Automation Radiomics in Predicting Radiation Pneumonitis (RP)

Abstract

Radiomics has shown great promise in predicting various diseases. Researchers have previously attempted to include radiomics in their automated detection, diagnosis, and segmentation algorithms, taking these steps based on the promising outcomes of radiomics-based studies. As a result of the increased attention given to this topic, numerous institutions have developed their own radiomics software. These packages, on the other hand, have been utilized interchangeably without regard for their fundamental differences. The primary purpose of this study was to explore benefits of predictive model performance for radiation pneumonitis (RP), which is the most frequent side effect of chest radiotherapy, and through this work, we developed a radiomics model based on deep learning that intends to increase RP prediction performance by combining more data points and digging deeper into these data. In order to evaluate the most popular machine learning models, radiographic characteristics were used, and we recorded the most important of them. The high dimensionality of radiomic datasets is a major issue. The method proposed for use in data problems is the synthetic minority oversampling technique, which we used in order to create a balanced dataset by leveraging suitable hardware and open-source software. The present study assessed the efficacy of various machine learning models, including logistic regression (LR), support vector machine (SVM), random forest (RF), and deep neural network (DNN), in predicting radiation pneumonitis by utilizing specific radiomics features. The findings of the study indicate that the four models displayed satisfactory efficacy in forecasting radiation pneumonitis. The DNN model demonstrated the highest area under the receiver operating curve (AUC-ROC) value, which was 0.87, suggesting its superior predictive capacity among the models considered. The AUC-ROC values for the random forest, SVM, and logistic regression models were 0.85, 0.83, and 0.81, respectively.

From pixels to prognosis: unveiling radiomics models with SHAP and LIME for enhanced interpretability

“…In conclusion, the research, titled 'From Pixels to Prognosis: Unveiling Radiomics Models with SHAP and LIME for Enhanced Interpretability,' lies at the intersection of state-of-the-art medical imaging techniques, advanced machine learning algorithms, and clinical decision-making processes. By combining radiomic parameters from PET/CT images and clinical information, we have gained a comprehensive understanding of predicting radiation pneumonitis [31]. Our methodology surpasses existing approaches by utilizing a wide range of data streams to extract valuable information.…”

“…This measures how well the model can differentiate between pneumonitis and non-pneumonitis across different imaging modalities. The integration of AUC-ROC scores derived from many modalities provides a comprehensive evaluation of prediction accuracy, capturing the interaction between various radiomic features [31]. Acknowledging the clinical importance of sensitivity, specificity, and the F1-score, we expanded the application of these metrics to incorporate multi-modal contributions.…”