Physics and Imaging in Radiation Oncology (Oct 2024)

Integrated framework for quantitative T2-weighted MRI analysis following prostate cancer radiotherapy

  • Evangelia I. Zacharaki,
  • Adrian L. Breto,
  • Ahmad Algohary,
  • Veronica Wallaengen,
  • Sandra M. Gaston,
  • Sanoj Punnen,
  • Patricia Castillo,
  • Pradip M. Pattany,
  • Oleksandr N. Kryvenko,
  • Benjamin Spieler,
  • John C. Ford,
  • Matthew C. Abramowitz,
  • Alan Dal Pra,
  • Alan Pollack,
  • Radka Stoyanova

Journal volume & issue
Vol. 32
p. 100660

Abstract

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Purpose: The aim of this study is to develop a framework for quantitative analysis of longitudinal T2-weighted MRIs (T2w) following radiotherapy (RT) for prostate cancer. Materials and methods: The developed methodology includes: (i) deformable image registration of longitudinal series to pre-RT T2w for automated detection of prostate, peripheral zone (PZ), and gross tumor volume (GTV); and (ii) T2w signal-intensity harmonization based on three reference tissues. The REgistration and HARMonization (REHARM) framework was applied on T2w acquired in a clinical trial consisting of two pre-RT and three post-RT MRI exams. Image registration was assessed by the DICE coefficient between automatic and manual contours, and intensity normalization via inter-patient histogram intersection. Longitudinal consistency was evaluated by the repeatability coefficient and Pearson correlation (r) between the two T2w exams before RT. Results: T2w from 107 MRI exams (23 patients) were utilized. Following REHARM, the histogram intersections for prostate, PZ and GTV increased from median = 0.43/0.16/0.13 to 0.66/0.44/0.46. The repeatability in T2w intensity estimation was better for the automatic than the manual contours for all three regions of interest (r = 0.9, p < 0.0001, for GTV). The changes in the tissues’ T2w values pre- and post-RT became significant, indicating the measurable quantitative signal related to radiation. Conclusions: The developed methodology allows to automate longitudinal analysis reducing data acquisition-related variation and improving consistency. The quantitative characterization of RT-induced changes in T2w will lead to new understanding of radiation effects enabling prediction modeling of RT response.

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