Deep learning model for automatic contouring of cardiovascular substructures on radiotherapy planning CT images: Dosimetric validation and reader study based clinical acceptability testing
| العنوان: | Deep learning model for automatic contouring of cardiovascular substructures on radiotherapy planning CT images: Dosimetric validation and reader study based clinical acceptability testing |
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| المؤلفون: | Robin Wijsman, Jonas Teuwen, Johan Bussink, B. Stam, M. Fernandes, René Monshouwer, Dominic A.X. Schinagl |
| المصدر: | Radiotherapy and Oncology, 165, pp. 52-59 Radiotherapy and Oncology, 165, 52-59. ELSEVIER IRELAND LTD Radiotherapy and Oncology, 165, 52-59 |
| بيانات النشر: | Elsevier BV, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Organs at Risk, medicine.medical_specialty, Lung Neoplasms, Contrast enhancement, IMPACT, medicine.medical_treatment, AUTO-SEGMENTATION, Locally advanced, Absolute difference, NSCLC, TOXICITY, Deep Learning, All institutes and research themes of the Radboud University Medical Center, Segmentation, Carcinoma, Non-Small-Cell Lung, medicine, Humans, Radiology, Nuclear Medicine and imaging, Contouring, business.industry, Radiotherapy Planning, Computer-Assisted, Deep learning, Cardiac radiotoxicity, Hematology, Surface dice, Women's cancers Radboud Institute for Health Sciences [Radboudumc 17], LUNG-CANCER PATIENTS, Radiation therapy, Oncology, Great vessels, SURVIVAL, HEART, Artificial intelligence, Radiology, Lung cancer, Tomography, X-Ray Computed, business, Rare cancers Radboud Institute for Health Sciences [Radboudumc 9], Automatic cardiac contouring |
| الوصف: | Background and purpose: Large radiotherapy (RT) planning imaging datasets with consistently contoured cardiovascular structures are essential for robust cardiac radiotoxicity research in thoracic cancers. This study aims to develop and validate a highly accurate automatic contouring model for the heart, cardiac chambers, and great vessels for RT planning computed tomography (CT) images that can be used for dose-volume parameter estimation. Materials and methods: A neural network model was trained using a dataset of 127 expertly contoured planning CT images from RT treatment of locally advanced non-small-cell lung cancer (NSCLC) patients. Evaluation of geometric accuracy and quality of dosimetric parameter estimation was performed on 50 independent scans with contrast and without contrast enhancement. The model was further evaluated regarding the clinical acceptability of the contours in 99 scans randomly sampled from the RTOG-0617 dataset by three experienced radiation oncologists. Results: Median surface dice at 3 mm tolerance for all dedicated thoracic structures was 90% in the test set. Median absolute difference between mean dose computed with model contours and expert contours was 0.45 Gy averaged over all structures. The mean clinical acceptability rate by majority vote in the RTOG-0617 scans was 91%. Conclusion: This model can be used to contour the heart, cardiac chambers, and great vessels in large datasets of RT planning thoracic CT images accurately, quickly, and consistently. Additionally, the model can be used as a time-saving tool for contouring in clinic practice. (c) 2021 The Authors. Published by Elsevier B.V. Radiotherapy and Oncology 165 (2021) 52-59 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| وصف الملف: | application/pdf |
| تدمد: | 0167-8140 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c4dabfa12b9bb4f9a438c5be09efb5a https://doi.org/10.1016/j.radonc.2021.10.008 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....0c4dabfa12b9bb4f9a438c5be09efb5a |
| قاعدة البيانات: | OpenAIRE |
Full Text via ClinicalKey | تدمد: | 01678140 |
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