Standardizing and improving dose predictions for head and neck cancers using complete sets of OAR contours.

Background: Radiotherapy dose predictions have been trained with data from previously treated patients of similar sites and prescriptions. However, clinical datasets are often inconsistent and do not contain the same number of organ at risk (OAR) structures. The effects of missing contour data in deep learning-based dose prediction models have not been studied.

Dosimetric characterization of a new surface-conforming electron MLC prototype.

The purpose is to reduce normal tissue radiation toxicity for electron therapy through the creation of a surface-conforming electron multileaf collimator (SCEM). The SCEM combines the benefits of skin collimation, electron conformal radiotherapy, and modulated electron radiotherapy. An early concept for the SCEM was constructed.

Monte Carlo modeling of the Elekta Versa HD and patient dose calculation with EGSnrc/BEAMnrc.

Introduction: Numerous studies have proven the Monte Carlo method to be an accurate means of dose calculation. Although there are several commercial Monte Carlo treatment planning systems (TPSs), some clinics may not have access to these resources. We present a method for routine, independent patient dose calculations from treatment plans generated in a commercial TPS with our own Monte Carlo model using free, open-source software.

Artificial intelligence based deconvolving on megavoltage photon beam profiles for radiotherapy applications.

. The aim of this work is an AI based approach to reduce the volume effect of ionization chambers used to measure high energy photon beams in radiotherapy. In particular for profile measurements, the air-filled volume leads to an inaccurate measurement of the penumbra.

Latent space arc therapy optimization.

Volumetric modulated arc therapy planning is a challenging problem in high-dimensional, non-convex optimization. Traditionally, heuristics such as fluence-map-optimization-informed segment initialization use locally optimal solutions to begin the search of the full arc therapy plan space from a reasonable starting point. These routines facilitate arc therapy optimization such that clinically satisfactory radiation treatment plans can be created in a reasonable time frame.

A sensitivity analysis of probability maps in deep-learning-based anatomical segmentation.

Purpose: Deep-learning-based segmentation models implicitly learn to predict the presence of a structure based on its overall prominence in the training dataset. This phenomenon is observed and accounted for in deep-learning applications such as natural language processing but is often neglected in segmentation literature. The purpose of this work is to demonstrate the significance of class imbalance in deep-learning-based segmentation and recommend tuning of the neural network optimization objective.

Incorporating biological modeling into patient-specific plan verification.

Purpose: Dose-volume histogram (DVH) measurements have been integrated into commercially available quality assurance systems to provide a metric for evaluating accuracy of delivery in addition to gamma analysis. We hypothesize that tumor control probability and normal tissue complication probability calculations can provide additional insight beyond conventional dose delivery verification methods.

Methods: A commercial quality assurance system was used to generate DVHs of treatment plan using the planning CT images and patient-specific QA measurements on a phantom.

Evaluation of the Elekta Agility MLC performance using high-resolution log files.

Purpose: With the advent of volumetric modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT) treatment techniques, the requirement for more elaborate approaches in reviewing linac components' integrity has become even more stringent. A possible solution to this challenge is to employ the usage of log files generated during treatment. The log files generated by the new generation of Elekta linacs record events at a higher frequency (25 Hz) than their predecessors, which allows for retrospective analysis and identification of subtle changes and provides another means of quality assurance.