Journal of Medical Radiation Sciences (Mar 2024)

Improvement in male pelvis magnetic resonance image contouring following radiologist‐delivered training

  • Bronwyn Oar,
  • Amy Brown,
  • Glen Newman,
  • Alan Boles,
  • Christopher N. Rumley,
  • Rachel Doyle,
  • John Baines,
  • Alex Tan

DOI
https://doi.org/10.1002/jmrs.727
Journal volume & issue
Vol. 71, no. 1
pp. 114 – 122

Abstract

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Abstract Introduction The magnetic resonance linear accelerator (MRL) combines both magnetic resonance imaging and a linear accelerator, allowing for daily treatment adaptation. This study aimed to assess the impact of radiologist‐delivered training in magnetic resonance (MR) contouring of relevant structures within the male pelvis. Methods Two radiation oncologists, two radiation oncology registrars and seven radiation therapists completed contouring on 10 male pelvis MR datasets both pre‐ and post‐training. A 2‐hour MR anatomy training session was delivered by a radiologist, who also provided the ‘gold standard’ contours. The pre‐ and post‐training contours were compared against the gold standard with Dice similarity coefficient (DSC) and Hausdorff distances calculated; and the pre‐ and post‐confidence scores and timing were compared. Results The improvement in DSC were significant in prostate, rectum and seminal vesicles, with a post‐training median DSC of 0.87 ± 0.06, 0.92 ± 0.04 and 0.80 ± 0.14, respectively. The median Hausdorff improved with a median of 1.46 ± 0.78 mm, 0.52 ± 0.32 mm and 1.11 ± 0.86 mm for prostate, rectum and seminal vesicles, respectively. Bladder concordance was high both pre‐ and post‐training. Urethra contours improved post‐training, however, remained difficult to contour with a median post‐DSC of 0.51 ± 0.24. Overall, confidence scoring improved (P < 0.001) and timing decreased by an average of 4.4 ± 16.4 min post‐training. Conclusion Radiologist‐delivered training improved concordance of male pelvis contouring on MR datasets. Further work is required in the identification of urethra on MRs. These findings are of importance in the MRL adaptive workflow.

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