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Detection and Enrichment of Rare Antigen-specific B Cells for Analysis of Phenotype and Function
Published on: February 16, 2017
Characterization and automatic screening of reactive and abnormal neoplastic B lymphoid cells from peripheral blood
S Alférez1, A Merino2, L Bigorra1,2
1Matematica Aplicada III, Technical University of Catalonia, Barcelona, Spain.
Insights
This study developed an automated image-based system for classifying lymphoid cells. The system accurately distinguishes normal, reactive, and abnormal lymphocytes, aiding in the detection of various B-cell lymphomas.
Area of Science:
- Hematology
- Computational Pathology
- Medical Imaging
Background:
- Lymphoid cell classification is crucial for diagnosing hematological malignancies.
- Current methods can be labor-intensive and subjective.
- Automated image analysis offers potential for improved efficiency and accuracy.
Purpose of the Study:
- To develop and validate an automated, image-based system for characterizing and recognizing heterogeneous lymphoid cells in peripheral blood.
- To differentiate between normal, reactive, and various abnormal lymphocyte subtypes, including B-cell lymphomas.
Main Methods:
- Utilized 4389 images from 105 patients, selected by pathologists.
- Extracted geometric, color (six spaces), and texture features for cell characterization.
- Developed a recognition system using support vector machines trained on the image dataset.
Main Results:
- Achieved 97.67% accuracy in classifying three groups: normal, abnormal, and reactive lymphocytes.
- Attained 91.23% accuracy in discriminating five specific abnormal lymphoid cell groups.
- Demonstrated high performance on independent test sets from new patients.
Conclusions:
- The automated system effectively screens lymphocytes, distinguishing between malignancy and non-malignancy.
- The ability to discriminate specific abnormal lymphoid cell types shows promise for automated screening of B-cell lymphomas.
- This image-based approach could serve as a valuable tool for identifying blood involvement in lymphomas.
Introduction:
The objective was to advance in the automatic, image-based, characterization and recognition of a heterogeneous set of lymphoid cells from peripheral blood, including normal, reactive, and five groups of abnormal lymphocytes: hairy cells, mantle cells, follicular lymphoma, chronic lymphocytic leukemia, and prolymphocytes.
Methods:
A number of 4389 images from 105 patients were selected by pathologists, based on morphologic visual appearance, from patients whose diagnosis was confirmed by all the remaining complementary tests. Besides geometry, new color and texture features were extracted using six alternative color spaces to obtain rich information to characterize the cell groups. The recognition system was designed using support vector machines trained with the whole image set.
Results:
In the experimental tests, individual sets of images from 21 new patients were analyzed by the trained recognition system and compared with the true diagnosis. An overall recognition accuracy of 97.67% was achieved when the cell screening was performed into three groups: normal lymphocytes, abnormal lymphoid cells, and reactive lymphocytes. The accuracy of the whole experimental study was 91.23% when considering the further discrimination of the abnormal lymphoid cells into the specific five groups.
Conclusion:
The excellent automatic screening of the three groups of normal, reactive, and abnormal lymphocytes is useful as it discriminates between malignancy and not malignancy. The discrimination of the five groups of abnormal lymphoid cells is encouraging toward the idea that the system could be an automated image-based screening method to identify blood involvement by a variety of B lymphomas.
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