Trial reportEuropean journal of radiology2013
Exploring intra- and inter-reader variability in uni-dimensional, bi-dimensional, and volumetric measurements of solid tumors on CT scans reconstructed at different slice intervals.
Trial report in European journal of radiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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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.
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Who cites it
64 citing papers in PubMed.
- Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases.European radiology experimental · 2020Trial
- Discrepancies of assessments in a RECIST 1.1 phase II clinical trial - association between adjudication rate and variability in images and tumors selection.Cancer imaging : the official publication of the International Cancer Imaging Society · 2018Trial
- Yttrium-90 radioembolization treatment for unresectable hepatocellular carcinoma: a single-centre prognostic factors analysis.Medical oncology (Northwood, London, England) · 2017Trial
- AI-driven volumetric approach for automatic chemotherapy response assessment in colorectal liver metastases.European radiology · 2026Article
- Radiologic 3D tumor volume predicts cervical metastasis in oral squamous cell carcinoma.Clinical oral investigations · 2026Article
- Assessing the reliability of Response Evaluation Criteria In Solid Tumors (RECIST): a systematic review of the factors contributing to inter-observer variability.Insights into imaging · 2026Article
- Deep learning for accurate tumour volume measurement and prediction of therapy response in paediatric osteosarcoma.European radiology · 2026Article
- Fully Automated AI-Based Lymph Node Measurements in Chest CT: Accuracy and Reproducibility Compared with Multi-Reader Assessment.Diagnostics (Basel, Switzerland) · 2026Article
- A Systematic Comparative Analysis of Tumor Size Models Based on Erlotinib Clinical Data in Advanced NSCLC.CPT: pharmacometrics & systems pharmacology · 2025Article
- Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging.Tomography (Ann Arbor, Mich.) · 2025Review
- Efficacy assessment in phase I clinical trials: endpoints and challenges.Annals of oncology : official journal of the European Society for Medical Oncology · 2025Review
- Towards automatical tumor segmentation in radiomics: a comparative analysis of various methods and radiologists for both region extraction and downstream diagnosis.BMC medical imaging · 2025Article
- Noninvasive MGMT-promotor methylation prediction in high grade gliomas using conventional MRI and deep learning-based segmentations.Frontiers in neuroscience · 2025Article
- Hierarchical classification of early microscopic lung nodule based on cascade network.Health information science and systems · 2024Article
- A Retrospective Evaluation of the Treatment Effects of Rituximab in Patients with Progressive and Symptomatic Fibrosing Mediastinitis.Annals of the American Thoracic Society · 2024Article
- Deep Learning Models Capture Histological Disease Activity in Crohn's Disease and Ulcerative Colitis with High Fidelity.Journal of Crohn's & colitis · 2024Article
- Beneficial Use of the Combination of Gemcitabine and Dacarbazine in Advanced Soft Tissue Sarcomas: Real-World Data.Cancers · 2024Article
- Deep learning-based cardiothoracic ratio measurement on chest radiograph: accuracy improvement without self-annotation.Quantitative imaging in medicine and surgery · 2023Article
- Criteria for the translation of radiomics into clinically useful tests.Nature reviews. Clinical oncology · 2023Review
- Breaking down the RECIST 1.1 double read variability in lung trials: What do baseline assessments tell us?Frontiers in oncology · 2023Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectiveUnderstanding magnitudes of variability when measuring tumor size may be valuable in improving detection of tumor change and thus evaluating tumor response to therapy in clinical trials and care. Our study explored intra- and inter-reader variability of tumor uni-dimensional (1D), bi-dimensional (2D), and volumetric (VOL) measurements using manual and computer-aided methods (CAM) on CT scans reconstructed at different slice intervals. MATERIALS AND
methodsRaw CT data from 30 patients enrolled in oncology clinical trials was reconstructed at 5, 2.5, and 1.25 mm slice intervals. 118 lesions in the lungs, liver, and lymph nodes were analyzed. For each lesion, two independent radiologists manually and, separately, using computer software, measured the maximum diameter (1D), maximum perpendicular diameter, and volume (CAM only). One of them blindly repeated the measurements. Intra- and inter-reader variability for the manual method and CAM were analyzed using linear mixed-effects models and Bland-Altman method.
resultsFor the three slice intervals, the maximum coefficients of variation for manual intra-/inter-reader variability were 6.9%/9.0% (1D) and 12.3%/18.0% (2D), and for CAM were 5.4%/9.3% (1D), 11.3%/18.8% (2D) and 9.3%/18.0% (VOL). Maximal 95% reference ranges for the percentage difference in intra-reader measurements for manual 1D and 2D, and CAM VOL were (-15.5%, 25.8%), (-27.1%, 51.6%), and (-22.3%, 33.6%), respectively.
conclusionsVariability in measuring the diameter and volume of solid tumors, manually and by CAM, is affected by CT slice interval. The 2.5mm slice interval provides the least measurement variability. Among the three techniques, 2D has the greatest measurement variability compared to 1D and 3D.
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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.