David Bird1, Michael G Nix2, Hazel McCallum3, Mark Teo2, Alexandra Gilbert4, Nathalie Casanova2, Rachel Cooper2, David L Buckley5, David Sebag-Montefiore4, Richard Speight2, Bashar Al-Qaisieh2, Ann M Henry4. 1. Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, United Kingdom; Radiotherapy Research Group, Leeds Institute of Medical Research, University of Leeds, United Kingdom. Electronic address: David.bird3@nhs.net. 2. Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, United Kingdom. 3. Northern Centre for Cancer Care, Newcastle Upon Tyne Hospitals NHS Foundation Trust, United Kingdom; Centre for Cancer, Newcastle University, United Kingdom. 4. Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, United Kingdom; Radiotherapy Research Group, Leeds Institute of Medical Research, University of Leeds, United Kingdom. 5. Biomedical Imaging, University of Leeds, United Kingdom.
Abstract
BACKGROUND AND PURPOSE: Comprehensive dosimetric analysis is required prior to the clinical implementation of pelvic MR-only sites, other than prostate, due to the limited number of site specific synthetic-CT (sCT) dosimetric assessments in the literature. This study aims to provide a comprehensive assessment of a deep learning-based, conditional generative adversarial network (cGAN) model for a large ano-rectal cancer cohort. The following challenges were investigated; T2-SPACE MR sequences, patient data from multiple centres and the impact of sex and cancer site on sCT quality. METHOD: RT treatment position CT and T2-SPACE MR scans, from two centres, were collected for 90 ano-rectal patients. A cGAN model trained using a focal loss function, was trained and tested on 46 and 44 CT-MR ano-rectal datasets, paired using deformable registration, respectively. VMAT plans were created on CT and recalculated on sCT. Dose differences and gamma indices assessed sCT dosimetric accuracy. A linear mixed effect (LME) model assessed the impact of centre, sex and cancer site. RESULTS: A mean PTV D95% dose difference of 0.1% (range: -0.5% to 0.7%) was found between CT and sCT. All gamma index (1%/1 mm threshold) measurements were >99.0%. The LME model found the impact of modality, cancer site, sex and centre was clinically insignificant (effect ranges: -0.4% and 0.3%). The mean dose difference for all OAR constraints was 0.1%. CONCLUSION: Focal loss cGAN models using T2-SPACE MR sequences from multiple centres can produce generalisable, dosimetrically accurate sCTs for ano-rectal cancers.
BACKGROUND AND PURPOSE: Comprehensive dosimetric analysis is required prior to the clinical implementation of pelvic MR-only sites, other than prostate, due to the limited number of site specific synthetic-CT (sCT) dosimetric assessments in the literature. This study aims to provide a comprehensive assessment of a deep learning-based, conditional generative adversarial network (cGAN) model for a large ano-rectal cancer cohort. The following challenges were investigated; T2-SPACE MR sequences, patient data from multiple centres and the impact of sex and cancer site on sCT quality. METHOD: RT treatment position CT and T2-SPACE MR scans, from two centres, were collected for 90 ano-rectal patients. A cGAN model trained using a focal loss function, was trained and tested on 46 and 44 CT-MR ano-rectal datasets, paired using deformable registration, respectively. VMAT plans were created on CT and recalculated on sCT. Dose differences and gamma indices assessed sCT dosimetric accuracy. A linear mixed effect (LME) model assessed the impact of centre, sex and cancer site. RESULTS: A mean PTV D95% dose difference of 0.1% (range: -0.5% to 0.7%) was found between CT and sCT. All gamma index (1%/1 mm threshold) measurements were >99.0%. The LME model found the impact of modality, cancer site, sex and centre was clinically insignificant (effect ranges: -0.4% and 0.3%). The mean dose difference for all OAR constraints was 0.1%. CONCLUSION: Focal loss cGAN models using T2-SPACE MR sequences from multiple centres can produce generalisable, dosimetrically accurate sCTs for ano-rectal cancers.
Authors: Laura M O'Connor; Jae H Choi; Jason A Dowling; Helen Warren-Forward; Jarad Martin; Peter B Greer Journal: Front Oncol Date: 2022-02-08 Impact factor: 6.244
Authors: Shu-Hui Hsu; Zhaohui Han; Jonathan E Leeman; Yue-Houng Hu; Raymond H Mak; Atchar Sudhyadhom Journal: Front Oncol Date: 2022-09-23 Impact factor: 5.738
Authors: David Bird; Matthew Beasley; Michael G Nix; Marcus Tyyger; Hazel McCallum; Mark Teo; Alexandra Gilbert; Nathalie Casanova; Rachel Cooper; David L Buckley; David Sebag-Montefiore; Richard Speight; Ann M Henry; Bashar Al-Qaisieh Journal: Phys Imaging Radiat Oncol Date: 2021-07-18