Gamma pass rate classification prediction and interpretation based on SHAP value feature selection
10.3760/cma.j.cn113030-20230110-00003
- VernacularTitle:基于SHAP值特征选择的γ通过率分类预测及解释
- Author:
Luqiao CHEN
1
;
Qianxi NI
;
Jinmeng PANG
;
Jianfeng TAN
;
Xin ZHOU
;
Longjun LUO
;
Degao ZENG
;
Jinjia CAO
Author Information
1. 湖南省肿瘤医院/中南大学湘雅医学院附属肿瘤医院放疗科，长沙 410013
- Keywords:
Machine learning;
Intensity-modulated radiotherapy;
Feature selection;
Model interpretation;
Gamma pass rate
- From:
Chinese Journal of Radiation Oncology
2023;32(10):914-919
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the feasibility and validity of constructing an intensity-modulated radiotherapy gamma pass rate prediction model after combining the SHAP values with the extreme gradient boosting tree (XGBoost) algorithm feature selection technique, and to deliver corresponding model interpretation.Methods:The dose validation results of 196 patients with pelvic tumors receiving fixed-field intensity-modulated radiotherapy using modality-based measurements with a gamma pass rate criterion of 3%/2 mm and 10% dose threshold in Hunan Provincial Tumor Hospital from November 2020 to November 2021 were retrospectively analyzed. Prediction models were constructed by extracting radiomic features based on dose files and using SHAP values combined with the XGBoost algorithm for feature filtering. Four machine learning classification models were constructed when the number of features was 50, 80, 110 and 140, respectively. The area under the receiver operating characteristic curve (AUC), recall rate and F1 score were calculated to assess the classification performance of the prediction models.Results:The AUC of prediction model constructed with 110 features selected based on the SHAP-valued features was 0.81, the recall rate was 0.93 and the F1 score was 0.82, which were all better than the other 3 models.Conclusion:For intensity-modulated radiotherapy of pelvic tumor, SHAP values can be used in combination with the XGBoost algorithm to select the optimal subset of radiomic features to construct predictive models of gamma pass rates, and deliver an interpretation of the model output by SHAP values, which may provide value in understanding the prediction by machine learning-dependent models.
