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Paczona VR, Capala ME, Deák-Karancsi B, Borzási E, Együd Z, Végváry Z, Kelemen G, Kószó R, Ruskó L, Ferenczi L, Verduijn GM, Petit SF, Oláh J, Cserháti A, Wiesinger F, Hideghéty K. Magnetic Resonance Imaging-Based Delineation of Organs at Risk in the Head and Neck Region. Adv Radiat Oncol 2022; 8:101042. [PMID: 36636382 PMCID: PMC9830100 DOI: 10.1016/j.adro.2022.101042] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/24/2022] [Indexed: 01/16/2023] Open
Abstract
Purpose The aim of this article is to establish a comprehensive contouring guideline for treatment planning using only magnetic resonance images through an up-to-date set of organs at risk (OARs), recommended organ boundaries, and relevant suggestions for the magnetic resonance imaging (MRI)-based delineation of OARs in the head and neck (H&N) region. Methods and Materials After a detailed review of the literature, MRI data were collected from the H&N region of healthy volunteers. OARs were delineated in the axial, coronal, and sagittal planes on T2-weighted sequences. Every contour defined was revised by 4 radiation oncologists and subsequently by 2 independent senior experts (H&N radiation oncologist and radiologist). After revision, the final structures were presented to the consortium partners. Results A definitive consensus was reached after multi-institutional review. On that basis, we provided a detailed anatomic and functional description and specific MRI characteristics of the OARs. Conclusions In the era of precision radiation therapy, the need for well-built, straightforward contouring guidelines is on the rise. Precise, uniform, delineation-based, automated OAR segmentation on MRI may lead to increased accuracy in terms of organ boundaries and analysis of dose-dependent sequelae for an adequate definition of normal tissue complication probability.
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Affiliation(s)
- Viktor R. Paczona
- Department of Oncotherapy, University of Szeged, Szeged, Hungary,Corresponding author: Viktor R. Paczona, MD
| | | | | | - Emőke Borzási
- Department of Oncotherapy, University of Szeged, Szeged, Hungary
| | - Zsófia Együd
- Department of Oncotherapy, University of Szeged, Szeged, Hungary
| | - Zoltán Végváry
- Department of Oncotherapy, University of Szeged, Szeged, Hungary
| | - Gyöngyi Kelemen
- Department of Oncotherapy, University of Szeged, Szeged, Hungary
| | - Renáta Kószó
- Department of Oncotherapy, University of Szeged, Szeged, Hungary
| | | | | | | | | | - Judit Oláh
- Department of Oncotherapy, University of Szeged, Szeged, Hungary
| | | | | | - Katalin Hideghéty
- Department of Oncotherapy, University of Szeged, Szeged, Hungary,ELI-HU Non-Profit Ltd, Szeged, Hungary
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Tang S, Rai R, Vinod SK, Elwadia D, Forstner D, Moretti D, Tran T, Do V, King O, Lim K, Liney G, Goozee G, Holloway L. Rates of MRI simulator utilisation in a tertiary cancer therapy centre. J Med Imaging Radiat Oncol 2022; 66:717-723. [PMID: 35687525 DOI: 10.1111/1754-9485.13422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 04/27/2022] [Indexed: 11/28/2022]
Abstract
Magnetic resonance imaging (MRI) is increasingly being integrated into the radiation oncology workflow, due to its improved soft tissue contrast without additional exposure to ionising radiation. A review of MRI utilisation according to evidence based departmental guidelines was performed. Guideline utilisation rates were calculated to be 50% (true utilisation rate was 46%) of all new cancer patients treated with adjuvant or curative intent, excluding simple skin and breast cancer patients. Guideline utilisation rates were highest in the lower gastrointestinal and gynaecological subsites, with the lowest being in the upper gastrointestinal and thorax subsites. Head and neck (38% vs 45%) and CNS (46% vs 67%) cancers had the largest discrepancy between true and guideline utilisation rates due to unnamed reasons and non-contemporaneous diagnostic imaging respectively. This report outlines approximate MRI utilisation rates in a tertiary radiation oncology service and may help guide planning for future departments contemplating installation of an MRI simulator.
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Affiliation(s)
- Simon Tang
- Central West Cancer, Gosford, New South Wales, Australia.,Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
| | - Robba Rai
- Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia.,South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Shalini K Vinod
- Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia.,South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Doaa Elwadia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia
| | - Dion Forstner
- Genesis Care, St Vincent's Clinic, Darlinghust, New South Wales, Australia
| | - Daniel Moretti
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia
| | - Thomas Tran
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia
| | - Viet Do
- Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia.,South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Odette King
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia
| | - Karen Lim
- Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia.,South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Gary Liney
- Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia.,South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Gary Goozee
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia
| | - Lois Holloway
- Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia.,South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia.,University of Sydney, Sydney, New South Wales, Australia.,University of Wollongong, Wollongong, New South Wales, Australia
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Elisabeth Olsson C, Suresh R, Niemelä J, Akram SU, Valdman A. Autosegmentation based on different-sized training datasets of consistently-curated volumes and impact on rectal contours in prostate cancer radiation therapy. Phys Imaging Radiat Oncol 2022; 22:67-72. [PMID: 35572041 PMCID: PMC9092250 DOI: 10.1016/j.phro.2022.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 04/20/2022] [Accepted: 04/22/2022] [Indexed: 12/01/2022] Open
Abstract
Background and purpose Autosegmentation techniques are emerging as time-saving means for radiation therapy (RT) contouring, but the understanding of their performance on different datasets is limited. The aim of this study was to determine agreement between rectal volumes by an existing autosegmentation algorithm and manually-delineated rectal volumes in prostate cancer RT. We also investigated contour quality by different-sized training datasets and consistently-curated volumes for retrained versions of this same algorithm. Materials and methods Single-institutional data from 624 prostate cancer patients treated to 50–70 Gy were used. Manually-delineated clinical rectal volumes (clinical) and consistently-curated volumes recontoured to one anatomical guideline (reference) were compared to autocontoured volumes by a commercial autosegmentation tool based on deep-learning (v1; n = 891, multiple-institutional data) and retrained versions using subsets of the curated volumes (v32/64/128/256; n = 32/64/128/256). Evaluations included dose-volume histogram metrics, Dice similarity coefficients, and Hausdorff distances; differences between groups were quantified using parametric or non-parametric hypothesis testing. Results Volumes by v1-256 (76–78 cm3) were larger than reference (75 cm3) and clinical (76 cm3). Mean doses by v1-256 (24.2–25.2 Gy) were closer to reference (24.2 Gy) than to clinical (23.8 Gy). Maximum doses were similar for all volumes (65.7–66.0 Gy). Dice for v1-256 and reference (0.87–0.89) were higher than for v1-256 and clinical (0.86–0.87) with corresponding Hausdorff comparisons including reference smaller than comparisons including clinical (5–6 mm vs. 7–8 mm). Conclusion Using small single-institutional RT datasets with consistently-defined rectal volumes when training autosegmentation algorithms created contours of similar quality as the same algorithm trained on large multi-institutional datasets.
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Stelmes JJ, Vu E, Grégoire V, Simon C, Clementel E, Kazmierska J, Grant W, Ozsahin M, Tomsej M, Vieillevigne L, Fortpied C, Hurkmans EC, Branquinho A, Andratschke N, Zimmermann F, Weber DC. Quality assurance of radiotherapy in the ongoing EORTC 1420 "Best of" trial for early stage oropharyngeal, supraglottic and hypopharyngeal carcinoma: results of the benchmark case procedure. Radiat Oncol 2021; 16:81. [PMID: 33933118 PMCID: PMC8088557 DOI: 10.1186/s13014-021-01809-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 04/19/2021] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION The current phase III EORTC 1420 Best-of trial (NCT02984410) compares the swallowing function after transoral surgery versus intensity modulated radiotherapy (RT) in patients with early-stage carcinoma of the oropharynx, supraglottis and hypopharynx. We report the analysis of the Benchmark Case (BC) procedures before patient recruitment with special attention to dysphagia/aspiration related structures (DARS). MATERIALS AND METHODS Submitted RT volumes and plans from participating centers were analyzed and compared against the gold-standard expert delineations and dose distributions. Descriptive analysis of protocol deviations was conducted. Mean Sorensen-Dice similarity index (mDSI) and Hausdorff distance (mHD) were applied to evaluate the inter-observer variability (IOV). RESULTS 65% (23/35) of the institutions needed more than one submission to achieve Quality assurance (RTQA) clearance. OAR volume delineations were the cause for rejection in 53% (40/76) of cases. IOV could be improved in 5 out of 12 OARs by more than 10 mm after resubmission (mHD). Despite this, final IOV for critical OARs in delineation remained significant among DARS by choosing an aleatory threshold of 0.7 (mDSI) and 15 mm (mHD). CONCLUSIONS This is to our knowledge the largest BC analysis among Head and neck RTQA programs performed in the framework of a prospective trial. Benchmarking identified non-common OARs and target delineations errors as the main source of deviations and IOV could be reduced in a significant number of cases after this process. Due to the substantial resources involved with benchmarking, future benchmark analyses should assess fully the impact on patients' clinical outcome.
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Affiliation(s)
- J-J Stelmes
- Radiation Oncology Department, Oncology Institute of Southern Switzerland, Via Athos Gallino 12, 6500, Bellinzona, Switzerland.
| | - E Vu
- Department of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | | | - C Simon
- Lausanne University Hospital, Lausanne, Switzerland
| | | | | | - W Grant
- Gloucestershire Hospitals, NHS Foundation Trust, Gloucester, UK
| | - M Ozsahin
- Lausanne University Hospital, Lausanne, Switzerland
| | - M Tomsej
- Hospital of Charleroi, Charleroi, Belgium
| | | | | | | | - A Branquinho
- Centro Hospitalar Lisboa Norte, Lisbon, Portugal
| | | | - F Zimmermann
- University Hospital of Basel, Basel, Switzerland
| | - D-C Weber
- University Hospital of Bern, Bern, Switzerland
- Paul-Scherrer-Institute, Villigen, Switzerland
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