Author: Qianxi Ni, Qionghui Zhou 👨🔬
Affiliation: The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University 🌍
Purpose:
This study aims to develop and evaluate interpretable machine learning models that use radiomic and dosimetric features to predict HT in advanced cervical cancer patients.
Methods:
Retrospectively collected general clinical data, planning CT images, and dose files from 205 patients with advanced cervical cancer who underwent chemoradiotherapy, and classified them according to the severity of HT. Radiomics and dosiomics features were extracted from the same region of interest, and feature selection was performed using a random forest algorithm. Radiomics models, dosiomics models, and hybrid models were then constructed based on XGBoost. Sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were calculated to evaluate the classification performance of the models. Finally, SHAP values were used to perform interpretability analysis on the best model to enhance the transparency and practicality of the model.
Results:
The sensitivity, specificity, and AUC values for the radiomics model were 0.42, 0.86, and 0.78, respectively, while those for the dosiomics model were 0.50, 0.90, and 0.74. In contrast, the hybrid model exhibited superior classification performance with sensitivity, specificity, and AUC values of 0.50, 0.83, and 0.83, respectively. Compared to the standalone radiomics and dosiomics models, the hybrid model demonstrated enhanced classification capability. Interpretability analysis based on SHAP values not only provided a ranking of feature importance and the distribution of feature impacts on model outputs but also elucidated the specific decision-making processes influenced by these features and the interactions between them.
Conclusion:
For patients with advanced cervical cancer undergoing chemoradiotherapy, the integration of radiomics and dosiomics features can significantly enhance the classification performance of predictive models, thereby holding considerable potential for refining patient treatment strategies. Interpretability analysis based on SHAP values can aid clinicians in more readily understanding the model's decisions, thus promoting the effective implementation of the model in clinical practice.