ReviewInsights into imaging2024
RECIST 1.1 assessments variability: a systematic pictorial review of blinded double reads.
Review in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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
14 citing papers in PubMed.
- Main factors associated with variability in the assessment of the objective response rate. A re-analysis of the PHEREXA phase 3 clinical trial.British journal of clinical pharmacology · 2026Trial
- Contrasting Shamblin classification with tumor volume in predicting morbidity and mortality in carotid body tumor resection.Journal of vascular surgery cases and innovative techniques · 2026Article
- Setting the standard: empirical benchmarks for case-level site-central discordance in radiographic endpoints in oncology clinical trials.Contemporary clinical trials communications · 2026Article
- Longitudinal methylated ctDNA increases predict immunotherapy progression across solid tumors.The journal of liquid biopsy · 2026Article
- Comments on "Three‑year overall survival in unresectable hepatocellular carcinoma treated with atezolizumab plus bevacizumab".Hepatology international · 2026Article
- Article
- What are RECIST 1.1 progressions made of? Variability in double-read oncology trials.European radiology · 2026Article
- Baseline CT imaging parameters predicting overall and progression-free survival for patients with pulmonary metastases from soft tissue and bone sarcoma.European radiology · 2026Article
- Review
- A CT Dataset with RECIST Measurements and Comprehensive Segmentation Masks for Tumors and Lymph Nodes.Scientific data · 2026Article
- A single-center external validation and refinement of the 9th TNM staging system for thymic epithelial tumors: a scheme enhancing prognostic stratification.Frontiers in oncology · 2026Article
- Evaluating the tumor response assessment of modified-RECIST 1.1 in advance non-small cell lung cancer: aTranslational lung cancer research · 2025Article
- Large Language Models in Cancer Imaging: Applications and Future Perspectives.Journal of clinical medicine · 2025Review
- Artificial intelligence-enabled early intracranial volumetric response predicts systemic progression-free survival in the phase III CROWN study.Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reader variability is intrinsic to radiologic oncology assessments, necessitating measures to enhance consistency and accuracy. RECIST 1.1 criteria play a crucial role in mitigating this variability by standardizing evaluations, aiming to establish an accepted "truth" confirmed by histology or patient survival. Clinical trials utilize Blind Independent Centralized Review (BICR) techniques to manage variability, employing double reads and adjudicators to address inter-observer discordance effectively. It is essential to dissect the root causes of variability in response assessments, with a specific focus on the factors influencing RECIST evaluations. We propose proactive measures for radiologists to address variability sources such as radiologist expertise, image quality, and accessibility of contextual information, which significantly impact interpretation and assessment precision. Adherence to standardization and RECIST guidelines is pivotal in diminishing variability and ensuring uniform results across studies. Variability factors, including lesion selection, new lesion appearance, and confirmation bias, can have profound implications on assessment accuracy and interpretation, underscoring the importance of identifying and addressing these factors. Delving into the causes of variability aids in enhancing the accuracy and consistency of response assessments in oncology, underscoring the role of standardized evaluation protocols and mitigating risk factors that contribute to variability. Access to contextual information is crucial. CRITICAL RELEVANCE STATEMENT: By understanding the causes of diagnosis variability, we can enhance the accuracy and consistency of response assessments in oncology, ultimately improving patient care and clinical outcomes. KEY POINTS: Baseline lesion selection and detection of new lesions play a major role in the occurrence of discordance. Image interpretation is influenced by contextual information, the lack of which can lead to diagnostic uncertainty. Radiologists must be trained in RECIST criteria to reduce errors and variability.
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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.