Refining prognostic stratification in salvage radiotherapy for prostate cancer: a retrospective multicenter cohort study using PSMA-PET and machine learning.

Background: Salvage radiotherapy (SRT) is the standard treatment for biochemical recurrence (bREC) after radical prostatectomy (RP), yet optimal radiation dose, field size, and the role of advanced imaging like PSMA-PET remain unclear. This study assessed the impact of SRT dose and the prognostic role of PSMA-PET on 2-year biochemical relapse-free survival (bRFS) in patients with localized disease.

Methods: In this retrospective multicenter study, 255 patients treated with SRT across 11 centers were selected from a database of 1,201 cases.

Near-miss Event in Lung Cancer Radiation Therapy Because of a Random Outlier of Target Volume.

Accurate tumor and organ-at-risk contouring is a critical step in radiation therapy. Contouring procedures, both manual and automated, are prone to errors and to a large degree of interobserver and intraobserver variability. Radiation oncologists and/or medical physicists have to perform independent reviews of all contours for each patient before using them for treatment planning, which is a time-consuming, labor-intensive, and still not error-free process.

Machine-learning prediction of treatment response to stereotactic body radiation therapy in oligometastatic gynecological cancer: A multi-institutional study.

Background And Purpose: We aimed to develop and validate different machine-learning (ML) prediction models for the complete response of oligometastatic gynecological cancer after SBRT.

Material And Methods: One hundred fifty-seven patients with 272 lesions from 14 different institutions and treated with SBRT with radical intent were included. Thirteen datasets including 222 lesions were combined for model training and internal validation purposes, with an 80:20 ratio.