Ms. Madhuri Madasu Profile

Madhuri Madasu

at PAREXEL International Corp.

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Author

Publications (5)

Proceedings Article | 3 April 2023 Poster + Paper

Predictive adjudication rate modeling for prioritizing case distribution in BICR for oncology clinical trials

Manish Sharma, Madhuri Madasu, Surabhi Bajpai, Kimberly Fernandes-Thomas, Sree Sudha Kota, Yibin Shao, Rajesh Selvaraj, Rajesh Kaja, Kira Cheng, Joy Luo

Proceedings Volume 12467, 124671E (2023) https://doi.org/10.1117/12.2654344

KEYWORDS: Autoregressive models, Oncology, Modeling, Tumors, Data modeling, Linear regression, Clinical trials, Tumor growth modeling, Medical imaging, Algorithm development

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Purpose: Blinded independent central review is recommended by the US FDA for registration of oncology trials as it provides bias-free image assessment and avoidance of potential unblinding of patient data. Double read with adjudication is a highly advocated review model used in such trials. Disagreement between readers is natural and inevitable. Radiological disagreement rates or Adjudication Rate (AR) of 30–65% are reported by several papers since 1959 for different oncologic indications. The aim of the study is to develop and use an algorithm to identify reader pair with predicted high AR and investigate if overall study AR can be kept constant or improved by assigning less cases to a reader pair with high AR.
Methods: A retrospective analysis was performed of 285 subjects with 3351 post-baseline timepoints reviewed by board-certified radiologist reviewers using Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria in a BICR set up. The reader adjudication rate was calculated and analyzed throughout the duration of review. The distribution of cases per reader and distribution of cases per reader pair was calculated and overall study AR, and each reader pair AR were calculated.
Data Analysis Methods: Data was prepared and analyzed using linear regression with MS Excel and R programming script (R version 4.1.2 (2021-11-01) -- "Bird Hippie" augmented by RStudio 2022.07.0+548 "Spotted Wakerobin".
Results: Using the data at completion of 50% reads, predicted AR per reader pair was found to correlate well for five out of six reader pairs on the study. This predicted AR can then be used to assign more cases to reader pair with low AR and less cases to reader pair with high AR to keep study level variability low.
Conclusions: Predictive modeling of AR using linear regression can provide an insight into variability of each reader pair, which in turn determines the AR of that reader pair and collectively determines study AR. However, it is still not clear to what level of prioritization in case assignment to specific pairs can be considered acceptable and not artificial.

Proceedings Article | 3 April 2023 Poster + Paper

Utilizing network analysis in blinded independent central review for clinical trials as adjudication agreement dashboard

Manish Sharma, Sree Sudha Kota, Surabhi Bajpai, Kemberly Fernandes-Thomas, Madhuri Madasu, Yibin Shao, Rajesh Kaja, Rajesh Selvaraj, Kira Cheng, Joy Luo

Proceedings Volume 12467, 124671C (2023) https://doi.org/10.1117/12.2653991

KEYWORDS: Network security, Visualization, Statistical analysis, Biological research, Multiple sclerosis, Data analysis, Clinical trials, Visual analytics, Telecommunication networks, Open source software

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Purpose: Variability in observer performance BICR is common but not well understood and various measures like AR, AAR, RDI help quantify it which leads to multiple complex data points. Network analysis uses mathematically based algorithms to characterize the components of a network of entities and identifying, visualizing, and analysing their relationships. In a network, variables are represented by nodes, the relationships represented by edges between these nodes. The visualization technique involves mapping relationships among entities based on the symmetry or asymmetry of data. Maps from data points generated during double read adjudication study can provide the performance of each reader pair primarily based on AAR.
Methods: Adjudication data from four oncology clinical trials with 2163 subjects, 16937 post-baseline responses was analyzed. Performance metrics included number of cases, adjudication rate, adjudication agreement rate for each read and reader pair. The data were aggregated and prepared for network analysis in Python-a high-level, cross-platform, and open-sourced programming language released under a GPL-compatible license. Python Software Foundation (PSF), a non-profit organization, holds the copyright. Url-https://www.python.org Version 3.9.0
Results: This graphic visualization provides simplistic organization of a complicated data analysis and supports the quality monitoring process of independent reviews. The tool provides a snapshot of the review performance of all the readers in the trial allowing the study team to investigate and intervene in a timely manner with the intent of supporting robust and accurate data analysis.
Conclusions: Network analysis plots for reader performance metrics in BICR provide excellent visual mapping to interpret multiple critical metrics in a single plot which would otherwise require multiple plots and tables. Timely review of these plots during the trial can help demonstrate the effectiveness of interventions as well.

Proceedings Article | 4 April 2022 Paper

Using reader disagreement index as a tool for monitoring impact on read quality due to reader fatigue in central reviewers

Manish Sharma, Madhuri Madasu, Sree Sudha Kota, Surabhi Bajpai, Yibin Shao, Srinivas Pasupuleti, Michael O’Connor

Proceedings Volume 12035, 120350J (2022) https://doi.org/10.1117/12.2613082

KEYWORDS: Clinical trials, Oncology, Visualization, Medical imaging, Medicine, Tumor growth modeling, Statistical analysis, Tumors, Solids, Radiology

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Purpose: Fatigue may lead to high medical errors by the radiologists. Fatigue is often described as feelings of weakness, lack of energy, and a desire to rest, and is associated with impairments in the ability to function. Visual fatigue has importance in medical imaging as errors (false-negatives) are relatively common. Blinded independent central review (BICR) is a well-used method employed in many oncology registration trials. Ongoing monitoring of radiologist “reviewer” performance is both good clinical trial practice and a requirement by regulatory authorities. We use reader disagreement index (RDI) as a potential tool to identify reader fatigue and compare reader fatigue in reviewers performing single versus multiple types of study.

Methods: A retrospective analysis of reviewers’ RDI in four different clinical trials were performed. Fourteen reviewers’ performance was analyzed with data for 3750 subjects having a total of 15105 timepoints across all clinical trials. These individual trial reviews were conducted by 14 board-certified radiologist reviewers using several established imaging assessment trial criteria. The objective of the study was to establish RDI as an effective tool to analyze if it could be a good surrogate marker for quality impacted by reader fatigue.

Results: The results indicate the RDI can be used as a tool to track reader quality which in turn may be able to predict reader fatigue. In the random pool of readers and studies analyzed, we did not notice any major trend or impact on read quality given that these trials were anyway actively monitored for read volume distribution and quality.

Conclusions: Fatigue may lead to high medical errors by the radiologists. RDI can be used as a good surrogate for read quality to monitor reader fatigue. Based on the results, it can be said that it is better to undertake more cases in a single study than undertake less number of cases in different types of studies to prevent reader fatigue.

Proceedings Article | 4 April 2022 Paper

Satisfaction of search (SOS) error and new lesions identification on imaging in central review for clinical trials

Manish Sharma, Sree Sudha Kota, Madhuri Madasu, Surabhi Bajpai, Yibin Shao, Srinivas Pasupuleti, Michael O’Connor

Proceedings Volume 12035, 1203509 (2022) https://doi.org/10.1117/12.2612995

KEYWORDS: Tumors, Gold, Diagnostics, Error analysis, Clinical trials, Solids, Chest imaging, Therapeutics, Statistical analysis, Radiology

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Proceedings Article | 16 March 2020 Presentation + Paper

Using “Reader Disagreement Index” as a predictive reviewer performance monitoring tool for timely intervention

Manish Sharma, J. Michael O'Connor, Anitha Singareddy, Madhuri Madasu, Nicholas Enus, Kassel Fotinos-Hoyer, Yibin Shao

Proceedings Volume 11316, 113160A (2020) https://doi.org/10.1117/12.2549980

KEYWORDS: Oncology, Clinical trials, Statistical analysis, Analytical research

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Purpose: Blinded Independent Central Review (BICR) is a well-accepted method employed in many oncology registration trials. Ongoing monitoring radiologist “reader” performance is both good clinical trial practice and a requirement by regulatory authorities. We continue to use Reader Disagreement Index (RDI) as an important measure in BICR. In this work we studied RDI as an early indicator to identify an outlier reader during the monitoring of reader performance in BICR. Early indication would enable early intervention and thus possibly improve trial outcomes.
Methods: We performed a retrospective analysis of readers’ RDIs in nineteen different clinical trials. Ninety-two reader performances were examined at five intervals in each trial. These individual trial reviews were conducted by forty-three board-certified radiologist readers using several established imaging assessment trial criteria. The objective was to see how well RDI performance above a threshold at progressive monitoring intervals would “flag” a potential overall end-point performance “issue” for that specific reader.
Results: We present results for the prediction of exceeding threshold (one standard deviation above a study mean RDI). Sensitivity, Specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) were determined for the predicted performance outcomes. We explored interpreting multiple “flags” for each trial to improve the aforementioned metrics.
Conclusions: One would expect that a “flag” of RDI exceeding threshold at an early-stage would likely give a useful prediction of end-point reader performance. We refined our methods to use multiple flags which enable statistically improved Specificity and PPV. Improved predictive capability at early stage intervals coupled with persistent monitoring across subsequent intervals will enable trial managers to focus on specific readers. An earlier indication of possible reader performance issues can permit proactive intervention and enhance good trial practices.

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