Impact of Artificial Intelligence-Based Autosegmentation of Organs at Risk in Low- and Middle-Income Countries.

Purpose: Radiation therapy (RT) processes require significant human resources and expertise, creating a barrier to rapid RT deployment in low- and middle-income countries (LMICs). Accurate segmentation of tumor targets and organs at risk (OARs) is crucial for optimal RT. This study assessed the impact of artificial intelligence (AI)-based autosegmentation of OARs in 2 LMICs.

Setting the Stage: Feasibility and Baseline Characteristics in the PARTIQoL Trial Comparing Proton Therapy Versus Intensity Modulated Radiation Therapy for Localized Prostate Cancer.

Purpose: Men with localized prostate cancer may receive either photon-based intensity modulated radiation therapy (IMRT) or proton beam therapy (PBT). The PARTIQoL trial (NCT01617161) demonstrates the feasibility of performing a large, multicenter phase 3 randomized trial comparing IMRT with PBT for localized prostate cancer. Here, we report baseline features of patients enrolled on this trial and present strategies to improve feasibility of other similar trials.

Large-language-model empowered 3D dose prediction for intensity-modulated radiotherapy.

Background: Treatment planning is currently a patient specific, time-consuming, and resource demanding task in radiotherapy. Dose-volume histogram (DVH) prediction plays a critical role in automating this process. The geometric relationship between DVHs in radiotherapy plans and organs-at-risk (OAR) and planning target volume (PTV) has been well established.