95
Views
1
CrossRef citations to date
0
Altmetric
Original Research

Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis

ORCID Icon, , , , &
Pages 8967-8977 | Published online: 01 Dec 2021

References

  • Brenner H, Kloor M, Pox CP. Colorectal cancer. Lancet. 2014;383(9927):1490–1502. doi:10.1016/S0140-6736(13)61649-924225001
  • Simon K. Colorectal cancer development and advances in screening. Clin Interv Aging. 2016;11:967–976. doi:10.2147/CIA.S10928527486317
  • Wen XQ, Qian XL, Sun HK, et al. MicroRNAs: multifaceted regulators of colorectal cancer metastasis and clinical applications. Onco Targets Ther. 2020;13:10851–10866. doi:10.2147/OTT.S26558033149603
  • Muto T, Oya M. Recent advances in diagnosis and treatment of colorectal T1 carcinoma. Dis Colon Rectum. 2003;46(10 Suppl):S89–93. doi:10.1097/01.DCR.0000083525.97708.B514530664
  • Nascimbeni R, Burgart LJ, Nivatvongs S, Larson DR. Risk of lymph node metastasis in T1 carcinoma of the colon and rectum. Dis Colon Rectum. 2002;45(2):200–206. doi:10.1007/s10350-004-6147-711852333
  • 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–iv40.28881920
  • Brenner H, Hoffmeister M, Arndt V, Stegmaier C, Altenhofen L, Haug U. Protection from right- and left-sided colorectal neoplasms after colonoscopy: population-based study. J Natl Cancer Inst. 2010;102(2):89–95. doi:10.1093/jnci/djp43620042716
  • Tian Y, Rong L, Ma Y. Surgical resection after endoscopic resection in patients with T1 colorectal cancer: a meta-analysis. Int J Colorectal Dis. 2021;36(3):457–466. doi:10.1007/s00384-020-03752-233111966
  • Suh JH, Han KS, Kim BC, et al. Predictors for lymph node metastasis in T1 colorectal cancer. Endoscopy. 2012;44(6):590–595. doi:10.1055/s-0031-129166522638780
  • Ichimasa K, Kudo SE, Miyachi H, Kouyama Y, Misawa M, Mori Y. Risk Stratification of T1 Colorectal Cancer Metastasis to Lymph Nodes: current Status and Perspective. Gut Liver. 2020;2:548.
  • Jiang T, Gradus JL, Rosellini AJ. Supervised Machine Learning: a Brief Primer. Behav Ther. 2020;51(5):675–687. doi:10.1016/j.beth.2020.05.00232800297
  • Ichimasa K, Kudo SE, Mori Y, et al. Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer. Endoscopy. 2018;50(3):230–240. doi:10.1055/s-0043-12238529272905
  • Rashidi HH, Tran NK, Betts EV, Howell LP, Green R. Artificial Intelligence and Machine Learning in Pathology: the Present Landscape of Supervised Methods. Acad Pathol. 2019;6:2374289519873088. doi:10.1177/237428951987308831523704
  • Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594. doi:10.1136/bmj.g759425569120
  • Armstrong RA. When to use the Bonferroni correction. Ophthalmic Physiol Optics. 2014;34(5):502–508. doi:10.1111/opo.12131
  • Buri M, Hothorn T. Model-based random forests for ordinal regression. Int J Biostat. 2020;16(2). doi:10.1515/ijb-2019-0063
  • Portet S. A primer on model selection using the Akaike Information Criterion. Infect Dis Modelling. 2020;5:111–128. doi:10.1016/j.idm.2019.12.010
  • Saitoh Y, Inaba Y, Sasaki T, Sugiyama R, Sukegawa R, Fujiya M. Management of colorectal T1 carcinoma treated by endoscopic resection. Digestive Endoscopy. 2016;28(3):324–329. doi:10.1111/den.1250326076802
  • Pedersen TB, Kildsig J, Serup-Hansen E, Gocht-Jensen P, Klein MF. Outcome following local excision of T1 anal cancers-A systematic review. Int J Colorectal Dis. 2020;35(9):1663–1671. doi:10.1007/s00384-020-03687-832671458
  • Oh JR, Park B, Lee S, et al. Nomogram Development and External Validation for Predicting the Risk of Lymph Node Metastasis in T1 Colorectal Cancer. Cancer Res Treatment. 2019;51(4):1275–1284. doi:10.4143/crt.2018.569
  • Park EY, Baek DH, Lee MW, Kim GH, Park DY, Song GA. Long-Term Outcomes of T1 Colorectal Cancer after Endoscopic Resection. J Clin Med. 2020;9(8):2451. doi:10.3390/jcm9082451
  • Asayama N, Oka S, Tanaka S, et al. Long-term outcomes after treatment for T1 colorectal carcinoma. Int J Colorectal Dis. 2016;31(3):571–578. doi:10.1007/s00384-015-2473-626689400
  • 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(7):727–734. doi:10.1007/s00535-015-1057-025725617
  • Cracco N, Todaro V, Pedrazzi G, Del Rio P, Haboubi N, Zinicola R. The risk of lymph node metastasis in T1 colorectal cancer: new parameters to assess the degree of submucosal invasion. Int J Colorectal Dis. 2021;36(1):41–45. doi:10.1007/s00384-020-03738-032901349
  • Mou S, Soetikno R, Shimoda T, Rouse R, Kaltenbach T. Pathologic predictive factors for lymph node metastasis in submucosal invasive (T1) colorectal cancer: a systematic review and meta-analysis. Surg Endosc. 2013;27(8):2692–2703. doi:10.1007/s00464-013-2835-523392988
  • Jia J, Zheng X, Chen Y, et al. Stage-dependent changes of preoperative neutrophil to lymphocyte ratio and platelet to lymphocyte ratio in colorectal cancer. Tumour Biol. 2015;36(12):9319–9325.26104767
  • Stojkovic Lalosevic M, Pavlovic Markovic A, Stankovic S, et al. Combined Diagnostic Efficacy of Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and Mean Platelet Volume (MPV) as Biomarkers of Systemic Inflammation in the Diagnosis of Colorectal Cancer. Dis Markers. 2019;2019:6036979. doi:10.1155/2019/603697930800188
  • Singh N, Baby D, Rajguru JP, Patil PB, Thakkannavar SS, Pujari VB. Inflammation and cancer. Ann Afr Med. 2019;18(3):121–126. doi:10.4103/aam.aam_56_1831417011
  • Coussens LM, Werb Z. Inflammation and cancer. Nature. 2002;420(6917):860–867. doi:10.1038/nature0132212490959
  • Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature. 2008;454(7203):436–444. doi:10.1038/nature0720518650914
  • Colotta F, Allavena P, Sica A, Garlanda C, Mantovani A. Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability. Carcinogenesis. 2009;30(7):1073–1081. doi:10.1093/carcin/bgp12719468060
  • Doupe P, Faghmous J, Basu S. Machine Learning for Health Services Researchers. Value Health. 2019;22(7):808–815. doi:10.1016/j.jval.2019.02.01231277828
  • Chen J, de Hoogh K, Gulliver J, et al. Development of Europe-Wide Models for Particle Elemental Composition Using Supervised Linear Regression and Random Forest. Environ Sci Technol. 2020;54(24):15698–15709. doi:10.1021/acs.est.0c0659533237771
  • Breiman L. Random Forests. Mach Learn. 2001;45(1):5–32. doi:10.1023/A:1010933404324
  • Lendrem BC, Lendrem DW, Pratt AG, et al. Between a ROC and a hard place: teaching prevalence plots to understand real world biomarker performance in the clinic. Pharm Stat. 2019;18(6):632–635. doi:10.1002/pst.196331231892
  • Sande SZ, Li J, D’Agostino R, Yin Wong T, Cheng CY. Statistical inference for decision curve analysis, with applications to cataract diagnosis. Stat Med. 2020;39(22):2980–3002. doi:10.1002/sim.858832667093