Artificial intelligence (AI) has made a tremendous impact in the space of healthcare, and proton therapy is not an exception. Proton therapy has witnessed growing popularity in oncology over recent decades, and researchers are increasingly looking to develop AI and machine learning tools to aid in various steps of the treatment planning and delivery processes. This review delves into the emergent role of AI in proton therapy, evaluating its development, advantages, intended clinical contexts, and areas of application.
View Article and Find Full Text PDFPurpose: Patients with p16 positive tonsillar cancer (p16 + TC) have an excellent prognosis and long-life expectancy. Deintensification of therapy is a prevalent topic of discussion. Proton radiotherapy is one way to reduce radiation exposure and thus reduce acute and late toxicity.
View Article and Find Full Text PDFBackground And Purpose: As no guidelines for pencil beam scanning (PBS) proton therapy (PT) of paediatric posterior fossa (PF) tumours exist to date, this study investigated planning techniques across European PT centres, with special considerations for brainstem and spinal cord sparing.
Materials And Methods: A survey and a treatment planning comparison were initiated across nineteen European PBS-PT centres treating paediatric patients. The survey assessed all aspects of the treatment chain, including but not limited to delineations, dose constraints and treatment planning.
Background: We retrospectively analyzed the 5-year biochemical disease-free survival (bDFS) and occurrence of late toxicity in prostate cancer patients treated with pencil beam scanning (PBS) proton radiotherapy.
Methodology: In the period from January 2013 to June 2018, 853 patients with prostate cancer were treated with an ultra-hypofractionated schedule (36.25 GyE/five fractions).