ArticleFrontiers in oncology2023
Breaking down the RECIST 1.1 double read variability in lung trials: What do baseline assessments tell us?
Article in Frontiers in oncology, 2023. 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, 7 citations in OpenAlex.
- What are RECIST 1.1 progressions made of? Variability in double-read oncology trials.European radiology · 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
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer.Cell reports. Medicine · 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
- Reproducing RECIST lesion selection via machine learning: Insights into intra and inter-radiologist variation.European journal of radiology open · 2024Article
- Can we predict discordant RECIST 1.1 evaluations in double read clinical trials?Frontiers in oncology · 2023Article
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Authors and funding
2 authors.
Funding
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Abstract
Background: In clinical trials with imaging, Blinded Independent Central Review (BICR) with double reads ensures data blinding and reduces bias in drug evaluations. As double reads can cause discrepancies, evaluations require close monitoring which substantially increases clinical trial costs. We sought to document the variability of double reads at baseline, and variabilities across individual readers and lung trials. Material and methods: We retrospectively analyzed data from five BICR clinical trials evaluating 1720 lung cancer patients treated with immunotherapy or targeted therapy. Fifteen radiologists were involved. The variability was analyzed using a set of 71 features derived from tumor selection, measurements, and disease location. We selected a subset of readers that evaluated ≥50 patients in ≥two trials, to compare individual reader's selections. Finally, we evaluated inter-trial homogeneity using a subset of patients for whom both readers assessed the exact same disease locations. Significance level was 0.05. Multiple pair-wise comparisons of continuous variables and proportions were performed using one-way ANOVA and Marascuilo procedure, respectively. Results: Across trials, on average per patient, target lesion (TL) number ranged 1.9 to 3.0, sum of tumor diameter (SOD) 57.1 to 91.9 mm. MeanSOD=83.7 mm. In four trials, MeanSOD of double reads was significantly different. Less than 10% of patients had TLs selected in completely different organs and 43.5% had at least one selected in different organs. Discrepancies in disease locations happened mainly in lymph nodes (20.1%) and bones (12.2%). Discrepancies in measurable disease happened mainly in lung (19.6%). Between individual readers, the MeanSOD and disease selection were significantly different (p<0.001). In inter-trials comparisons, on average per patient, the number of selected TLs ranged 2.1 to 2.8, MeanSOD 61.0 to 92.4 mm. Trials were significantly different in MeanSOD (p<0.0001) and average number of selected TLs (p=0.007). The proportion of patients having one of the top diseases was significantly different only between two trials for lung. Significant differences were observed for all other disease locations (p<0.05). Conclusions: We found significant double read variabilities at baseline, evidence of reading patterns and a means to compare trials. Clinical trial reliability is influenced by the interplay of readers, patients and trial design.
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