Leukemia research · 2023
Enhancing morphological analysis of peripheral blood cells in chronic lymphocytic leukemia with an artificial intelligence-based tool.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Wang Yan, Liu Hailing, Wang Hui, Wu Yujie, Qiu Hairong, Qiao Chun, Cao Lei, Zhang Jianfu, Li Jianyong, Fan Lei, Wang Rong
Аннотация
BACKGROUND: Real-time monitoring is essential for the management of chronic lymphocytic leukemia (CLL) patients. Utilizing peripheral blood is advantageous due to its affordability and convenience. Existing methods of assessing peripheral blood films have limitations that include lack of automation, dependence on personal experience, and low repeatability and reproducibility. To overcome these challenges, we have designed an artificial intelligence-driven system that provides a clinical perspective to objectively evaluate morphologic features in CLL patients' blood cells.
METHODS: Based on our center's CLL dataset, we developed an automated algorithm using a deep convolutional neural network to precisely identify regions of interest on blood films and used the well-established Visual Geometry Group-16 as the encoder to segment cells and extract morphological features. This tool enabled us to extract morphological features of all lymphocytes for subsequent analysis.
RESULTS: Our study's lymphocyte identification had a recall of 0.96 and an F1 score of 0.97. Cluster analysis identified three clear, morphological groups of lymphocytes that reflect distinct stages of disease development to some extent. To investigate the longitudinal evolution of lymphocyte, we extracted cellular morphology parameters at various time points from the same patient. The results showed some similar trends to those observed in the aforementioned cluster analysis. Correlation analysis further supports the prognostic potential of cell morphology-based parameters.
CONCLUSION: Our study provides valuable insights and potential avenues for further exploration of lymphocyte dynamics in CLL. Investigating morphological changes may help in determining the optimal timing for intervening with CLL patients, but further research is needed.
METHODS: Based on our center's CLL dataset, we developed an automated algorithm using a deep convolutional neural network to precisely identify regions of interest on blood films and used the well-established Visual Geometry Group-16 as the encoder to segment cells and extract morphological features. This tool enabled us to extract morphological features of all lymphocytes for subsequent analysis.
RESULTS: Our study's lymphocyte identification had a recall of 0.96 and an F1 score of 0.97. Cluster analysis identified three clear, morphological groups of lymphocytes that reflect distinct stages of disease development to some extent. To investigate the longitudinal evolution of lymphocyte, we extracted cellular morphology parameters at various time points from the same patient. The results showed some similar trends to those observed in the aforementioned cluster analysis. Correlation analysis further supports the prognostic potential of cell morphology-based parameters.
CONCLUSION: Our study provides valuable insights and potential avenues for further exploration of lymphocyte dynamics in CLL. Investigating morphological changes may help in determining the optimal timing for intervening with CLL patients, but further research is needed.