References
- 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.
- Xi Y, Xu P. Global colorectal cancer burden in 2020 and projections to 2040. Transl Oncol 2021;14:101174.
- 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.
- Dekker E, Tanis PJ, Vleugels JL, et al. Colorectal cancer. Lancet 2019;394:1467-1480.
- 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.
- 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.
- 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.
- Niimi K, Fujishiro M, Kodashima S, et al. Long-term outcomes of endoscopic submucosal dissection for colorectal epithelial neoplasms. Endoscopy 2010;42:723-729.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Yamamoto S, Watanabe M, Hasegawa H, et al. The risk of lymph node metastasis in T1 colorectal carcinoma. Hepatogastroenterology 2004;51:998-1000.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Chen H, Sung JJ. Potentials of AI in medical image analysis in gastroenterology and hepatology. J Gastroenterol Hepatol 2021;36:31-38.
- Collins GS, Moons KG. Reporting of artificial intelligence prediction models. Lancet 2019;393:1577-1579.
- CONSORT-AI and SPIRIT-AI Steering Group. Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. Nat Med 2019;25:1467-1468.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Takeda K, Kudo SE, Mori Y, et al. Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy. Endoscopy 2017;49:798-802.
- Stefanescu D, Streba C, Cartana ET, et al. Computer aided diagnosis for confocal laser endomicroscopy in advanced colorectal adenocarcinoma. PLoS One 2016;11:e0154863.
- 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.
- 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.
- 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.
- Ito N, Kawahira H, Nakashima H, et al. Endoscopic diagnostic support system for cT1b colorectal cancer using deep learning. Oncology 2019;96:44-50.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Weinstein RS, Holcomb MJ, Krupinski EA. Invention and early history of telepathology (1985-2000). J Pathol Inform 2019;10:1.
- Ben Hamida A, Devanne M, Weber J, et al. Deep learning for colon cancer histopathological images analysis. Comput Biol Med 2021;136:104730.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Yang CB, Kim SH, Lim YJ. Preparation of image databases for artificial intelligence algorithm development in gastrointestinal endoscopy. Clin Endosc 2022;55:594-604.
- 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.
- 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.