ArticleInternational journal of radiation oncology, biology, physics2018
Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function.
Article in International journal of radiation oncology, biology, physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled it.
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Who cites it
72 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- A blind randomized validated convolutional neural network for auto-segmentation of clinical target volume in rectal cancer patients receiving neoadjuvant radiotherapy.Cancer medicine · 2022Trial
- Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.Physics in medicine and biology · 2021Trial
- U-Net-Based Automated Quality Control of Knee Radiographs: Dual-Center Validation and Clinical Intervention.Journal of imaging informatics in medicine · 2026Article
- An intelligent point-of-care photoacoustic and ultrasound dual-modality imaging system for Rheumatoid arthritis in fingers: Technical development and initial clinical test.Photoacoustics · 2026Article
- Artificial intelligence applications in adaptive radiotherapy-a narrative review.Translational cancer research · 2026Review
- Deep learning-based auto-segmentation model for clinical target volume delineation in brachytherapy after parotid cancer surgery.Journal of contemporary brachytherapy · 2025Article
- A deep learning-based computed tomography reading system for the diagnosis of lung cancer associated with cystic airspaces.Scientific reports · 2025Article
- Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Hybrid ensemble deep learning model for advancing breast cancer detection and classification in clinical applications.Heliyon · 2024Article
- A multicenter study on deep learning for glioblastoma auto-segmentation with prior knowledge in multimodal imaging.Cancer science · 2024Article
- Automated plan generation for prostate radiotherapy patients using deep learning and scripted optimization.Physics and imaging in radiation oncology · 2024Article
- Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net.European radiology · 2024Article
- Improved automatic segmentation of brain metastasis gross tumor volume in computed tomography images for radiotherapy: a position attention module for U-Net architecture.Quantitative imaging in medicine and surgery · 2024Article
- Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions.Advances in radiation oncology · 2024Review
- Deep learning for automated segmentation in radiotherapy: a narrative review.The British journal of radiology · 2024Review
- Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers.Medical physics · 2024Article
- Clinical acceptability of automatically generated lymph node levels and structures of deglutition and mastication for head and neck radiation therapy.Physics and imaging in radiation oncology · 2024Article
- Fully-automated, CT-only GTV contouring for palliative head and neck radiotherapy.Scientific reports · 2023Article
- Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy.Journal of applied clinical medical physics · 2023Article
12 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors.
Funding
Abstract
purposeAutomating and standardizing the contouring of clinical target volumes (CTVs) can reduce interphysician variability, which is one of the largest sources of uncertainty in head and neck radiation therapy. In addition to using uniform margin expansions to auto-delineate high-risk CTVs, very little work has been performed to provide patient- and disease-specific high-risk CTVs. The aim of the present study was to develop a deep neural network for the auto-delineation of high-risk CTVs. METHODS AND MATERIALS: Fifty-two oropharyngeal cancer patients were selected for the present study. All patients were treated at The University of Texas MD Anderson Cancer Center from January 2006 to August 2010 and had previously contoured gross tumor volumes and CTVs. We developed a deep learning algorithm using deep auto-encoders to identify physician contouring patterns at our institution. These models use distance map information from surrounding anatomic structures and the gross tumor volume as input parameters and conduct voxel-based classification to identify voxels that are part of the high-risk CTV. In addition, we developed a novel probability threshold selection function, based on the Dice similarity coefficient (DSC), to improve the generalization of the predicted volumes. The DSC-based function is implemented during an inner cross-validation loop, and probability thresholds are selected a priori during model parameter optimization. We performed a volumetric comparison between the predicted and manually contoured volumes to assess our model.
resultsThe predicted volumes had a median DSC value of 0.81 (range 0.62-0.90), median mean surface distance of 2.8 mm (range 1.6-5.5), and median 95th Hausdorff distance of 7.5 mm (range 4.7-17.9) when comparing our predicted high-risk CTVs with the physician manual contours.
conclusionsThese predicted high-risk CTVs provided close agreement to the ground-truth compared with current interobserver variability. The predicted contours could be implemented clinically, with only minor or no changes.
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