Integrating Audiovisual Immersion Into Pediatric Radiation Therapy Across Multiple Centers: Methodology, Timeliness, and Cost of the Audiovisual-Assisted Therapeutic Ambience in Radiation Therapy Prospective Multi-Institutional Trial.

Purpose: The Audiovisual-Assisted Therapeutic Ambience in Radiotherapy (AVATAR) trial was a prospective multicenter study (NCT03991156) examining the combination of video immersion with radiation therapy and was successfully conducted through the collaboration of pediatric radiation oncology teams at 10 institutions independent of any pre-existing consortium. We sought to analyze and report the methodology of trial conception and development, process map, and cost.

Methods And Materials: The study enrolled patients aged 3 to 10 years preparing to undergo radiation therapy, integrated the combination of AVATAR-based video immersion with radiation therapy at each institution, and offered AVATAR use as an alternative to anesthesia, with rates of anesthesia use and outcomes of serial standardized anxiety and quality-of-life assessments assessed among the 81 children enrolled.

CT- and MR-based image-based data mining are consistent in the brain.

Purpose: Image-based data mining (IBDM) is a voxel-based analysis technique to investigate dose-response. Most often, IBDM uses radiotherapy planning CTs because of their broad accessibility, however, it was unknown whether CT provided sufficient soft tissue contrast for brain IBDM. This study evaluates whether MR-based IBDM improves upon CT-based IBDM for studies of children with brain tumours.

Validation of an automated contouring and treatment planning tool for pediatric craniospinal radiation therapy.

Purpose: Treatment planning for craniospinal irradiation (CSI) is complex and time-consuming, especially for resource-constrained centers. To alleviate demanding workflows, we successfully automated the pediatric CSI planning pipeline in previous work. In this work, we validated our CSI autosegmentation and autoplanning tool on a large dataset from St.

Three-dimensional dose and LET prediction in proton therapy using artificial neural networks.

Purpose: Challenges in proton therapy include identifying patients most likely to benefit; ensuring consistent, high-quality plans as its adoption becomes more widespread; and recognizing biological uncertainties that may be related to increased relative biologic effectiveness driven by linear energy transfer (LET). Knowledge-based planning (KBP) is a domain that may help to address all three.

Methods: Artificial neural networks were trained using 117 unique treatment plans and associated dose and dose-weighted LET (LET ) distributions.