Cancer Res Treat.  2023 Oct;55(4):1240-1249. 10.4143/crt.2022.1330.

Development and Validation of Models to Predict Lymph Node Metastasis in Early Gastric Cancer Using Logistic Regression and Gradient Boosting Machine Methods

• Lee HD
• Nam KH
• Shin CM
• Lee HS
• Chang YH
• Yoon H
• Park YS
• Kim N
• Lee DH
• Ahn SH
• Kim HH

• KMID: 2547798
• DOI: http://doi.org/10.4143/crt.2022.1330

Abstract

Purpose
To identify important features of lymph node metastasis (LNM) and develop a prediction model for early gastric cancer (EGC) using a gradient boosting machine (GBM) method.
Materials and Methods
The clinicopathologic data of 2556 patients with EGC who underwent gastrectomy were used as training set and the internal validation set (set 1) at a ratio of 8:2. Additionally, 548 patients with EGC who underwent endoscopic submucosal dissection (ESD) as the initial treatment were included in the external validation set (set 2). The GBM model was constructed, and its performance was compared with that of the Japanese guidelines.
Results
LNM was identified in 12.6% (321/2556) of the gastrectomy group (training set & set 1) and 4.3% (24/548) of the ESD group (set 2). In the GBM analysis, the top five features that most affected LNM were lymphovascular invasion, depth, differentiation, size, and location. The accuracy, sensitivity, specificity, and the area under the receiver operating characteristics of set 1 were 0.566, 0.922, 0.516, and 0.867, while those of set 2 were 0.810, 0.958, 0.803, and 0.944, respectively. When the sensitivity of GBM was adjusted to that of Japanese guidelines (beyond the expanded criteria in set 1 [0.922] and eCuraC-2 in set 2 [0.958]), the specificities of GBM in sets 1 and 2 were 0.516 (95% confidence interval, 0.502-0.523) and 0.803 (0.795-0.805), while those of the Japanese guidelines were 0.502 (0.488-0.509) and 0.788 (0.780-0.790), respectively.
Conclusion
The GBM model showed good performance comparable with the eCura system in predicting LNM risk in EGCs.

Keyword

Early gastric cancer; Lymphatic metastasis; Prediction; Machine learning

Figure

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  Fig. 1 Flowchart of the study. ESD, endoscopic submucosal dissection; F/U, follow-up.

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  Fig. 2 SHAP (SHapley Additive exPlanations) value of features (A) and feature importance (B) of gradient boosting machine analysis. The SHAP value shows how each feature affects the lymph node metastasis (LNM) according to the feature value (A). Red color indicates high feature value, while the SHAP value indicates the level of influence on the model output. Plotting of feature importance as the average of absolute values of SHAP values has the advantage of superior consistency compared to feature importance calculated by several other criteria (B). LV, lymphovascular.

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  Fig. 3 Receiver operating characteristic curves of the logistic regression (LR) model, gradient boosting machine (GBM) model, and expanded criteria for predicting lymph node metastasis for the internal validation set (set 1, n=512). AUC, area under the curve.

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  Fig. 4 Receiver operating characteristic curves of the logistic regression (LR) model, gradient boosting machine (GBM) model, and recent Japanese Gastric Cancer Association guidelines (eCuraC-2) for predicting lymph node metastasis for the external validation set (set 2, n=548). AUC, area under the curve.

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  Fig. 5 Examples of individual explanations to predict the risk of lymph node metastasis (LNM). In patient #1 (A), LNM risk was predicted to be positive by the gradient boosting machine model because the f(x) value (0.45) was larger than the base value (0.42); the case was subsequently confirmed to be LNM positive. In patient #2 (B), LNM risk was predicted to be negative because the final f(x) value (–1.55) was less than the base value (0.42); the case was subsequently confirmed to be LNM negative. LV, lymphovascular; MD, moderately differentiated; PD, poorly differentiated; SM1, submucosa 1; SM2, submucosa 2; WD, well differentiated.

Reference

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