Prediction Model of Lymph Node Metastasis Risk in Elderly Patients with Early Gastric Cancer before Endoscopic Resection: A Retrospective Analysis Based on International Multicenter Data

Abstract

Background: Patients with early gastric cancer (EGC) must suffer reoperation if diagnosed with a high possibility of lymph node (LN) metastasis. The purpose of the current study was to develop and validate a model to predict the risk of LN metastasis in elderly patients before endoscopic resection. Methods: A total of 1911 EGC patients who had undergone radical surgery were selected and assigned randomly (2:1) to either the training cohort or the validation cohort. A nomogram was established based on the univariate and multivariate logistic regression models using the training cohort. Cox proportional hazards regression models were applied to identify the prognostic factors in univariate and multivariable analyses. Results: Three variables—tumor size, grade, and T stage—were derived from the multivariate analyses in the training cohort and incorporated into the nomogram. The AUC of the nomogram was 0.732 in the training cohort and 0.706 in the validation cohort. There were significant differences in survival among patients with different degrees of LN metastasis risk (training cohort: five-year disease-specific survival (DSS): low risk 88.1% vs. moderate risk 80.0% vs. high risk 72.9%, P < 0.001; validation cohort five-year DSS: low risk 89.0% vs. moderate risk 84.3% vs. high risk 72.2%, P < 0.001). The LN metastasis risk assessed from the model was also an independent prognostic factor. Conclusion: We established a nomogram that accurately predicts LN metastasis risk for elderly patients with EGC before endoscopic resection to avoid further injury from reoperation.

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

“…Larger tumor typically indicates a higher risk of LNM ( 86 – 88 ), which might be attributed to easiness of invasion for larger tumor to the surrounding tissues. Depth of tumor invasion was found to be a strong predictor in 32 models ( 32 , 34 – 37 , 39 , 41 , 42 , 46 , 49 , 50 , 52 , 54 – 56 , 62 , 69 – 71 ), which is in consistence with substantial evidence supporting its use as a predictor of LNM ( 36 , 63 , 89 – 91 ). There were 20 models considered histology differentiation as a vital factor for predicting LNM ( 34 – 37 , 41 , 42 , 48 – 55 , 61 – 63 ).…”

“…Depth of tumor invasion was found to be a strong predictor in 32 models ( 32 , 34 – 37 , 39 , 41 , 42 , 46 , 49 , 50 , 52 , 54 – 56 , 62 , 69 – 71 ), which is in consistence with substantial evidence supporting its use as a predictor of LNM ( 36 , 63 , 89 – 91 ). There were 20 models considered histology differentiation as a vital factor for predicting LNM ( 34 – 37 , 41 , 42 , 48 – 55 , 61 – 63 ). Deeply infiltrated and poorly differentiated tumors might have sufficient nutritional support to facilitate its invasion to tissues, capillaries and lymphatic vessels, and thus to have the potential to grow and metastasize faster ( 69 ).…”

“…All the included studies were of low risk of bias with respect to the domain of participants and outcome, 19 (46.34%) studies had low risk of bias in predictors ( 32 , 34 , 37 , 38 , 40 , 42 , 44 , 54 – 56 , 60 , 62 , 63 , 66 – 71 ), 22 (53.66%) had unclear risk of bias due to that the prediction assessment was performed in the know of outcome data ( 33 , 35 , 36 , 39 , 41 , 43 , 45 – 53 , 57 – 59 , 61 , 64 , 65 , 72 ). As for the domain of analysis, the risk of bias in 16 studies was considered high ( 33 , 35 , 41 , 43 , 47 , 50 , 51 , 53 , 58 , 59 , 61 , 63 , 65 , 68 , 70 , 71 ), and the reasons were that (1): Insufficient sample size.…”

“…Eight studies lacked external validation in model development ( 35 , 50 , 51 , 53 , 59 , 63 , 65 , 70 ). Concern regarding ‘overall applicability’ was rated as low in 15 studies (36.59%) ( 32 , 34 , 37 , 38 , 40 , 42 , 44 , 54 – 56 , 60 , 62 , 66 , 67 , 69 ), high in 16 studies (39.02%) ( 33 , 35 , 41 , 43 , 47 , 50 , 53 , 58 , 59 , 61 , 63 , 65 , 68 , 70 , 71 ) and unclear in the remaining 10 (24.39%) ( 36 , 39 , 45 , 46 , 48 , 49 , 52 , 57 , 64 , 72 ). Risk of bias and applicability assessment were shown in Table 2 .…”

“…Of the 41 included studies, 21 were early-gastric GC (EGC) (T1) studies ( 32 , 34 , 35 , 37 , 39 , 42 , 46 , 48 – 53 , 55 , 56 , 60 , 64 , 65 , 69 , 71 , 72 ) and 3 were advanced GC (T2-4) studies ( 43 , 45 , 67 ). In EGC, there was a pooled c-index of 0.832 [95%CI (0.804, 0.860)] ( Table 5 ; eFigure 13 ) and 0.795 [95%CI (0.755, 0.838)] ( eFigure 14 ) for the training and test groups, respectively.…”