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Clinical Trial Summary

Double reads in blinded independent central reviews (BICRs) are recommended to control the quality of trials but they are prone to discordances. We analyzed inter-reader discordances in a pool of lung cancer trials using RECIST 1.1.

Clinical Trial Description

In the past decade, the lung cancer treatment landscape has dramatically evolved, increasingly branching out thanks to better understanding of disease mechanisms of action, novel technologies, and some amount of serendipity in drug development. Today, approximately 2500 clinical trials registered on are about to recruit or are actively recruiting in order to investigate new therapeutics of lung cancer, offering new hope to patients for better survival and for improvements in quality of life. Blinded independent central reviews (BICRs) are advocated in clinical trials to in-dependently verify endpoints and control bias that might result from errors in response or progression assessments. In the BICR settings with double reads, the medical images are reviewed by two independent readers blinded to the results of the other reader, the study treatment, the investigator assessment, and some pre-defined clinical information. The double-reading paradigm creates the possibility for discordance between the two readers; therefore, a third radiologist is involved to make the final decision of the evaluation outcome. The monitoring of reader performance is required by regulatory bodies to ensure data quality and reliability. At the trial level, a high adjudication rate could be an alert of poor quality at the study level, and a low number of endorsements from a given reader would raise concerns about the reliability of that specific reader. Therefore, relevant key performance indicators (KPIs) must be designed and implemented before starting the reads; these allow the study monitor to trigger corrective actions accordingly. A pooled analysis of 79 oncology clinical trials showed that the proportion of cases requiring adjudication among the 11 lung cancer trials included in the analysis was 38% (95% CI: 37-40%). However, this study was general to all cancer types and did not included details on discrepancy root cause or recently approved novel therapeutics. Considering the atypical response patterns provided by those drugs, we thought it prudent to provide an update on reader performance specific to new therapeutics in lung cancer. Focusing on BICRs in assessing novel drugs, the aim of this study was to analyze a pool of lung trials using RECIST 1.1, document the proportion of reader discrepancies, and provide suggestions to aid in improving the read consistency of future trials by estimating relevant KPIs. ;

Study Design

Related Conditions & MeSH terms

NCT number NCT05038826
Study type Observational
Source Median Technologies
Status Active, not recruiting
Start date February 1, 2021
Completion date December 2021

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