Mitigating Risks in Cone Beam Computed Tomography Guided Online Adaptive Radiation Therapy: A Preventative Reference Planning Review Approach.

Purpose: Online adaptive radiation therapy (oART) treatment planning requires evaluating the temporal robustness of reference plans and anticipating the potential changes during treatment courses that may even lead to risks unique to the adaptive workflow. This study conducted a risk analysis of the cone beam computed tomography guided adaptive workflow and is the first to assess an adaptive-specific reference planning review that mitigates risk in the planning process to prevent events and treatment deficiencies during adaptation.

Methods And Materials: A quality management team of medical physicists, residents, physicians, and radiation therapists performed a fault tree analysis and failure mode and effects analysis.

Impact of Manual Contour Editing on Plan Quality for Online Adaptive Radiation Therapy for Head and Neck Cancer.

Purpose: Online adaptive radiation therapy (oART) has high resource costs especially for head and neck (H&N) cancer, which requires recontouring complex targets and numerous organs-at-risk (OARs). Adaptive radiation therapy systems provide autocontours to help. We aimed to explore the optimal level of editing automatic contours to maintain plan quality in a cone beam computed tomography-based oART system for H&N cancer.

The accuracy of artificial intelligence deformed nodal structures in cervical online cone-beam-based adaptive radiotherapy.

Background And Purpose: Online cone-beam-based adaptive radiotherapy (ART) adjusts for anatomical changes during external beam radiotherapy. However, limited cone-beam image quality complicates nodal contouring. Despite this challenge, artificial-intelligence guided deformation (AID) can auto-generate nodal contours.

Evaluating machine learning enhanced intelligent-optimization-engine (IOE) performance for ethos head-and-neck (HN) plan generation.

Purpose: Varian Ethos utilizes novel intelligent-optimization-engine (IOE) designed to automate the planning. However, this introduced a black box approach to plan optimization and challenge for planners to improve plan quality. This study aims to evaluate machine-learning-guided initial reference plan generation approaches for head & neck (H&N) adaptive radiotherapy (ART).

Radiotherapy-Compatible Robotic System for Multi-Landmark Positioning in Head and Neck Cancer Treatments.

The spine flexibility creates one of the most significant challenges to proper positioning in radiation therapy of head and neck cancers. Even though existing immobilization techniques can reduce the positioning uncertainty, residual errors (2-3 mm along the cervical spine) cannot be mitigated by single translation-based approaches. Here, we introduce a fully radiotherapy-compatible electro-mechanical robotic system, capable of positioning a patient's head with submillimeter accuracy in clinically acceptable spatial constraints.