Yaoming Chen

Background: Multiple myeloma (MM) is an incurable hematologic malignancy, and the majority of patients eventually relapse due to persistent myeloma cells. While minimal residual disease (MRD) reflects tumor burden, the host immune system also plays a crucial role in disease control. Our study aimed to evaluate MRD, complete blood count (CBC), and immune cell profiles in MM patients treated with bortezomib/adriamycin/dexamethasone (PAD) chemotherapy followed by autologous stem cell transplantation (ASCT) to determine their prognostic value and interplay. Objectives: This study aims to identify markers indicative of a favorable prognosis in MM patients. Methods: CBC data were collected from 93 MM patients at diagnosis, prior to ASCT, and 3 months post-ASCT. Immune cell profiles were assessed via flow cytometry using fresh peripheral blood samples from a subset of 33 prior to ASCT and 3 months post-ASCT. We subsequently investigated the associations between MRD status and prognosis, the predictive value of CBC, and the longitudinal changes in immune cell profiles and their correlation with clinical outcomes. Results: An increased frequency of negative immunomodulatory cell subsets and activated T lymphocytes prior to ASCT were associated with a poor prognosis. Conversely, lower levels of exhausted T lymphocytes, alongside higher levels of functional T cells and marginal zone B cells post-ASCT, predicted a favorable prognosis. Conclusions: Our study demonstrates that pre-ASCT immune status, post-ASCT immune reconstitution, and MRD status at 3 months post-ASCT are closely associated with clinical outcomes in MM patients. These findings underscore the critical importance of regular immune monitoring in the comprehensive management of MM.

We developed an artificial intelligence (AI) model that evaluates the feasibility of AI-assisted multiparameter flow cytometry (MFC) diagnosis of acute leukemia. Two hundred acute leukemia patients and 94 patients with cytopenia(s) or hematocytosis were selected to study the AI application in MFC diagnosis of acute leukemia. The kappa test analyzed the consistency of the diagnostic results and the immunophenotype of acute leukemia. Bland–Altman and Pearson analyses evaluated the consistency and correlation of the abnormal cell proportion between the AI and manual methods. The AI analysis time for each case (83.72 ± 23.90 s, mean ± SD) was significantly shorter than the average time for manual analysis (15.64 ± 7.16 min, mean ± SD). The total consistency of diagnostic results was 0.976 (kappa (κ) = 0.963). The Bland–Altman evaluation of the abnormal cell proportion between the AI analysis and manual analysis showed that the bias ± SD was 0.752 ± 6.646, and the 95% limit of agreement was from −12.775 to 13.779 (p = 0.1225). The total consistency of the AI immunophenotypic diagnosis and the manual results was 0.889 (kappa, 0.775). The consistency and speedup of the AI-assisted workflow indicate its promising clinical application.