Magnetic resonance imaging radiomic feature analysis of radiation-induced femoral head changes in prostate cancer radiotherapy

Background and Purpose: As a feasible approach, radiotherapy has a great role in prostate cancer (Pca) management. However, Pca patients have an increased risk of femoral head damages including fractures after radiotherapy. The mechanisms of these complications are unknown and time of manifestations is too long; however, they may be predicted by early imaging. The main purpose of this study was to assess the early changes in femoral heads in Pca patients treated with intensity-modulated radiation therapy (IMRT) using multiparametric magnetic resonance imaging (mpMRI) radiomic feature analysis. Materials and Methods: Thirty Pca patients treated with IMRT were included in the study. All patients underwent two mpMRI pre- and postradiotherapy. Thirty-four robust radiomic features were extracted from T1, T2, and apparent diffusion coefficient (ADC) obtained from diffusion-weighted images. Wilcoxon signed-rank test was performed to assess the significance of the change in the mean T1, T2, and ADC radiomic features postradiotherapy relative to preradiotherapy values. The percentage change values were normalized based on the natural logarithm base ten. Features were also ranked based on their median changes. Results: Sixty femoral heads were analyzed. All radiomic features have undergone changes. Significant postradiotherapy radiomic feature changes were observed in 20 and 5 T1- and T2-weighted radiomic features, respectively (P < 0.05). ADC features did not vary significantly postradiotherapy. The mean radiation dose received by femoral heads was 40 Gy. No fractures were observed within the follow-up time. Different features were found as high ranked among T1, T2, and ADC images. Conclusion: Early structural change analysis using radiomic features may contribute to predict postradiotherapy fracture in Pca patients. These features can be identified as being potentially important imaging biomarkers for predicting radiotherapy-induced femoral changes. © 2019 Journal of Cancer Research and Therapeutics | Published by Wolters Kluwer - Medknow

Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy

In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians