Clinical Endoscopy

The Korean Society of Gastrointestinal Endoscopy (대한소화기내시경학회)
권호 표지
DOI QR코드

DOI QR Code

Use of artificial intelligence in the management of T1 colorectal cancer: a new tool in the arsenal or is deep learning out of its depth?

  • James Weiquan Li (Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services) ;
  • Lai Mun Wang (Department of Laboratory Medicine, Changi General Hospital, Singapore Health Services) ;
  • Katsuro Ichimasa (Digestive Disease Center, Showa University Northern Yokohama Hospital) ;
  • Kenneth Weicong Lin (Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services) ;
  • James Chi-Yong Ngu (Department of General Surgery, Changi General Hospital, Singapore Health Services) ;
  • Tiing Leong Ang (Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services)
  • Received : 2023.02.01
  • Accepted : 2023.05.11
  • Published : 2024.01.30
Download PDF
⟨ Previous Next ⟩

Abstract

The field of artificial intelligence is rapidly evolving, and there has been an interest in its use to predict the risk of lymph node metastasis in T1 colorectal cancer. Accurately predicting lymph node invasion may result in fewer patients undergoing unnecessary surgeries; conversely, inadequate assessments will result in suboptimal oncological outcomes. This narrative review aims to summarize the current literature on deep learning for predicting the probability of lymph node metastasis in T1 colorectal cancer, highlighting areas of potential application and barriers that may limit its generalizability and clinical utility.

Keywords

References

  1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021;71:209-249. 
  2. Xi Y, Xu P. Global colorectal cancer burden in 2020 and projections to 2040. Transl Oncol 2021;14:101174. 
  3. Bretthauer M, Kaminski MF, Loberg M, et al. Population-based colonoscopy screening for colorectal cancer: a randomized clinical trial. JAMA Intern Med 2016;176:894-902. 
  4. Dekker E, Tanis PJ, Vleugels JL, et al. Colorectal cancer. Lancet 2019;394:1467-1480. 
  5. Draganov PV, Wang AY, Othman MO, et al. AGA Institute Clinical Practice Update: endoscopic submucosal dissection in the United States. Clin Gastroenterol Hepatol 2019;17:16-25. 
  6. Pimentel-Nunes P, Dinis-Ribeiro M, Ponchon T, et al. Endoscopic submucosal dissection: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. Endoscopy 2015;47:829-854. 
  7. Dykstra MA, Gimon TI, Ronksley PE, et al. Classic and novel histopathologic risk factors for lymph node metastasis in T1 colorectal cancer: a systematic review and meta-analysis. Dis Colon Rectum 2021;64:1139-1150. 
  8. Niimi K, Fujishiro M, Kodashima S, et al. Long-term outcomes of endoscopic submucosal dissection for colorectal epithelial neoplasms. Endoscopy 2010;42:723-729. 
  9. Saito Y, Uraoka T, Yamaguchi Y, et al. A prospective, multicenter study of 1111 colorectal endoscopic submucosal dissections (with video). Gastrointest Endosc 2010;72:1217-1225. 
  10. Hashiguchi Y, Muro K, Saito Y, et al. Japanese Society for Cancer of the Colon and Rectum (JSCCR) guidelines 2019 for the treatment of colorectal cancer. Int J Clin Oncol 2020;25:1-42. 
  11. Japanese Society for Cancer of the Colon and Rectum. Japanese classification of colorectal, appendiceal, and anal carcinoma: the 3d English edition [secondary publication]. J Anus Rectum Colon 2019;3:175-195. 
  12. Tateishi Y, Nakanishi Y, Taniguchi H, et al. Pathological prognostic factors predicting lymph node metastasis in submucosal invasive (T1) colorectal carcinoma. Mod Pathol 2010;23:1068-1072. 
  13. Glynne-Jones R, Wyrwicz L, Tiret E, et al. Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2017;28(suppl_4):iv22-iv40. 
  14. Benson AB, Venook AP, Al-Hawary MM, et al. Rectal cancer, version 2.2018, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 2018;16:874-901. 
  15. 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. 
  16. Ebbehoj AL, Jorgensen LN, Krarup PM, et al. Histopathological risk factors for lymph node metastases in T1 colorectal cancer: meta-analysis. Br J Surg 2021;108:769-776. 
  17. 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. 
  18. Yamamoto S, Watanabe M, Hasegawa H, et al. The risk of lymph node metastasis in T1 colorectal carcinoma. Hepatogastroenterology 2004;51:998-1000. 
  19. Zong Z, Li H, Hu CG, et al. Predictors of lymph-node metastasis in surgically resected T1 colorectal cancer in Western populations. Gastroenterol Rep (Oxf) 2021;9:470-474. 
  20. Vermeer NC, Backes Y, Snijders HS, et al. National cohort study on postoperative risks after surgery for submucosal invasive colorectal cancer. BJS Open 2018;3:210-217. 
  21. Ichimasa K, Kudo SE, Miyachi H, et al. Current problems and perspectives of pathological risk factors for lymph node metastasis in T1 colorectal cancer: systematic review. Dig Endosc 2022;34:901-912. 
  22. Hassan C, Spadaccini M, Iannone A, et al. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc 2021;93:77-85. 
  23. Sivananthan A, Nazarian S, Ayaru L, et al. Does computer-aided diagnostic endoscopy improve the detection of commonly missed polyps?: a meta-analysis. Clin Endosc 2022;55:355-364. 
  24. Li JW, Chia T, Fock KM, et al. Artificial intelligence and polyp detection in colonoscopy: use of a single neural network to achieve rapid polyp localization for clinical use. J Gastroenterol Hepatol 2021;36:3298-3307. 
  25. Li JW, Ang TL. Colonoscopy and artificial intelligence: bridging the gap or a gap needing to be bridged? Artif Intell Gastrointest Endosc 2021;2:36-49. 
  26. Chen H, Sung JJ. Potentials of AI in medical image analysis in gastroenterology and hepatology. J Gastroenterol Hepatol 2021;36:31-38. 
  27. Collins GS, Moons KG. Reporting of artificial intelligence prediction models. Lancet 2019;393:1577-1579. 
  28. CONSORT-AI and SPIRIT-AI Steering Group. Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. Nat Med 2019;25:1467-1468. 
  29. Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 2020;368:m689. 
  30. Rivera SC, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ 2020;370:m3210. 
  31. Ahmad OF, Mori Y, Misawa M, et al. Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method. Endoscopy 2021;53:893-901. 
  32. Li JW, Ang TL. Narrow-band imaging. In: Chiu PW, Sano Y, Uedo N, Singh R, editors. Endoscopy in early gastrointestinal cancers, volume 1: diagnosis. Singapore: Springer Singapore; 2021. p. 111-119. 
  33. Sano Y, Tanaka S, Kudo SE, et al. Narrow-band imaging (NBI) magnifying endoscopic classification of colorectal tumors proposed by the Japan NBI Expert Team. Dig Endosc 2016;28:526-533. 
  34. Ito R, Ikematsu H, Murano T, et al. Diagnostic ability of Japan Narrow-Band Imaging Expert Team classification for colorectal lesions by magnifying endoscopy with blue laser imaging versus narrow-band imaging. Endosc Int Open 2021;9:E271-E277. 
  35. Hewett DG, Kaltenbach T, Sano Y, et al. Validation of a simple classification system for endoscopic diagnosis of small colorectal polyps using narrow-band imaging. Gastroenterology 2012;143:599-607. 
  36. Kaltenbach T, Anderson JC, Burke CA, et al. Endoscopic removal of colorectal lesions-recommendations by the US Multi-Society Task Force on Colorectal Cancer. Gastroenterology 2020;158:1095-1129. 
  37. Desai M, Kennedy K, Aihara H, et al. External validation of blue light imaging (BLI) criteria for the optical characterization of colorectal polyps by endoscopy experts. J Gastroenterol Hepatol 2021;36:2728-2734. 
  38. Bisschops R, East JE, Hassan C, et al. Advanced imaging for detection and differentiation of colorectal neoplasia: European Society of Gastrointestinal Endoscopy (ESGE) guideline: update 2019. Endoscopy 2019;51:1155-1179. 
  39. Ishigaki T, Kudo SE, Miyachi H, et al. Treatment policy for colonic laterally spreading tumors based on each clinicopathologic feature of 4 subtypes: actual status of pseudo-depressed type. Gastrointest Endosc 2020;92:1083-1094. 
  40. Vosko S, Shahidi N, Sidhu M, et al. Optical evaluation for predicting cancer in large nonpedunculated colorectal polyps is accurate for flat lesions. Clin Gastroenterol Hepatol 2021;19:2425-2434. 
  41. Smith SCL, Siau K, Cannatelli R, et al. Training methods in optical diagnosis and characterization of colorectal polyps: a systematic review and meta-analysis. Endosc Int Open 2021;9:E716-E726. 
  42. Klare P, Haller B, Wormbt S, et al. Narrow-band imaging vs. high definition white light for optical diagnosis of small colorectal polyps: a randomized multicenter trial. Endoscopy 2016;48:909-915. 
  43. Takemura Y, Yoshida S, Tanaka S, et al. Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video). Gastrointest Endosc 2012;75:179-185. 
  44. Takeda K, Kudo SE, Mori Y, et al. Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy. Endoscopy 2017;49:798-802. 
  45. Stefanescu D, Streba C, Cartana ET, et al. Computer aided diagnosis for confocal laser endomicroscopy in advanced colorectal adenocarcinoma. PLoS One 2016;11:e0154863. 
  46. Lui TK, Wong KK, Mak LL, et al. Endoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence. Endosc Int Open 2019;7:E514-E520. 
  47. Luo X, Wang J, Han Z, et al. Artificial intelligence-enhanced whitelight colonoscopy with attention guidance predicts colorectal cancer invasion depth. Gastrointest Endosc 2021;94:627-638. 
  48. Tokunaga M, Matsumura T, Nankinzan R, et al. Computer-aided diagnosis system using only white-light endoscopy for the prediction of invasion depth in colorectal cancer. Gastrointest Endosc 2021;93:647-653. 
  49. Ito N, Kawahira H, Nakashima H, et al. Endoscopic diagnostic support system for cT1b colorectal cancer using deep learning. Oncology 2019;96:44-50. 
  50. Nakajima Y, Zhu X, Nemoto D, et al. Diagnostic performance of artificial intelligence to identify deeply invasive colorectal cancer on non-magnified plain endoscopic images. Endosc Int Open 2020;8:E1341-E1348. 
  51. Lu Z, Xu Y, Yao L, et al. Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video). Gastrointest Endosc 2022;95:1186-1194. 
  52. Ang TL, Lim JF, Chua TS, et al. Clinical guidance on endoscopic management of colonic polyps in Singapore. Singapore Med J 2022;63:173-186. 
  53. Barel F, Auffret A, Cariou M, et al. High reproducibility is attainable in assessing histoprognostic parameters of pT1 colorectal cancer using routine histopathology slides and immunohistochemistry analyses. Pathology 2019;51:46-54. 
  54. Kojima M, Puppa G, Kirsch R, et al. Blood and lymphatic vessel invasion in pT1 colorectal cancer: an international concordance study. J Clin Pathol 2015;68:628-632. 
  55. Kouyama Y, Kudo SE, Miyachi H, et al. Practical problems of measuring depth of submucosal invasion in T1 colorectal carcinomas. Int J Colorectal Dis 2016;31:137-146. 
  56. Hacking S, Nasim R, Lee L, et al. Whole slide imaging and colorectal carcinoma: a validation study for tumor budding and stromal differentiation. Pathol Res Pract 2020;216:153233. 
  57. Hacking S, Angert M, Jin C, et al. Tumor budding in colorectal carcinoma: an institutional interobserver reliability and prognostic study of colorectal adenocarcinoma cases. Ann Diagn Pathol 2019;43:151420. 
  58. Martin B, Schafer E, Jakubowicz E, et al. Interobserver variability in the H&E-based assessment of tumor budding in pT3/4 colon cancer: does it affect the prognostic relevance? Virchows Arch 2018;473:189-197. 
  59. 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. 
  60. Nakadoi K, Tanaka S, Kanao H, et al. Management of T1 colorectal carcinoma with special reference to criteria for curative endoscopic resection. J Gastroenterol Hepatol 2012;27:1057-1062. 
  61. Zwager LW, Bastiaansen BA, Montazeri NS, et al. Deep submucosal invasion is not an independent risk factor for lymph node metastasis in T1 colorectal cancer: a meta-analysis. Gastroenterology 2022;163:174-189. 
  62. Goacher E, Randell R, Williams B, et al. The diagnostic concordance of whole slide imaging and light microscopy: a systematic review. Arch Pathol Lab Med 2017;141:151-161. 
  63. Koch LH, Lampros JN, Delong LK, et al. Randomized comparison of virtual microscopy and traditional glass microscopy in diagnostic accuracy among dermatology and pathology residents. Hum Pathol 2009;40:662-667. 
  64. Mukhopadhyay S, Feldman MD, Abels E, et al. Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: a multicenter blinded randomized noninferiority study of 1992 cases (pivotal study). Am J Surg Pathol 2018;42:39-52. 
  65. Weinstein RS, Holcomb MJ, Krupinski EA. Invention and early history of telepathology (1985-2000). J Pathol Inform 2019;10:1. 
  66. Ben Hamida A, Devanne M, Weber J, et al. Deep learning for colon cancer histopathological images analysis. Comput Biol Med 2021;136:104730. 
  67. Skrede OJ, De Raedt S, Kleppe A, et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet 2020;395:350-360. 
  68. Wang KS, Yu G, Xu C, et al. Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC Med 2021;19:76. 
  69. Gupta P, Huang Y, Sahoo PK, et al. Colon tissues classification and localization in whole slide images using deep learning. Diagnostics (Basel) 2021;11:1398. 
  70. Kwak MS, Lee HH, Yang JM, et al. Deep convolutional neural network-based lymph node metastasis prediction for colon cancer using histopathological images. Front Oncol 2021;10:619803. 
  71. Kudo SE, Ichimasa K, Villard B, et al. Artificial intelligence system to determine risk of T1 colorectal cancer metastasis to lymph node. Gastroenterology 2021;160:1075-1084. 
  72. 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 6:vi64-vi72. 
  73. Kang J, Choi YJ, Kim IK, et al. LASSO-based machine learning algorithm for prediction of lymph node metastasis in T1 colorectal cancer. Cancer Res Treat 2021;53:773-783. 
  74. Backes Y, Elias SG, Groen JN, et al. Histologic factors associated with need for surgery in patients with pedunculated T1 colorectal carcinomas. Gastroenterology 2018;154:1647-1659. 
  75. Takamatsu M, Yamamoto N, Kawachi H, et al. Prediction of early colorectal cancer metastasis by machine learning using digital slide images. Comput Methods Programs Biomed 2019;178:155-161. 
  76. Song JH, Hong Y, Kim ER, et al. Utility of artificial intelligence with deep learning of hematoxylin and eosin-stained whole slide images to predict lymph node metastasis in T1 colorectal cancer using endoscopically resected specimens; prediction of lymph node metastasis in T1 colorectal cancer. J Gastroenterol 2022;57:654-666. 
  77. Kasahara K, Katsumata K, Saito A, et al. Artificial intelligence predicts lymph node metastasis or risk of lymph node metastasis in T1 colorectal cancer. Int J Clin Oncol 2022;27:1570-1579. 
  78. Brockmoeller S, Echle A, Ghaffari Laleh N, et al. Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer. J Pathol 2022;256:269-281. 
  79. Echle A, Grabsch HI, Quirke P, et al. Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning. Gastroenterology 2020;159:1406-1416. 
  80. Krause J, Grabsch HI, Kloor M, et al. Deep learning detects genetic alterations in cancer histology generated by adversarial networks. J Pathol 2021;254:70-79. 
  81. Yamashita R, Long J, Longacre T, et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. Lancet Oncol 2021;22:132-141. 
  82. Li JW, Wang LM, Ang TL. Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications. Singapore Med J 2022;63:118-124. 
  83. Yang CB, Kim SH, Lim YJ. Preparation of image databases for artificial intelligence algorithm development in gastrointestinal endoscopy. Clin Endosc 2022;55:594-604. 
  84. Oliveira SP, Neto PC, Fraga J, et al. CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance. Sci Rep 2021;11:14358. 
  85. Peterson E, May FP, Kachikian O, et al. Automated identification and assignment of colonoscopy surveillance recommendations for individuals with colorectal polyps. Gastrointest Endosc 2021;94:978-987.