ArticleFrontiers in oncology2022
Gross Tumor Volume Definition and Comparative Assessment for Esophageal Squamous Cell Carcinoma From 3D
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 7 papers.
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Who cites it
7 citing papers in PubMed.
- 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
- Evaluation of PET-detected nodal upstaging and planning target volume expansion in esophageal cancer radiotherapy planning.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Article
- Machine learning applications in the detection and treatment of esophageal cancer.Discover oncology · 2026Review
- AI-Guided Delineation of Gross Tumor Volume for Body Tumors: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer.Cancers · 2024Review
- Article
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Authors and funding
7 authors.
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Abstract
Background: The accurate definition of gross tumor volume (GTV) of esophageal squamous cell carcinoma (ESCC) can promote precise irradiation field determination, and further achieve the radiotherapy curative effect. This retrospective study is intended to assess the applicability of leveraging deep learning-based method to automatically define the GTV from 3D Methods: We perform experiments on a clinical cohort with 164 Results: We perform 4-fold cross-validation for evaluation, reporting the values of DSC, HD, and MSD as 0.72 ± 0.02, 11.87 ± 4.20 mm, and 2.43 ± 0.60 mm (mean ± standard deviation), respectively. Pearson correlations (R Conclusions: The predicted tumors correspond well with the manual ground truth. The proposed GTV estimation approach ETECIM is more precise than the most commonly used voxel volume summation method. The ground truth GTVs can be solved out due to the good linear correlation with the predicted results. Deep learning-based method shows its promising in GTV definition and clinical radiotherapy application.
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