ArticlePhysics and imaging in radiation oncology2020
A convolutional neural network for contouring metastatic lymph nodes on diffusion-weighted magnetic resonance images for assessment of radiotherapy response.
Article in Physics and imaging in radiation oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review.Journal of personalized medicine · 2023Review
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- A Survey on Deep Learning for Precision Oncology.Diagnostics (Basel, Switzerland) · 2022Review
- Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases.Inflammatory bowel diseases · 2022Review
- Automatic evaluation of contours in radiotherapy planning utilising conformity indices and machine learning.Physics and imaging in radiation oncology · 2020Article
- Quantitative magnetic resonance imaging on hybrid magnetic resonance linear accelerators: Perspective on technical and clinical validation.Physics and imaging in radiation oncology · 2020Review
- Future directions on the merge of quantitative imaging and artificial intelligence in radiation oncology.Physics and imaging in radiation oncology · 2020Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
background and purposeRetrieving quantitative parameters from magnetic resonance imaging (MRI), e.g. for early assessment of radiotherapy treatment response, necessitates contouring regions of interest, which is time-consuming and prone to errors. This becomes more pressing for daily imaging on MRI-guided radiotherapy systems. Therefore, we trained a deep convolutional neural network to automatically contour involved lymph nodes on diffusion-weighted (DW) MRI of head and neck cancer (HNC) patients receiving radiotherapy. MATERIALS AND
methodsDW-images from 48 HNC patients (18 induction-chemotherapy + chemoradiotherapy; 30 definitive chemoradiotherapy) with 68 involved lymph nodes were obtained on a diagnostic 1.5 T MR-scanner prior to and 2-3 timepoints throughout treatment. A radiation oncologist delineated the lymph nodes on the b = 50 s/mm
resultsIn the definitive chemoradiotherapy patients (n = 96 patients/lymphnodes/timepoints) the DSC was 0.87 (0.81-0.91) [median (1st-3rd quantiles)] and ΔADC was 1.9% (0.8-3.4%) and both remained stable throughout treatment. The network performed worse in the patients receiving induction-chemotherapy (n = 65), with DSC = 0.80 (0.71-0.87) and ΔADC = 3.3% (1.6-8.0%). The network performed well on the MR-Linac data (n = 8) with DSC = 0.80 (0.75-0.82) and ΔADC = 4.0% (0.6-9.1%).
conclusionsWe established accurate automatic contouring of involved lymph nodes for HNC patients on diagnostic and MR-Linac DW-images.
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