rectal cancer. Surg Endosc 2003; 17: 1283-1287
    CrossrefPubMedGoogle Scholar
  • 27 Wu HY, Gong CS, Lin SP. et al. Predicting postoperative vomiting among orthopedic patients receiving patient-controlled epidural analgesia using SVM and LR. Sci Rep 2016; 6: 27041
    CrossrefPubMedGoogle Scholar
  • 28 Mori Y, Kudo SE, Wakamura K. et al. Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy (with videos). Gastrointest Endosc 2015; 81: 621-629
    CrossrefPubMedGoogle Scholar
  • 29 Yang HX, Feng W, Wei JC. et al. Support vector machine-based nomogram predicts postoperative distant metastasis for patients with oesophageal squamous cell carcinoma. Br J Cancer 2013; 109: 1109-1116
    CrossrefPubMedGoogle Scholar
  • 30 Misawa M, Kudo SE, Mori Y. et al. Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy. Gastroenterology 2016; 150: 1531-1532 e1533
    CrossrefPubMedGoogle Scholar
  • 31 Mourao-Miranda J, Bokde AL, Born C. et al. Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data. Neuroimage 2005; 28: 980-995
    CrossrefPubMedGoogle Scholar