Development of an Ensemble Machine Learning-Based Graphical User Interface (GUI) Tool for Predicting Radiation Pneumonitis in Locally Advanced Squamous Cell Lung Cancer Patients Undergoing Concurrent Chemoradiotherapy
Authors: Li Xiadong , Wang Jinhu , Zhang Qianqian , Xiong Xi'an , Huang Shixiong , Zhou xing , He Jingtao , Yu Wu , Tang Shiqiang , Tian Wei , Huang Jian , Yu Gang , Ni Qianxi 
Published: September 2024

Background
Predicting the development of grade II or higher radiation pneumonitis (RP) in locally advanced squamous cell lung cancer (LASCLC) patients prior to concurrent chemoradiotherapy remains challenging, as traditional indicators based on dose-volume histograms or biological markers typically lack data or assessed post-treatment. In this study, we explored the potential of utilizing multi-omics (radiomics, dosimetric, clinical, and radiobiology features) as novel biomarkers to predict the occurrence of grade II or higher RP in LASCLC patients undergoing concurrent chemoradiotherapy.
Methods
In this study, we enrolled a total of 129 LASCLC patients who underwent post-concurrent chemoradiotherapy treatment at four participating institutions as a training and validation cohort, and an additional 34 participants were enrolled in an independent test set. Four distinct types of features were utilized for training and validating the predictive model, including radiomics features, dosimetry factors, clinical factors, and radiobiological factors. We applied a four-step feature selection algorithm for pre-feature selection, and three machine learning models with the best prediction performance were selected for the ensemble machine learning model construction. Next, we utilized Shapley Additive explanations (SHAP) values to interpret the output features of the machine learning model. Finally, a user-friendly graphical user interface (GUI) was developed for clinical applications.
Results
Nearly 51.3% of the enrolled patients experienced RP2+, univariate analysis showed statistically significant differences in Smoking status, Radiotherapy Position (RTP), Lungs_V5, PTV_Volume, Heart_V30, LEUD(a = 0.3) _SICK, LEUD(a = 0.3) _TOTAL, NTCP_LEUD_SICK, and NTCP_LKB_ SICK between patients with and without RP2+. Nine features were ultimately selected for machine learning model training and verification, including three dosimetric features, three radiomic features, and three radiobiological features. Two dosimetric features (Lung_V5 and Lung_V20), one radiobiological feature (NTCP_LEUD_Sick), and one radiomic feature (glcmInverseVariance_PGTV) consistently exhibit strong predictive ability for RP2 + across all 9 machine learning models, with average AUC values exceeding 0.7. the combination of radiobiological parameters and radiomic features (RM + RB signature) achieved the best model generalization ability (MGA) values across all three machine learning models, with MGA values exceeding 0.92. This performance was closely followed by radiobiological features alone, which attained MGA values above 0.90. Among RM + RB signature, three contribute negatively, while the other three contribute positively to the prediction of RP2+. Specifically, a smaller SHAP value for NTCP_LEUD_SICK indicates a lower probability of developing RP2+.
Conclusions
We proposed a user-friendly GUI. This innovative approach greatly facilitates the practical application of the model by clinical practitioners and enhances clinical decision-making processes.