ReviewTherapeutic innovation & regulatory science2021
Radiologists and Clinical Trials: Part 1 The Truth About Reader Disagreements.
Review in Therapeutic innovation & regulatory science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Challenges with the PRETEXT staging system: a SIOPEL Radiology Group position paper.Pediatric radiology · 2026Review
- Setting the standard: empirical benchmarks for case-level site-central discordance in radiographic endpoints in oncology clinical trials.Contemporary clinical trials communications · 2026Article
- Tumor metrics imaging core labs: primer for radiologists.Abdominal radiology (New York) · 2026Review
- What are RECIST 1.1 progressions made of? Variability in double-read oncology trials.European radiology · 2026Article
- AI-Powered Lesion-Level Tumor Growth Inhibition Modeling Improves Model Stability and Prognostic Association With PFS.CPT: pharmacometrics & systems pharmacology · 2026Article
- An Explainable Radiomics-Based Classification Model for Sarcoma Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Long-term behaviour of non-functioning pituitary microadenomas: experience from a tertiary care centre in Romania.Frontiers in endocrinology · 2025Article
- A call for objectivity: Radiologists' proposed wishlist for response evaluation in solid tumors (RECIST 1.1).Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- RECIST 1.1 assessments variability: a systematic pictorial review of blinded double reads.Insights into imaging · 2024Review
- Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms.European radiology experimental · 2024Article
- Can blood-based markers predict RECIST progression in non-small cell lung cancer treated with immunotherapy?Journal of cancer research and clinical oncology · 2024Article
- The ins and outs of errors in oncology imaging: the DAC framework for radiologists.Frontiers in oncology · 2024Review
- Iodine density mapping for the diagnosis of acute bowel ischemia using fast kV-switching dual-energy CT.Abdominal radiology (New York) · 2024Article
- The role of clinical imaging in oncology drug development: progress and new challenges.The British journal of radiology · 2023Review
- Inter-rater reliability of retrograde urethrograms.World journal of urology · 2023Article
- Application of the Lugano Classification for Initial Evaluation, Staging, and Response Assessment of Hodgkin and Non-Hodgkin Lymphoma: The PRoLoG Consensus Initiative (Part 2-Technical).Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Article
- Is Artificial Intelligence Replacing Our Radiology Stars? Not Yet!European urology open science · 2023Article
- Breaking down the RECIST 1.1 double read variability in lung trials: What do baseline assessments tell us?Frontiers in oncology · 2023Article
- Can we predict discordant RECIST 1.1 evaluations in double read clinical trials?Frontiers in oncology · 2023Article
- Advancements in Imaging Technologies and AI Integration for Neurodegenerative Disease Management: A Narrative Review.Molecular imagingReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The debate over human visual perception and how medical images should be interpreted have persisted since X-rays were the only imaging technique available. Concerns over rates of disagreement between expert image readers are associated with much of the clinical research and at times driven by the belief that any image endpoint variability is problematic. The deeper understanding of the reasons, value, and risk of disagreement are somewhat siloed, leading, at times, to costly and risky approaches, especially in clinical trials. Although artificial intelligence promises some relief from mistakes, its routine application for assessing tumors within cancer trials is still an aspiration. Our consortium of international experts in medical imaging for drug development research, the Pharma Imaging Network for Therapeutics and Diagnostics (PINTAD), tapped the collective knowledge of its members to ground expectations, summarize common reasons for reader discordance, identify what factors can be controlled and which actions are likely to be effective in reducing discordance. Reinforced by an exhaustive literature review, our work defines the forces that shape reader variability. This review article aims to produce a singular authoritative resource outlining reader performance's practical realities within cancer trials, whether they occur within a clinical or an independent central review.
Indexed as
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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.