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Leveraging AI to Evaluate Minimal Residual Disease Endpoint Surrogacy in Multiple Myeloma
Zexin Ren1, Zixuan Zhao1, Andrew J Cowan2
1George Washington University, Washington, District of Columbia.
Abstract:
Minimal residual disease (MRD) has been endorsed by the FDA Oncology Drugs Advisory Committee as an endpoint for accelerated approval in multiple myeloma based on individual patient data collected from randomized trials. However, emerging data from recent trials were not included. A novel artificial intelligence (AI)-assisted framework is proposed, which automates information identification and extraction, providing up-to-date analyses that confirm moderate trial-level and strong individual patient-level associations between MRD- rates at suspected complete response (MRD-CR) and survival endpoints in multiple myeloma. Specifically, this study utilized an AI-assisted framework that identifies relevant studies and filters critical information to analyze published data via 2 independent objectives. First, we examined the trial-level association using the coefficients of determination (R2) and its 95% confidence intervals (CI) based on published statistics of treatment effects on MRD and various endpoints. Next, we generated synthetic individual patient data with covariates through AI-curated tools to estimate the individual-level association. The AI tool searched for eligible randomized clinical trials (RCT). A total of 20 two-arm comparisons from 19 RCTs were analyzed. Trial-level analysis showed an R2 of 0.71 (95% CI, 0.52-0.89) pooling disease subpopulations. Furthermore, AI techniques were applied to create synthetic individual data, combining information extracted from Kaplan-Meier curves and subgroup analyses from published literatures. Using generated synthetic data, we estimated the individual-level correlation between MRD-CR rates and progression-free survival outcomes with a bivariate copula model and calculated a global odds ratio of 7.28 (95% CI, 5.60-8.95).
Significance:
(i) A novel AI-assisted framework is proposed, which automates information identification and extraction, providing rapid up-to-date analyses. (ii) Moderate trial-level and strong individual patient-level associations between MRD and various clinical endpoints in multiple myeloma are confirmed.
Insights
Minimal residual disease (MRD) is key for Multiple Myeloma drug approval. An AI framework confirms strong links between MRD-CR and survival, supporting its use with updated data.
Area of Science:
- Hematology
- Oncology
- Artificial Intelligence in Medicine
Background:
- Minimal residual disease (MRD) is an FDA-endorsed endpoint for Multiple Myeloma (MM) accelerated approval.
- Existing analyses may not incorporate the latest clinical trial data.
- There is a need for updated, comprehensive assessments of MRD's prognostic value.
Purpose of the Study:
- To develop and validate an AI-assisted framework for automated MRD data extraction and analysis.
- To confirm the association between MRD-CR and survival endpoints in MM using up-to-date evidence.
- To assess both trial-level and individual-patient-level correlations.
Main Methods:
- An AI framework was employed to identify and extract information from relevant randomized clinical trials (RCTs).
- Trial-level association was assessed using coefficients of determination (R²).
- Synthetic individual patient data (IPD) were generated using AI tools for individual-level correlation analysis with survival endpoints like progression-free survival (PFS).
Main Results:
- The AI framework analyzed 20 two-arm comparisons from 19 RCTs.
- Trial-level analysis revealed a moderate association (R² = 0.71; 95% CI 0.52-0.89).
- Individual-level analysis using synthetic IPD showed a strong correlation between MRD-CR and PFS (Global OR = 7.28; 95% CI 5.60-8.95).
Conclusions:
- The AI-assisted framework provides up-to-date analyses confirming the prognostic significance of MRD-CR in MM.
- Both trial-level and individual-patient-level data support MRD-CR as a robust predictor of survival outcomes.
- This approach enhances the evidence base for MRD as an accelerated approval endpoint in MM.
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