Clinical target volume delineation quality assurance for MRI-guided prostate radiotherapy using deep learning with uncertainty estimation

Hang Min; Jason Dowling; Michael G Jameson; Kirrily Cloak; Joselle Faustino; Mark Sidhom; Jarad Martin; Michael Cardoso; Martin A Ebert; Annette Haworth; Phillip Chlap; Jeremiah de Leon; Megan Berry; David Pryor; Peter Greer; Shalini K Vinod; Lois Holloway

doi:10.1016/j.radonc.2023.109794

Clinical target volume delineation quality assurance for MRI-guided prostate radiotherapy using deep learning with uncertainty estimation

Radiother Oncol. 2023 Sep:186:109794. doi: 10.1016/j.radonc.2023.109794. Epub 2023 Jul 4.

Authors

Hang Min¹, Jason Dowling², Michael G Jameson³, Kirrily Cloak⁴, Joselle Faustino⁵, Mark Sidhom⁶, Jarad Martin⁷, Michael Cardoso⁵, Martin A Ebert⁸, Annette Haworth⁹, Phillip Chlap¹⁰, Jeremiah de Leon¹¹, Megan Berry⁵, David Pryor¹², Peter Greer¹³, Shalini K Vinod¹⁰, Lois Holloway¹⁴

Affiliations

¹ CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia; Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia; South Western Clinical Campuses, University of New South Wales, Australia. Electronic address: hang.min@csiro.au.
² CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia; South Western Clinical Campuses, University of New South Wales, Australia; Centre for Medical Radiation Physics, University of Wollongong, New South Wales, Australia; Institute of Medical Physics, The University of Sydney, New South Wales, Australia; School of Mathematical and Physical Sciences, University of Newcastle, New South Wales, Australia.
³ Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia; St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Australia; GenesisCare, Sydney, New South Wales, Australia; Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia.
⁴ Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia; South Western Clinical Campuses, University of New South Wales, Australia.
⁵ Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia.
⁶ South Western Clinical Campuses, University of New South Wales, Australia; Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia.
⁷ Calvary Mater Newcastle Hospital, Radiation Oncology, Newcastle, Australia.
⁸ Centre for Medical Radiation Physics, University of Wollongong, New South Wales, Australia; Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia; School of Physics Mathematics and Computing, University of Western Australia, Perth, Western Australia, Australia.
⁹ Institute of Medical Physics, The University of Sydney, New South Wales, Australia.
¹⁰ Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia; South Western Clinical Campuses, University of New South Wales, Australia; Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia.
¹¹ GenesisCare, Sydney, New South Wales, Australia; Illawarra Cancer Care Centre, Wollongong, Australia.
¹² Princess Alexandra Hospital, Brisbane, Queensland, Australia.
¹³ School of Mathematical and Physical Sciences, University of Newcastle, New South Wales, Australia; Calvary Mater Newcastle Hospital, Radiation Oncology, Newcastle, Australia.
¹⁴ Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia; South Western Clinical Campuses, University of New South Wales, Australia; Centre for Medical Radiation Physics, University of Wollongong, New South Wales, Australia; Institute of Medical Physics, The University of Sydney, New South Wales, Australia; Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia.

PMID: 37414257
DOI: 10.1016/j.radonc.2023.109794

Abstract

Background and purpose: Previous studies on automatic delineation quality assurance (QA) have mostly focused on CT-based planning. As MRI-guided radiotherapy is increasingly utilized in prostate cancer treatment, there is a need for more research on MRI-specific automatic QA. This work proposes a clinical target volume (CTV) delineation QA framework based on deep learning (DL) for MRI-guided prostate radiotherapy.

Materials and methods: The proposed workflow utilized a 3D dropblock ResUnet++ (DB-ResUnet++) to generate multiple segmentation predictions via Monte Carlo dropout which were used to compute an average delineation and area of uncertainty. A logistic regression (LR) classifier was employed to classify the manual delineation as pass or discrepancy based on the spatial association between the manual delineation and the network's outputs. This approach was evaluated on a multicentre MRI-only prostate radiotherapy dataset and compared with our previously published QA framework based on AN-AG Unet.

Results: The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.92, a true positive rate (TPR) of 0.92 and a false positive rate of 0.09 with an average processing time per delineation of 1.3 min. Compared with our previous work using AN-AG Unet, this method generated fewer false positive detections at the same TPR with a much faster processing speed.

Conclusion: To the best of our knowledge, this is the first study to propose an automatic delineation QA tool using DL with uncertainty estimation for MRI-guided prostate radiotherapy, which can potentially be used for reviewing prostate CTV delineation in multicentre clinical trials.

Keywords: Clinical target volume; Deep learning; Prostate cancer; Quality assurance; Radiotherapy; Uncertainty estimation.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Deep Learning*
Humans
Magnetic Resonance Imaging
Male
Prostatic Neoplasms* / diagnostic imaging
Prostatic Neoplasms* / radiotherapy
Quality Assurance, Health Care
Radiotherapy, Image-Guided*
Uncertainty