Routine use of immunophenotype by flow cytometry in tissues with suspected hematological malignancies

Antoni Martínez1, Marta Aymerich, Mireia Castillo

  • 1Hematopathology Unit, Department of Pathology, Hospital Clinic, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.

Insights

Flow cytometry (FCM) aids in diagnosing hematological malignancies from tissue biopsies, offering rapid and reliable results. This method effectively identifies rare cancers and clonality in B-cell lymphomas, improving diagnostic accuracy.

Area of Science:

  • Hematopathology
  • Immunophenotyping
  • Diagnostic Cytometry

Background:

  • Immunophenotype is crucial for diagnosing hematological malignancies.
  • Flow cytometry (FCM) is typically used for blood samples, not tissue biopsies.
  • This study investigates FCM's utility in diagnosing hematological disorders from tissue biopsies.

Purpose of the Study:

  • To evaluate the role and effectiveness of flow cytometry (FCM) in diagnosing hematological disorders from tissue biopsies.
  • To compare FCM with standard morphology and immunohistochemistry (IHC) for biopsy analysis.

Main Methods:

  • Analyzed 422 consecutive tissue biopsies using morphology, immunohistochemistry (IHC), and FCM.
  • FCM results were obtained within 3 hours and interpreted independently.
  • Compared FCM findings with morphology and IHC results.

Main Results:

  • FCM showed a strong correlation with malignant disease (218/250 cases), except for Hodgkin disease.
  • FCM demonstrated high positive predictive value (1) and identified light chain restriction in B-cell lymphomas (182/201).
  • FCM enabled rapid diagnosis of rare malignancies and detection of double pathologies.

Conclusions:

  • FCM is a fast, reliable method for phenotyping tissue samples in diagnosing hematological malignancies.
  • FCM excels at detecting infrequent cancers, specific phenotypes, and clonality in B-cell lymphomas.
  • FCM aids in diagnosing composite or double pathologies by recognizing multiple cell populations simultaneously.
Abstract

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