Predicting outcomes of gastric endoscopic submucosal dissection using a Bayesian approach: a step for individualized risk assessment

Abstract

Background and study aims Efficacy and adverse events probabilities influence decisions regarding the best options to manage patients with gastric superficial lesions. We aimed at developing a Bayesian model to individualize the prediction of outcomes after gastric endoscopic submucosal dissection (ESD).

Patients and methods Data from 245 gastric ESD were collected, including patient and lesion factors. The two endpoints were curative resection and post-procedural bleeding (PPB). Logistic regression and Bayesian networks were built for each outcome; their predictive value was evaluated in-sample and validated through leave-one-out and cross-validation. Clinical decision support was enhanced by the definition of risk matrices, direct use of Bayesian inference software and by a developed online platform.

Results ESD was curative in 85.3 % and PPB occurred in 7.7 % of patients. In univariate analysis, male sex, ASA status, carcinoma histology, polypoid or depressed morphology, and lesion size ≥ 20 mm were associated with non-curative resection, while ASA status, antithrombotics and lesion size ≥ 20 mm were associated with PPB. Naïve Bayesian models presented AUROCs of ~80 % in the derivation cohort and ≥ 74 % in cross-validation for both outcomes. Risk matrices were computed, showing that lesions with cancer at biopsies, ≥ 20 mm, proximal or in the middle third, and polypoid are more prone to non-curative resection. PPB risk was < 5 % in lesions < 20 mm in the absence of antithrombotics.

Conclusions The derived Bayesian model presented good discriminative power in the prediction of ESD outcomes and can be used to predict individualized probabilities, improving patient information and supporting clinical and management decisions.

• References

• 1 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
• 2 Facciorusso A, Antonino M, Di Maso M. et al. Endoscopic submucosal dissection vs endoscopic mucosal resection for early gastric cancer: A meta-analysis. World journal of gastrointestinal endoscopy 2014; 6: 555-563
• 3 Lian J, Chen S, Zhang Y. et al. A meta-analysis of endoscopic submucosal dissection and EMR for early gastric cancer. Gastrointestinal endoscopy 2012; 76: 763-770
• 4 Park YM, Cho E, Kang HY. et al. The effectiveness and safety of endoscopic submucosal dissection compared with endoscopic mucosal resection for early gastric cancer: a systematic review and metaanalysis. Surgical endoscopy 2011; 25: 2666-2677
• 5 Pimentel-Nunes P, Mourao F, Veloso N. et al. Long-term follow-up after endoscopic resection of gastric superficial neoplastic lesions in Portugal. Endoscopy 2014; 46: 933-940
• 6 Toyokawa T, Inaba T, Omote S. et al. Risk factors for non-curative resection of early gastric neoplasms with endoscopic submucosal dissection: Analysis of 1,123 lesions. Experimental and therapeutic medicine 2015; 9: 1209-1214
• 7 Hirasawa K, Kokawa A, Oka H. et al. Risk assessment chart for curability of early gastric cancer with endoscopic submucosal dissection. Gastrointestinal endoscopy 2011; 74: 1268-1275
• 8 Lucas PJ, van der Gaag LC, Abu-Hanna A. Bayesian networks in biomedicine and health-care. Artificial intelligence in medicine 2004; 30: 201-214
• 9 Japanese Gastric Cancer Association. Japanese gastric cancer treatment guidelines 2010 (ver. 3). Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association 2011; 14: 113-123
• 10 Lambert R, Lightdale CJ, Lambert R. et al. The Paris endoscopic classification of superficial neoplastic lesions: esophagus, stomach, and colon: November 30 to December 1, 2002. Gastrointestinal endoscopy 2003; 58: S3-43
• 11 Hoff P. A first course in Bayesian statistical methods. New York, NY: Springer New York; 2009
• 12 R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
• 13 Aragon T. Epitools: Epidemiology tools. In 2012
• 14 Scutari M. Learning Bayesian Networks with the bnlearn R Package. J Stat Softw 2010; 35: 22
• 15 Søren H. Graphical independence networks with the gRain package for R. J Stat Softw 2012; 46: 1-26
• 16 Lauritzen DJ, Spiegelhalter SL. Local computations with probabilities on graphical structures and their application to expert systems. J R Stat Soc Ser B 1988; 50: 157-224
• 17 Robin X, Turck N, Hainard A. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC bioinformatics 2011; 12: 77
• 18 Darwiche A. Modeling and Reasoning with Bayesian Networks. Cambridge University Press; 2009
• 19 Choi J, Kim SG, Im JP. et al. Endoscopic prediction of tumor invasion depth in early gastric cancer. Gastrointestinal endoscopy 2011; 73: 917-927
• 20 Choi J, Kim SG, Im JP. et al. Comparison of endoscopic ultrasonography and conventional endoscopy for prediction of depth of tumor invasion in early gastric cancer. Endoscopy 2010; 42: 705-713
• 21 Libanio D, Costa MN, Pimentel-Nunes P. et al. Risk factors for bleeding after gastric endoscopic submucosal dissection: a systematic review and meta-analysis. Gastrointestinal endoscopy 2016;