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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Predicting prostate cancer recurrence using an atlas-based tumor control probability model.

Kazi Ridita Mahtaba1, Martin A Ebert2,3,4, Jeremy Booth1,5

  • 1Institute of Medical Physics, School of Physics, The University of Sydney, Camperdown, New South Wales, Australia.

Medical Physics
|January 14, 2026
PubMed
Summary
This summary is machine-generated.

This study developed an atlas-based model to predict prostate cancer recurrence after radiation therapy. The enhanced model accurately identified high-risk areas, potentially enabling personalized treatment to reduce recurrence without increasing toxicity.

Keywords:
prostate cancer recurrenceradiosensitivity parameterstumor control probability

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Area of Science:

  • Oncology
  • Radiation Oncology
  • Medical Physics

Background:

  • Prostate cancer (PCa) recurrence after radiation therapy (RT) is a significant clinical challenge.
  • Dose escalation in RT improves tumor control but increases toxicity.
  • Identifying radioresistant subvolumes could enable personalized RT to reduce treatment failure risk.

Purpose of the Study:

  • To evaluate an atlas-based tumor control probability (TCP) model for predicting PCa recurrence.
  • To retrospectively integrate patient-specific RT and histopathology data.
  • To enhance recurrence prediction using segment-wise adjustments to the TCP model.

Main Methods:

  • Utilized data from nine patients with biopsy-proven local recurrence after definitive RT.
  • Deformably registered population-based CD-atlas and TP-atlas to patient prostate contours.
  • Segmented atlases based on histopathology, derived radiosensitivity parameters (α/β ratios), and evaluated three adjustment approaches.

Main Results:

  • The combined cell density and Gleason Score (GS)-dependent α/β adjustment approach significantly reduced overall TCP (p=0.004).
  • This approach aligned lower TCP regions with 78% of relapsed tumor sites.
  • Voxel-level analysis confirmed significant TCP differences between gross tumor volume (GTV) and non-GTV regions (p=0.003).

Conclusions:

  • The atlas-based TCP model, enhanced with histopathology data, shows promise for predicting PCa recurrence.
  • This approach can potentially guide personalized RT by optimizing dose distribution to minimize recurrence risk.
  • The derived α/β ratios align with established values for PCa.