Effect of synthetic CT on dose-derived toxicity predictors for MR-only prostate radiotherapy.

Objectives: Toxicity-driven adaptive radiotherapy (RT) is enhanced by the superior soft tissue contrast of magnetic resonance (MR) imaging compared with conventional computed tomography (CT). However, in an MR-only RT pathway synthetic CTs (sCT) are required for dose calculation. This study evaluates 3 sCT approaches for accurate rectal toxicity prediction in prostate RT.

Application of failure mode and effects analysis to validate a novel hybrid Linac QC program that integrates automated and conventional QC testing.

A hybrid quality control (QC) program was developed that integrates automated and conventional Linac QC, realizing the benefits of both automated and conventional QC, increasing efficiency and maintaining independent measurement methods. Failure mode and effects analysis (FMEA) was then applied in order to validate the program prior to clinical implementation. The hybrid QC program consists of automated QC with machine performance check and DailyQA3 array on the TrueBeam Linac, and Delta4 volumetric modulated arc therapy (VMAT) standard plan measurements, alongside conventional monthly QC at a reduced frequency.

Neural network dose prediction for rectal spacer stratification in dose-escalated prostate radiotherapy.

Purpose: To develop a knowledge-based decision-support system capable of stratifying patients for rectal spacer (RS) insertion based on neural network predicted rectal dose, reducing the need for time- and resource-intensive radiotherapy (RT) planning.

Methods: Forty-four patients treated for prostate cancer were enrolled into a clinical trial (NCT03238170). Dose-escalated prostate RT plans were manually created for 30 patients with simulated boost volumes using a conventional treatment planning system (TPS) and used to train a hierarchically dense 3D convolutional neural network to rapidly predict RT dose distributions.