Publications by authors named "T Purdie"
Artificial Intelligence for Radiation Treatment Planning: Bridging Gaps From Retrospective Promise to Clinical Reality.
Clin Oncol (R Coll Radiol)
January 2025
Artificial intelligence (AI) radiation therapy (RT) planning holds promise for enhancing the consistency and efficiency of the RT planning process. Despite technical advancements, the widespread integration of AI into RT treatment planning faces challenges. The transition from controlled retrospective environments to real-world clinical settings introduces heightened scrutiny from clinical end users, potentially leading to decreased clinical acceptance.
View Article and Find Full Text PDFBackground And Purpose: No best practices currently exist for achieving high quality radiation therapy (RT) treatment plan adaptation during magnetic resonance (MR) guided RT of prostate cancer. This study validates the use of machine learning (ML) automated RT treatment plan adaptation and benchmarks it against current clinical RT plan adaptation methods.
Materials And Methods: We trained an atlas-based ML automated treatment planning model using reference MR RT treatment plans (42.
Article Synopsis
- This study looked at whether radiotherapy could help reduce pain from liver cancer in patients who weren't getting better with regular treatments.
- It included 66 patients who were given either radiotherapy and extra care or just extra care alone.
- The researchers wanted to see if the patients felt less pain after a month and found out if the treatment was safe for them.
Purpose: The capacity for machine learning (ML) to facilitate radiation therapy (RT) planning for primary brain tumors has not been described. We evaluated ML-assisted RT planning with regard to clinical acceptability, dosimetric outcomes, and planning efficiency for adults and children with primary brain tumors.
Methods And Materials: In this prospective study, children and adults receiving 54 Gy fractionated RT for a primary brain tumor were enrolled.