Dose Prediction with Deep Learning for Prostate Cancer Radiation Therapy: Model Adaptation to Different Treatment Planning Practices

This work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy (VMAT) for prostate cancer and to adapt the model to three different internal treatment planning styles and one external institution planning style. We built the source model with planning data from 108 patients previously treated with VMAT for prostate cancer. For the transfer learning, we selected patient cases planned with three different styles from the same institution and one style from a different institution to adapt the source model to four target models. We compared the dose distributions predicted by the source model and the target models with the clinical dose predictions and quantified the improvement in the prediction quality for the target models over the source model using the Dice similarity coefficients (DSC) of 10% to 100% isodose volumes and the dose-volume-histogram (DVH) parameters of the planning target volume and the organs-at-risk. The source model accurately predicts dose distributions for plans generated in the same source style but performs sub-optimally for the three internal and one external target styles, with the mean DSC ranging between 0.81-0.94 and 0.82-0.91 for the internal and the external styles, respectively. With transfer learning, the target model predictions improved the mean DSC to 0.88-0.95 and 0.92-0.96 for the internal and the external styles, respectively. Target model predictions significantly improved the accuracy of the DVH parameter predictions to within 1.6%. We demonstrated model generalizability for DL-based dose prediction and the feasibility of using transfer learning to solve this problem. With 14-29 cases per style, we successfully adapted the source model into several different practice styles. This indicates a realistic way to widespread clinical implementation of DL-based dose prediction

Expression of SUMO enzymes is fiber type dependent in skeletal muscles and is dysregulated in muscle disuse

SUMOylation is a dynamic, reversible, enzymatic drug-targetable post-translational modification (PTM) reaction where the Small Ubiquitin-like Modifier (SUMO) moieties are attached to proteins. This reaction regulates various biological functions like cell growth, differentiation, and it is crucial for maintaining organ homeostasis. However, the actions of SUMO in skeletal muscle pathophysiology are still not investigated. In this study, we quantified the abundance of the SUMO enzymes and determined the distribution of SUMOylated proteins along the fibers of nine different muscles. We find that skeletal muscles contain a distinctive group of SUMO enzymes and SUMOylated proteins in relation to their different metabolism, functions, and fiber type composition. In addition, before the activation of protein degradation pathways, this unique set is quickly altered in response to muscle sedentariness. Finally, we demonstrated that PAX6 acts as an upstream regulator of the SUMO conjugation reaction, which can become a potential therapeutic marker to prevent muscle diseases generated by inactivity