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Consensus for Flow Cytometry Clinical Report on Multiple Myeloma: A Multicenter Harmonization Process Merging
Iole Cordone1, Rachele Amodeo2, Silvia Bellesi3
1Department of Research, Advanced Diagnostic and Technological Innovation, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy.
Cancers
|April 13, 2023
Summary
This study standardized flow cytometry reporting for multiple myeloma, improving diagnostic accuracy. A consensus among expert laboratories ensures reliable clinical decision-making for plasma cell disorders.
Area of Science:
- Hematology
- Immunology
- Clinical Diagnostics
Background:
- Flow cytometry is crucial for distinguishing normal from clonal plasma cells in multiple myeloma.
- Established response criteria exist, but flow cytometry data reporting lacks standardization across centers.
- This variability can hinder consistent clinical interpretation and decision-making.
Purpose of the Study:
- To develop a uniform and shared reporting system for flow cytometry data in multiple myeloma.
- To identify essential data points for clinical reports through a collaborative, multi-stage process.
- To minimize subjective interpretation and potential bias in flow cytometry analysis.
Main Methods:
- A multicenter study involving 8 expert laboratories and 12 clinical hematology units.
- Utilized multiple-choice questionnaires to gather consensus on data reporting at each process stage.
- Established a 75% agreement threshold among laboratories for data inclusion in the final report.
Main Results:
- Achieved a consensus on essential data for flow cytometry reports in multiple myeloma.
- Defined a standardized process from pre-analytics to data evaluation.
- Ensured operational autonomy for participating laboratories while promoting uniformity.
Conclusions:
- The developed reporting system limits subjective interpretation and highlights potential biases.
- Standardized flow cytometry reporting enhances the reliability of clinical decision-making for plasma cell disorders.
- This collaborative effort provides a robust framework for consistent diagnostic evaluation.

