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Le Bao V, Haworth A, Dowling J, Walker A, Arumugam S, Jameson M, Chlap P, Wiltshire K, Keats S, Cloak K, Sidhom M, Kneebone A, Holloway L. Evaluating the relationship between contouring variability and modelled treatment outcome for prostate bed radiotherapy. Phys Med Biol 2024; 69:085008. [PMID: 38471173 DOI: 10.1088/1361-6560/ad3325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/12/2024] [Indexed: 03/14/2024]
Abstract
Objectives.Contouring similarity metrics are often used in studies of inter-observer variation and automatic segmentation but do not provide an assessment of clinical impact. This study focused on post-prostatectomy radiotherapy and aimed to (1) identify if there is a relationship between variations in commonly used contouring similarity metrics and resulting dosimetry and (2) identify the variation in clinical target volume (CTV) contouring that significantly impacts dosimetry.Approach.The study retrospectively analysed CT scans of 10 patients from the TROG 08.03 RAVES trial. The CTV, rectum, and bladder were contoured independently by three experienced observers. Using these contours reference simultaneous truth and performance level estimation (STAPLE) volumes were established. Additional CTVs were generated using an atlas algorithm based on a single benchmark case with 42 manual contours. Volumetric-modulated arc therapy (VMAT) treatment plans were generated for the observer, atlas, and reference volumes. The dosimetry was evaluated using radiobiological metrics. Correlations between contouring similarity and dosimetry metrics were calculated using Spearman coefficient (Γ). To access impact of variations in planning target volume (PTV) margin, the STAPLE PTV was uniformly contracted and expanded, with plans created for each PTV volume. STAPLE dose-volume histograms (DVHs) were exported for plans generated based on the contracted/expanded volumes, and dose-volume metrics assessed.Mainresults. The study found no strong correlations between the considered similarity metrics and modelled outcomes. Moderate correlations (0.5 <Γ< 0.7) were observed for Dice similarity coefficient, Jaccard, and mean distance to agreement metrics and rectum toxicities. The observations of this study indicate a tendency for variations in CTV contraction/expansion below 5 mm to result in minor dosimetric impacts.Significance. Contouring similarity metrics must be used with caution when interpreting them as indicators of treatment plan variation. For post-prostatectomy VMAT patients, this work showed variations in contours with an expansion/contraction of less than 5 mm did not lead to notable dosimetric differences, this should be explored in a larger dataset to assess generalisability.
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Affiliation(s)
- Viet Le Bao
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, University of Sydney, Australia
| | - Jason Dowling
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
| | - Amy Walker
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Sankar Arumugam
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Michael Jameson
- St Vincent's Clinical School, University of New South Wales, Sydney, Australia
- GenesisCare, Sydney, NSW, Australia
| | - Phillip Chlap
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Kirsty Wiltshire
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Sarah Keats
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Kirrily Cloak
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Mark Sidhom
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | | | - Lois Holloway
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
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Min H, Dowling J, Jameson MG, Cloak K, Faustino J, Sidhom M, Martin J, Cardoso M, Ebert MA, Haworth A, Chlap P, de Leon J, Berry M, Pryor D, Greer P, Vinod SK, Holloway L. Clinical target volume delineation quality assurance for MRI-guided prostate radiotherapy using deep learning with uncertainty estimation. Radiother Oncol 2023; 186:109794. [PMID: 37414257 DOI: 10.1016/j.radonc.2023.109794] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 06/19/2023] [Accepted: 06/30/2023] [Indexed: 07/08/2023]
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.
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Affiliation(s)
- Hang Min
- 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.
| | - Jason Dowling
- 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
| | - Michael G Jameson
- 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
| | - Kirrily Cloak
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia; South Western Clinical Campuses, University of New South Wales, Australia
| | - Joselle Faustino
- Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Mark Sidhom
- South Western Clinical Campuses, University of New South Wales, Australia; Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Jarad Martin
- Calvary Mater Newcastle Hospital, Radiation Oncology, Newcastle, Australia
| | - Michael Cardoso
- Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Martin A Ebert
- 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
| | - Annette Haworth
- Institute of Medical Physics, The University of Sydney, New South Wales, Australia
| | - Phillip Chlap
- 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
| | - Jeremiah de Leon
- GenesisCare, Sydney, New South Wales, Australia; Illawarra Cancer Care Centre, Wollongong, Australia
| | - Megan Berry
- Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - David Pryor
- Princess Alexandra Hospital, Brisbane, Queensland, Australia
| | - Peter Greer
- School of Mathematical and Physical Sciences, University of Newcastle, New South Wales, Australia; Calvary Mater Newcastle Hospital, Radiation Oncology, Newcastle, Australia
| | - Shalini K Vinod
- 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
| | - Lois Holloway
- 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
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Haidar A, Field M, Batumalai V, Cloak K, Al Mouiee D, Chlap P, Huang X, Chin V, Aly F, Carolan M, Sykes J, Vinod SK, Delaney GP, Holloway L. Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach. Cancers (Basel) 2023; 15:cancers15030564. [PMID: 36765523 PMCID: PMC9913464 DOI: 10.3390/cancers15030564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/01/2023] [Accepted: 01/11/2023] [Indexed: 01/18/2023] Open
Abstract
In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume.
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Affiliation(s)
- Ali Haidar
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
- Correspondence: or
| | - Matthew Field
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Vikneswary Batumalai
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
- GenesisCare, Alexandria, NSW 2015, Australia
| | - Kirrily Cloak
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Daniel Al Mouiee
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Phillip Chlap
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Xiaoshui Huang
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- University of Sydney, Camperdown, NSW 2006, Australia
| | - Vicky Chin
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Farhannah Aly
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Martin Carolan
- Illawarra Cancer Care Center, Wollongong, NSW 2522, Australia
- University of Wollongong, Wollongong, NSW 2522, Australia
| | - Jonathan Sykes
- University of Sydney, Camperdown, NSW 2006, Australia
- Blacktown Hospital, Blacktown, NSW 2148, Australia
| | - Shalini K. Vinod
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Geoffrey P. Delaney
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Lois Holloway
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
- University of Sydney, Camperdown, NSW 2006, Australia
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haidar A, Field M, Batumalai V, Cloak K, Al Mouiee D, Chlap P, Huang X, Chin V, Carolan M, Sykes J, Vinod S, Delaney G, Holloway L. PO-1618 Standardising Nomenclatures in Breast Radiotherapy Imaging Data using Machine Learning Algorithms. Radiother Oncol 2022. [DOI: 10.1016/s0167-8140(22)03582-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Min H, Dowling J, Jameson MG, Cloak K, Faustino J, Sidhom M, Martin J, Ebert MA, Haworth A, Chlap P, de Leon J, Berry M, Pryor D, Greer P, Vinod SK, Holloway L. Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial. Phys Med Biol 2021; 66. [PMID: 34507305 DOI: 10.1088/1361-6560/ac25d5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 09/10/2021] [Indexed: 11/11/2022]
Abstract
Volume delineation quality assurance (QA) is particularly important in clinical trial settings where consistent protocol implementation is required, as outcomes will affect future as well current patients. Currently, where feasible, this is conducted manually, which is time consuming and resource intensive. Although previous studies mostly focused on automating delineation QA on CT, magnetic resonance imaging (MRI) is being increasingly used in radiotherapy treatment. In this work, we propose to perform automatic delineation QA on prostate MRI for both the clinical target volume (CTV) and organs-at-risk (OARs) by using delineations generated by 3D Unet variants as benchmarks for QA. These networks were trained on a small gold standard atlas set and applied on a multicentre radiotherapy clinical trial dataset to generate benchmark delineations. Then, a QA stage was designed to recommend 'pass', 'minor correction' and 'major correction' for each manual delineation in the trial set by thresholding its Dice similarity coefficient to the network generated delineation. Among all 3D Unet variants explored, the Unet with anatomical gates in an AtlasNet architecture performed the best in delineation QA, achieving an area under the receiver operating characteristics curve of 0.97, 0.92, 0.89 and 0.97 for identifying unacceptable (major correction) delineations with a sensitivity of 0.93, 0.73, 0.74 and 0.90 at a specificity of 0.93, 0.86, 0.86 and 0.95 for bladder, prostate CTV, rectum and gel spacer respectively. To the best of our knowledge, this is the first study to propose automated delineation QA for a multicentre radiotherapy clinical trial with treatment planning MRI. The methods proposed in this work can potentially improve the accuracy and consistency of CTV and OAR delineation in radiotherapy treatment planning.
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Affiliation(s)
- Hang Min
- CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia.,Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.,South Western Clinical School, University of New South Wales, Australia
| | - Jason Dowling
- CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia.,South Western Clinical School, 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
| | - Michael G Jameson
- St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Australia.,GenesisCare, Sydney, New South Wales, Australia
| | - Kirrily Cloak
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.,South Western Clinical School, University of New South Wales, Australia
| | - Joselle Faustino
- Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Mark Sidhom
- South Western Clinical School, University of New South Wales, Australia.,Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Jarad Martin
- Department of Radiation Oncology, Calvary Mater Newcastle, Newcastle, New South Wales, Australia
| | - Martin A Ebert
- 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
| | - Annette Haworth
- Institute of Medical Physics, The University of Sydney, New South Wales, Australia
| | - Phillip Chlap
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.,South Western Clinical School, University of New South Wales, Australia.,Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Jeremiah de Leon
- GenesisCare, Sydney, New South Wales, Australia.,Illawarra Cancer Care Centre, Wollongong, Australia
| | - Megan Berry
- South Western Clinical School, University of New South Wales, Australia.,Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - David Pryor
- Princess Alexandra Hospital, Brisbane, Queensland, Australia
| | - Peter Greer
- School of Mathematical and Physical Sciences, University of Newcastle, New South Wales, Australia.,Department of Radiation Oncology, Calvary Mater Newcastle, Newcastle, New South Wales, Australia
| | - Shalini K Vinod
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.,South Western Clinical School, University of New South Wales, Australia.,Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Lois Holloway
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.,South Western Clinical School, 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
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Le Bao V, Jameson M, Cloak K, Ochoa C, Kneebone A, Haworth A, Holloway L. Automatic Post-Prostatectomy Planning: Potential for Improving Quality and Consistency in Clinical Trials. Int J Radiat Oncol Biol Phys 2019. [DOI: 10.1016/j.ijrobp.2019.06.755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Cloak K, Jameson MG, Paneghel A, Wiltshire K, Kneebone A, Pearse M, Sidhom M, Tang C, Fraser‐Browne C, Holloway LC, Haworth A. Contour variation is a primary source of error when delivering post prostatectomy radiotherapy: Results of the Trans‐Tasman Radiation Oncology Group 08.03 Radiotherapy Adjuvant Versus Early Salvage (RAVES) benchmarking exercise. J Med Imaging Radiat Oncol 2019; 63:390-398. [DOI: 10.1111/1754-9485.12884] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 03/10/2019] [Indexed: 12/17/2022]
Affiliation(s)
- Kirrily Cloak
- South Western Sydney Clinical School University of NSW Sydney New South Wales Australia
- Cancer Therapy Centre Liverpool Hospital Sydney New South Wales Australia
- Ingham Institute of Applied Medical Research Liverpool Hospital Sydney New South Wales Australia
| | - Michael G Jameson
- South Western Sydney Clinical School University of NSW Sydney New South Wales Australia
- Cancer Therapy Centre Liverpool Hospital Sydney New South Wales Australia
- Ingham Institute of Applied Medical Research Liverpool Hospital Sydney New South Wales Australia
| | | | | | - Andrew Kneebone
- University of Sydney Sydney New South Wales Australia
- Royal North Shore Hospital Sydney New South Wales Australia
| | | | - Mark Sidhom
- South Western Sydney Clinical School University of NSW Sydney New South Wales Australia
- Cancer Therapy Centre Liverpool Hospital Sydney New South Wales Australia
| | - Colin Tang
- Sir Charles Gairdner Hospital Perth Western Australia Australia
| | | | - Lois C Holloway
- South Western Sydney Clinical School University of NSW Sydney New South Wales Australia
- Cancer Therapy Centre Liverpool Hospital Sydney New South Wales Australia
- Ingham Institute of Applied Medical Research Liverpool Hospital Sydney New South Wales Australia
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Jameson M, Dowling J, Faustino J, Cloak K, Sidhom M, Martin J, De Leon J, Berry M, Pryor D, Holloway L. PO-0823: TRAC: Automated atlas based machine learning QA of contouring accuracy for the PROMETHEUS trial. Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)31133-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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de Leon JF, Jameson MG, Windsor A, Cloak K, Keats S, Vial P, Holloway L, Metcalfe P, Sidhom M. Superior target volume and organ stability with the use of endorectal balloons in post-prostatectomy radiotherapy. J Med Imaging Radiat Oncol 2015; 59:507-513. [PMID: 25828420 DOI: 10.1111/1754-9485.12300] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2014] [Accepted: 02/01/2015] [Indexed: 11/26/2022]
Abstract
INTRODUCTION We investigated the endorectal balloon (ERB) as a method to improve post-prostatectomy clinical target volume (CTV) stability. METHODS Seventy cone-beam CT (CBCT) obtained during radiotherapy treatment from seven patients treated with an ERB and 68 CBCT from seven patients treated without an ERB were contoured according to published guidelines. CTV was subdivided into superior and inferior CTV; whole rectal volume was subdivided into superior and inferior rectum and anal volume. Concordance index (CI) of CBCT treatment volumes compared with planning volumes was calculated and displacements were measured. RESULTS Whole rectal, superior and inferior rectum and anal CI were significantly improved with the ERB by 21%, 17%, 26% and 17% respectively (P < 0.0001). Overall CTV and inferior CTV CI was improved by 4% with the ERB (overall CTV P = 0.021; Inferior CTV P < 0.0001). In the ERB cohort, average displacement for superior CTV was 0.37 cm anterior-posterior (AP) and 0.10 cm left-right (LR). Average standard deviation was 0.27 cm AP and 0.11 cm LR. Inferior CTV average displacement was 0.11 cm AP and 0.02 cm LR. Average standard deviation was 0.11 cm AP and 0.02 cm LR. In the non-ERB cohort, average displacement for superior CTV was 0.43 cm AP and 0.10 mm left-right (LR). Average standard deviation was 0.45 cm AP and 0.13 cm LR. Inferior CTV average displacement was 0.16 cm AP and 0.01 cm LR. Average standard deviation was 0.17 cm AP and 0.03 cm LR. There was no statistically significant impact of bladder filling on CTV CI in ERB patients (P = 0.551) as opposed to non-ERB patients (P = 0.0421). CONCLUSION ERBs in the post-prostatectomy setting resulted in increased rectal and CTV stability while negating the effects of bladder filling on CTV stability.
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Affiliation(s)
- Jeremiah F de Leon
- Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia
| | - Michael G Jameson
- Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia.,Centre For Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia.,Ingham Institute, Sydney, New South Wales, Australia
| | - Apsara Windsor
- Central Coast Cancer Centre, Gosford, New South Wales, Australia.,University of New South Wales, Australia
| | - Kirrily Cloak
- Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia
| | - Sarah Keats
- Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia
| | - Philip Vial
- Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia.,Ingham Institute, Sydney, New South Wales, Australia.,Medical Physics, School of Physics, University of Sydney, New South Wales, Australia
| | - Lois Holloway
- Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia.,Centre For Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia.,Ingham Institute, Sydney, New South Wales, Australia.,Medical Physics, School of Physics, University of Sydney, New South Wales, Australia.,SWSCS, School of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Peter Metcalfe
- Centre For Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia.,Ingham Institute, Sydney, New South Wales, Australia
| | - Mark Sidhom
- Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia
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Jameson MG, De Leon J, Windsor AA, Cloak K, Keats S, Dowling JA, Chandra SS, Vial P, Sidhom M, Holloway L, Metcalfe P. Endorectal balloons in the post prostatectomy setting: do gains in stability lead to more predictable dosimetry? Radiother Oncol 2013; 109:493-7. [PMID: 24044793 DOI: 10.1016/j.radonc.2013.08.024] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2012] [Revised: 08/14/2013] [Accepted: 08/15/2013] [Indexed: 10/26/2022]
Abstract
PURPOSE To perform a comparative study assessing potential benefits of endorectal-balloons (ERB) in post-prostatectomy patients. METHOD AND MATERIALS Ten retrospective post-prostatectomy patients treated without ERB and ten prospective patients treated with the ERB in situ were recruited. All patients received IMRT and IGRT using kilovoltage cone-beam computed tomography (kVCBCT). kVCBCT datasets were registered to the planning dataset, recontoured and the original plan recalculated on the kVCBCTs to recreate anatomical conditions during treatment. The imaging, structure and dose data were imported into in-house software for the assessment of geometric variation and cumulative equivalent uniform dose (EUD) in the two groups. RESULTS The difference in location (ΔCOV) for the bladder between planning and each CBCT was similar for each group. The range of mean ΔCOV for the rectum was 0.15-0.58 cm and 0.15-0.59 cm for the non-ERB and ERB groups. For superior-CTV and inferior-CTV the difference between planned and delivered D95% (mean ± SD) for the non-ERB group was 2.1 ± 6.0 Gy and -0.04 ± 0.20 Gy. While for the ERB group the difference in D95% was 8.7 ± 12.6 Gy and 0.003 ± 0.104 Gy. CONCLUSIONS The use of ERBs in the post-prostatectomy setting did improve geometric reproducibility of the target and surrounding normal tissues, however no improvement in dosimetric stability was observed for the margins employed.
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Affiliation(s)
- Michael G Jameson
- Liverpool and Macarthur Cancer Therapy Centres, Australia; Centre for Medical Radiation Physics, University of Wollongong, Australia; Ingham Institute, Australia.
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Windsor A, De Leon J, Jameson M, Cloak K, Zammit A, Ko R, Vial P, Holloway L, Sidhom M, Metcalfe P. Endorectal Balloons in Postprostatectomy Radiation Therapy — Improved Stability of Clinical Target Volumes and Reduction of Geographical Miss. Int J Radiat Oncol Biol Phys 2012. [DOI: 10.1016/j.ijrobp.2012.07.1033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Jameson M, Leon JD, Windsor A, Cloak K, Holloway L, Vial P, Sidhom M, Metcalfe P. TH-C-BRA-04: Endorectal Balloons in Post-Prostatectomy: Do Gains in Stability Lead to More Predictable Dosimetry? Med Phys 2012. [DOI: 10.1118/1.4736320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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