ArticleFrontiers in oncology2022
Deep Learning for Automated Contouring of Gross Tumor Volumes in Esophageal Cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy.Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus · 2023Pooled it
- Towards deep-learning (DL) based fully automated target delineation for rectal cancer neoadjuvant radiotherapy using a divide-and-conquer strategy: a study with multicenter blind and randomized validation.Radiation oncology (London, England) · 2023Trial
- Trial
- A human-AI collaborative workflow for improved gross tumor volume delineation in esophageal cancer radiotherapy.BMC medical imaging · 2026Article
- MR-Guided Radiotherapy in Oesophageal Cancer: From Principles to Practice-A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- Automated segmentation of esophageal squamous cell carcinoma in contrast-enhanced free-breathing 3D-GRE: a comparative study of UNet, nnUNet, and UMamba for tumor delineation.BMC medical imaging · 2025Article
- AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Current Role of Artificial Intelligence in the Management of Esophageal Cancer.Journal of clinical medicine · 2025Review
- Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer.Cancers · 2024Review
- Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network.Cancer medicine · 2024Article
Corrections and comments
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Authors and funding
14 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The aim of this study was to propose and evaluate a novel three-dimensional (3D) V-Net and two-dimensional (2D) U-Net mixed (VUMix-Net) architecture for a fully automatic and accurate gross tumor volume (GTV) in esophageal cancer (EC)-delineated contours. Methods: We collected the computed tomography (CT) scans of 215 EC patients. 3D V-Net, 2D U-Net, and VUMix-Net were developed and further applied simultaneously to delineate GTVs. The Dice similarity coefficient (DSC) and 95th-percentile Hausdorff distance (95HD) were used as quantitative metrics to evaluate the performance of the three models in ECs from different segments. The CT data of 20 patients were randomly selected as the ground truth (GT) masks, and the corresponding delineation results were generated by artificial intelligence (AI). Score differences between the two groups (GT versus AI) and the evaluation consistency were compared. Results: In all patients, there was a significant difference in the 2D DSCs from U-Net, V-Net, and VUMix-Net (p=0.01). In addition, VUMix-Net showed achieved better 3D-DSC and 95HD values. There was a significant difference among the 3D-DSC (mean ± STD) and 95HD values for upper-, middle-, and lower-segment EC (p<0.001), and the middle EC values were the best. In middle-segment EC, VUMix-Net achieved the highest 2D-DSC values (p<0.001) and lowest 95HD values (p=0.044). Conclusion: The new model (VUMix-Net) showed certain advantages in delineating the GTVs of EC. Additionally, it can generate the GTVs of EC that meet clinical requirements and have the same quality as human-generated contours. The system demonstrated the best performance for the ECs of the middle segment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.