ArticleFrontiers in oncology2022
Inter-Observer and Intra-Observer Variability in Gross Tumor Volume Delineation of Primary Esophageal Carcinomas Based on Different Combinations of Diagnostic Multimodal Images.
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 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 8 citations in OpenAlex.
- 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
- A human-AI collaborative workflow for improved gross tumor volume delineation in esophageal cancer radiotherapy.BMC medical imaging · 2026Article
- Margin for compensating displacement of adrenal gland metastasis and fiducial marker along with respiratory phase in real-time motion-tracking radiation therapy.Radiological physics and technology · 2025Article
- Multicentre validation of CT grey-level co-occurrence matrix features for overall survival in primary oesophageal adenocarcinoma.European radiology · 2024Article
- Head and neck automatic multi-organ segmentation on Dual-Energy Computed Tomography.Physics and imaging in radiation oncology · 2024Article
- Clinical adoption of deep learning target auto-segmentation for radiation therapy: challenges, clinical risks, and mitigation strategies.BJR artificial intelligence · 2024Review
- The Role of MRI and PET/CT in Radiotherapy Target Volume Determination in Gastrointestinal Cancers-Review of the Literature.Cancers · 2023Review
- A Retrospective Comparison of CT Imaging and Computational Simulations of Irreversible Electroporation in the Liver.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Purpose: This study aimed to investigate inter-/intra-observer delineation variability in GTVs of primary esophageal carcinomas (ECs) based on planning CT with reference to different combinations of diagnostic multimodal images from endoscopy/EUS, esophagography and FDG-PET/CT. Materials and Methods: Fifty patients with pathologically proven thoracic EC who underwent diagnostic multimodal images before concurrent chemoradiotherapy were enrolled. Five radiation oncologist independently delineated the GTVs based on planning CT only (GTV Results: The intra-/inter-observer variability in the volume and longitudinal length of the GTVs showed no significant differences ( Conclusion: For radiation oncologists with advanced medical imaging training and clinical experience, using diagnostic multimodal images from endoscopy/EUS, esophagography, and FDG-PET/CT could reduce the intra-/inter-observer variability and increase the accuracy of target delineation in primary esophageal carcinomas.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.