Risk of genitourinary late effects after radiotherapy for prostate cancer associated with early changes in bladder shape

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s): not specified
• Date: October 2025
• Article Number: 100855
• DOI: 10.1016/j.phro.2025.100855
• ISSN: 2405-6316
• Journal: Physics and Imaging in Radiation Oncology
• Open Access: yes
• Pages: 5
• Volume: 36
• Publisher: Elsevier Inc.
• Keywords: Adaptive radiotherapy, Machine learning, Prostate cancer radiotherapy, Radiation-induced late effects

Abstract

Background and purpose: The risk of genitourinary late effects is a major dose-limiting factor in radiotherapy for prostate cancer. By using shape analysis and machine learning, the aim of this study was to evaluate whether bladder shape descriptors from the first week of treatment could identify patients experiencing genitourinary late effects. Material and methods: From a cohort of 258 prostate cancer patients treated with daily cone-beam computed tomography (CBCT)-guided radiotherapy (prescription doses of 77.4–81.0 Gy), 7 pre-treatment asymptomatic cases experiencing RTOG genitourinary late effects ≥Grade 2 and 21 matched controls were selected. The bladder was manually contoured on each CBCT, and a 17-D vector comprising shape descriptors was used for patient clustering, focusing on bladder contours from the first week of treatment. ANOVA was used to test statistical significance of descriptors across and within clusters. Results: Of the contours from the first week of treatment, 84 % could be classified in two main clusters with distinct bladder shape characteristics. This cluster stratification remained identical when bladder contours from the entire course of treatment were used. Convexity, elliptic variance and compactness were significantly different between patients with vs. without genitourinary late effects ≥Grade 2 (p < 0.05). Dice Coefficients between predictive models using descriptors of the first week and the voxels’ probability of belonging to the bladder were above 93 ± 6 % (median ± interquartile range). Conclusion: Bladder shape descriptors in the first week of treatment showed potential to predict the risk of developing genitourinary late effects after radiotherapy for prostate cancer.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.1016/j.phro.2025.100855

BibTeX

@article{casares-magaz-2025-rog,
  title =      "Risk of genitourinary late effects after radiotherapy for
               prostate cancer associated with early changes in bladder
               shape",
  author =     "Oscar Casares-Magaz and Renata Raidou and Katar\'{i}­na
               Furmanov\'{a} and Niclas Pettersson and Vitali Moiseenko and
               John Einck and Austin Hopper and Rick Knopp and Ludvig P.
               Muren",
  year =       "2025",
  abstract =   "Background and purpose: The risk of genitourinary late
               effects is a major dose-limiting factor in radiotherapy for
               prostate cancer. By using shape analysis and machine
               learning, the aim of this study was to evaluate whether
               bladder shape descriptors from the first week of treatment
               could identify patients experiencing genitourinary late
               effects. Material and methods: From a cohort of 258 prostate
               cancer patients treated with daily cone-beam computed
               tomography (CBCT)-guided radiotherapy (prescription doses of
               77.4–81.0 Gy), 7 pre-treatment asymptomatic cases
               experiencing RTOG genitourinary late effects ≥Grade 2 and
               21 matched controls were selected. The bladder was manually
               contoured on each CBCT, and a 17-D vector comprising shape
               descriptors was used for patient clustering, focusing on
               bladder contours from the first week of treatment. ANOVA was
               used to test statistical significance of descriptors across
               and within clusters. Results: Of the contours from the first
               week of treatment, 84 % could be classified in two main
               clusters with distinct bladder shape characteristics. This
               cluster stratification remained identical when bladder
               contours from the entire course of treatment were used.
               Convexity, elliptic variance and compactness were
               significantly different between patients with vs. without
               genitourinary late effects ≥Grade 2 (p < 0.05). Dice
               Coefficients between predictive models using descriptors of
               the first week and the voxels’ probability of belonging to
               the bladder were above 93 ± 6 % (median ± interquartile
               range). Conclusion: Bladder shape descriptors in the first
               week of treatment showed potential to predict the risk of
               developing genitourinary late effects after radiotherapy for
               prostate cancer.",
  month =      oct,
  articleno =  "100855",
  doi =        "10.1016/j.phro.2025.100855",
  issn =       "2405-6316",
  journal =    "Physics and Imaging in Radiation Oncology",
  pages =      "5",
  volume =     "36",
  publisher =  "Elsevier Inc.",
  keywords =   "Adaptive radiotherapy, Machine learning, Prostate cancer
               radiotherapy, Radiation-induced late effects",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2025/casares-magaz-2025-rog/",
}