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Risk-Scoring System for Prediction of Non-Curative Endoscopic Submucosal Dissection Requiring Additional Gastrectomy in Patients with Early Gastric Cancer

  • Kim, Tae-Se (Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Min, Byung-Hoon (Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Kim, Kyoung-Mee (Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Yoo, Heejin (Biomedical Statistics Center, Data Science Research Institute, Research Institute for Future Medicine, Samsung Medical Center) ;
  • Kim, Kyunga (Biomedical Statistics Center, Data Science Research Institute, Research Institute for Future Medicine, Samsung Medical Center) ;
  • Min, Yang Won (Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Lee, Hyuk (Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Rhee, Poong-Lyul (Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Kim, Jae J. (Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Lee, Jun Haeng (Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine)
  • Received : 2021.07.30
  • Accepted : 2021.11.02
  • Published : 2021.12.31

Abstract

Purpose: When patients with early gastric cancer (EGC) undergo non-curative endoscopic submucosal dissection requiring gastrectomy (NC-ESD-RG), additional medical resources and expenses are required for surgery. To reduce this burden, predictive model for NC-ESD-RG is required. Materials and Methods: Data from 2,997 patients undergoing ESD for 3,127 forceps biopsy-proven differentiated-type EGCs (2,345 and 782 in training and validation sets, respectively) were reviewed. Using the training set, the logistic stepwise regression analysis determined the independent predictors of NC-ESD-RG (NC-ESD other than cases with lateral resection margin involvement or piecemeal resection as the only non-curative factor). Using these predictors, a risk-scoring system for predicting NC-ESD-RG was developed. Performance of the predictive model was examined internally with the validation set. Results: Rate of NC-ESD-RG was 17.3%. Independent pre-ESD predictors for NC-ESD-RG included moderately differentiated or papillary EGC, large tumor size, proximal tumor location, lesion at greater curvature, elevated or depressed morphology, and presence of ulcers. A risk-score was assigned to each predictor of NC-ESD-RG. The area under the receiver operating characteristic curve for predicting NC-ESD-RG was 0.672 in both training and validation sets. A risk-score of 5 points was the optimal cut-off value for predicting NC-ESD-RG, and the overall accuracy was 72.7%. As the total risk score increased, the predicted risk for NC-ESD-RG increased from 3.8% to 72.6%. Conclusions: We developed and validated a risk-scoring system for predicting NC-ESD-RG based on pre-ESD variables. Our risk-scoring system can facilitate informed consent and decision-making for preoperative treatment selection between ESD and surgery in patients with EGC.

Keywords

References

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