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

Katsuro Ichimasa; Shin-ei Kudo; Yuichi Mori; Masashi Misawa; Shingo Matsudaira; Yuta Kouyama; Toshiyuki Baba; Eiji Hidaka; Kunihiko Wakamura; Takemasa Hayashi; Toyoki Kudo; Tomoyuki Ishigaki; Yusuke Yagawa; Hiroki Nakamura; Kenichi Takeda; Amyn Haji; Shigeharu Hamatani; Kensaku Mori; Fumio Ishida; Hideyuki Miyachi

doi:10.1055/s-0043-122385

Endoscopy, Table of Contents

Endoscopy 2018; 50(03): 230-240
DOI: 10.1055/s-0043-122385

Original article

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

Authors

Katsuro Ichimasa

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Shin-ei Kudo

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Yuichi Mori

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Masashi Misawa

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Shingo Matsudaira

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Yuta Kouyama

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Toshiyuki Baba

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Eiji Hidaka

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Kunihiko Wakamura

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Takemasa Hayashi

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Toyoki Kudo

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Tomoyuki Ishigaki

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Yusuke Yagawa

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Hiroki Nakamura

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Kenichi Takeda

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Amyn Haji

²King’s Institute of Therapeutic Endoscopy, King’s College Hospital, London, United Kingdom
Shigeharu Hamatani

³Department of Pathology, Jikei University School of Medicine, Tokyo, Japan
Kensaku Mori

⁴Information and Communications, Nagoya University, Nagoya, Japan
Fumio Ishida

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
Hideyuki Miyachi

¹Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan

⁵Miyachi Clinic, Kakogawa, Japan

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.

Full Text

References

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;
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
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
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
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
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
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
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
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
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;
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
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
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
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
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
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
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
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
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
20 Bosman FT, Carneiro F, Hruban RH, Theise ND. WHO classification of tumours of the digestive system, 4th edn. Lyon, France: 2010
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
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
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
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
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
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
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
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
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
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
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

Supplementary Material

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