Validation of automated magnetic resonance image segmentation for radiation therapy planning in prostate cancer

Anna Kuisma; Iiro Ranta; Jani Keyriläinen; Sami Suilamo; Pauliina Wright; Marko Pesola; Lizette Warner; Eliisa Löyttyniemi; Heikki Minn

doi:10.1016/j.phro.2020.02.004

Validation of automated magnetic resonance image segmentation for radiation therapy planning in prostate cancer

Phys Imaging Radiat Oncol. 2020 Mar 13:13:14-20. doi: 10.1016/j.phro.2020.02.004. eCollection 2020 Jan.

Authors

Anna Kuisma¹, Iiro Ranta^{1

2

3}, Jani Keyriläinen^{1

2

3}, Sami Suilamo^{1

2}, Pauliina Wright^{1

2}, Marko Pesola⁴, Lizette Warner⁵, Eliisa Löyttyniemi⁶, Heikki Minn¹

Affiliations

¹ Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland.
² Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland.
³ University of Turku, Department of Physics and Astronomy, Vesilinnantie 5, FI-20014 University of Turku, Finland.
⁴ Philips MR Therapy Oy, Äyritie 4, FI-01510 Vantaa, Finland.
⁵ Philips MR Oncology, 3000 Minuteman Road, Andover, MA 01810, United States.
⁶ University of Turku, Department of Biostatistics, Kiinamyllynkatu 10, FI-20014 University of Turku, Finland.

Abstract

Background and purpose: Magnetic resonance imaging (MRI) is increasingly used in radiation therapy planning of prostate cancer (PC) to reduce target volume delineation uncertainty. This study aimed to assess and validate the performance of a fully automated segmentation tool (AST) in MRI based radiation therapy planning of PC.

Material and methods: Pelvic structures of 65 PC patients delineated in an MRI-only workflow according to established guidelines were included in the analysis. Automatic vs manual segmentation by an experienced oncologist was compared with geometrical parameters, such as the dice similarity coefficient (DSC). Fifteen patients had a second MRI within 15 days to assess repeatability of the AST for prostate and seminal vesicles. Furthermore, we investigated whether hormonal therapy or body mass index (BMI) affected the AST results.

Results: The AST showed high agreement with manual segmentation expressed as DSC (mean, SD) for delineating prostate (0.84, 0.04), bladder (0.92, 0.04) and rectum (0.86, 0.04). For seminal vesicles (0.56, 0.17) and penile bulb (0.69, 0.12) the respective agreement was moderate. Performance of AST was not influenced by neoadjuvant hormonal therapy, although those on treatment had significantly smaller prostates than the hormone-naïve patients (p < 0.0001). In repeat assessment, consistency of prostate delineation resulted in mean DSC of 0.89, (SD 0.03) between the paired MRI scans for AST, while mean DSC of manual delineation was 0.82, (SD 0.05).

Conclusion: Fully automated MRI segmentation tool showed good agreement and repeatability compared with manual segmentation and was found clinically robust in patients with PC. However, manual review and adjustment of some structures in individual cases remain important in clinical use.

Keywords: Auto-segmentation; Delineation; MRI; Prostate cancer; Radiotherapy planning.