Cytometry Part B: Clinical Cytometry最新文献

{"title":"Technical, gating and interpretation recommendations for the partitioning of circulating monocyte subsets assessed by flow cytometry","authors":"Sihem Tarfi,&nbsp;Wolfgang Kern,&nbsp;Elodie Goulas,&nbsp;Dorothée Selimoglu-Buet,&nbsp;Orianne Wagner-Ballon,&nbsp;the CytHem-LMMC","doi":"10.1002/cyto.b.22176","DOIUrl":"10.1002/cyto.b.22176","url":null,"abstract":"<p>The monocyte subset partitioning by flow cytometry, known as “monocyte assay,” is now integrated into the new classifications as a supporting criterion for CMML diagnosis, if a relative accumulation of classical monocytes above 94% of total circulating monocytes is observed. Here we provide clinical flow cytometry laboratories with technical support adapted for the most commonly used cytometers. Step-by-step explanations of the gating strategy developed on whole peripheral blood are presented while underlining the most common difficulties. In a second part, interpretation recommendations of circulating monocyte partitioning from the dedicated French working group “CytHem-LMMC” are shared as well as the main pitfalls, including false positive and false negative cases. The particular flow-defined inflammatory profile is described and the usefulness of the nonclassical monocyte specific marker, namely slan, highlighted. Examples of reporting to the physician with frequent situations encountered when using the monocyte assay are also presented.</p>","PeriodicalId":10883,"journal":{"name":"Cytometry Part B: Clinical Cytometry","volume":"106 3","pages":"203-215"},"PeriodicalIF":3.4,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.b.22176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A lasso and random forest model using flow cytometry data identifies primary myelofibrosis","authors":"Feng Zhang,&nbsp;Ya-Zhe Wang,&nbsp;Yan Chang,&nbsp;Xiao-Ying Yuan,&nbsp;Wei-Hua Shi,&nbsp;Hong-Xia Shi,&nbsp;Jian-Zhen Shen,&nbsp;Yan-Rong Liu","doi":"10.1002/cyto.b.22173","DOIUrl":"10.1002/cyto.b.22173","url":null,"abstract":"<p>Thrombocythemia (ET), polycythemia vera (PV), primary myelofibrosis (PMF), prefibrotic/early (pre-PMF), and overt fibrotic PMF (overt PMF) are classical Philadelphia-Negative (<i>Ph-negative</i>) myeloproliferative neoplasms (MPNs). Differentiating between these types based on morphology and molecular markers is challenging. This study aims to clarify the application of flow cytometry in the diagnosis and differential diagnosis of classical MPNs. This study retrospectively analyzed the immunophenotypes, clinical characteristics, and laboratory findings of 211 <i>Ph-negative</i> MPN patients, including ET, PV, pre-PMF, overt PMF, and 47 controls. Compared to ET and PV, PMF differed in white blood cells, hemoglobin, blast cells in the peripheral blood, abnormal karyotype, and WT1 gene expression. PMF also differed from controls in CD34⁺ cells, granulocyte phenotype, monocyte phenotype, percentage of plasma cells, and dendritic cells. Notably, the PMF group had a significantly lower plasma cell percentage compared with other groups. A lasso and random forest model select five variables (CD34⁺CD19⁺cells and CD34⁺CD38⁻ cells on CD34⁺cells, CD13^dim+CD11b⁻ cells in granulocytes, CD38^str+CD19^+/−plasma, and CD123⁺HLA-DR⁻basophils), which identify PMF with a sensitivity and specificity of 90%. Simultaneously, a classification and regression tree model was constructed using the percentage of CD34⁺CD38⁻ on CD34⁺ cells and platelet counts to distinguish between ET and pre-PMF, with accuracies of 94.3% and 83.9%, respectively. Flow immunophenotyping aids in diagnosing PMF and differentiating between ET and PV. It also helps distinguish pre-PMF from ET and guides treatment decisions.</p>","PeriodicalId":10883,"journal":{"name":"Cytometry Part B: Clinical Cytometry","volume":"106 4","pages":"272-281"},"PeriodicalIF":2.3,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140805114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Possible alternative strategies to implement basophil activation testing in multicentric studies","authors":"Pénélope Bourgoin,&nbsp;Thomas Dupont,&nbsp;Chantal Agabriel,&nbsp;Ania Carsin,&nbsp;Aurélie Verles,&nbsp;Maciej Cabanski,&nbsp;Alessandra Vitaliti,&nbsp;Jean-Marc Busnel","doi":"10.1002/cyto.b.22172","DOIUrl":"10.1002/cyto.b.22172","url":null,"abstract":"<p>The Basophil Activation Test (BAT) enables flow cytometry characterization of basophil reactivity against specific allergenic molecules. The focus now revolves around democratizing this tool, but, as blood sample stability could be challenging, after having developed a simplified approach, herein, we aimed to characterize two strategies for implementing BAT in multicentric studies: store and ship blood before or after sample processing. Fresh heparin- and EDTA-anticoagulated whole blood samples followed both BAT workflows: “collect, store, process &amp; analyze” or “collect, process, store &amp; analyze”. Storage temperatures of 18–25 °C or 2–8 °C and preservation times from 0 to 7 days were considered. Interleukin-3 was also evaluated. With the “collect, store, process &amp; analyze” workflow, heparin-anticoagulated blood and 18–25 °C storage were better than other conditions. While remaining possible, basophil activation exhibited a possible reactivity decay after 24 h. Under the conditions tested, interleukin-3 had no role in enhancing basophil reactivity after storage. Conversely, the “collect, process, store &amp; analyze” workflow demonstrated that either heparin- or EDTA-anticoagulated blood can be processed and kept up to 7 days at 18–25 °C or 2–8 °C before being analyzed. Various strategies can be implemented to integrate BAT in multicentric studies. The “collect, store, process &amp; analyze” workflow remains a simplified logistical approach, but depending on time required to ship from the clinical centers to the reference laboratories, it might not be applicable, or should be used with caution. The “collect, process, store &amp; analyze” workflow may constitute a workflow improvement to provide significant flexibility without impact on basophil reactivity.</p>","PeriodicalId":10883,"journal":{"name":"Cytometry Part B: Clinical Cytometry","volume":"106 5","pages":"392-404"},"PeriodicalIF":2.3,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140561487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of a novel eight-color flow cytometry panel for measurable residual disease assessment of chronic lymphocytic leukemia","authors":"Xiao Chen,&nbsp;Xia Chen,&nbsp;Sishu Zhao,&nbsp;Yu Shi,&nbsp;Ninghan Zhang,&nbsp;Zhen Guo,&nbsp;Chun Qiao,&nbsp;Huimin Jin,&nbsp;Liying Zhu,&nbsp;Huayuan Zhu,&nbsp;Jianyong Li,&nbsp;Yujie Wu","doi":"10.1002/cyto.b.22170","DOIUrl":"10.1002/cyto.b.22170","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Measurable residual disease (MRD) is an important prognostic indicator of chronic lymphocytic leukemia (CLL). Different flow cytometric panels have been developed for the MRD assessment of CLL in Western countries; however, the application of these panels in China remains largely unexplored.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Owing to the requirements for high accuracy, reproducibility, and comparability of MRD assessment in China, we investigated the performance of a flow cytometric approach (CD45-ROR1 panel) to assess MRD in patients with CLL. The European Research Initiative on CLL (ERIC) eight-color panel was used as the “gold standard.”</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The sensitivity, specificity, and concordance rate of the CD45-ROR1 panel in the MRD assessment of CLL were 100% (87/87), 88.5% (23/26), and 97.3% (110/113), respectively. Two of the three inconsistent samples were further verified using next-generation sequencing. In addition, the MRD results obtained from the CD45-ROR1 panel were positively associated with the ERIC eight-color panel results for MRD assessment (<i>R</i> = 0.98, <i>p</i> &lt; 0.0001). MRD detection at low levels (≤1.0%) demonstrated a smaller difference between the two methods (bias, −0.11; 95% CI, −0.90 to 0.68) than that at high levels (&gt;1%). In the reproducibility assessment, the bias was smaller at three data points (within 24, 48, and 72 h) in the CD45-ROR1 panel than in the ERIC eight-color panel. Moreover, MRD levels detected using the CD45-ROR1 panel for the same samples from different laboratories showed a strong statistical correlation (<i>R</i> = 0.99, <i>p</i> &lt; 0.0001) with trivial interlaboratory variation (bias, 0.135; 95% CI, −0.439 to 0.709). In addition, the positivity rate of MRD in the bone marrow samples was higher than that in the peripheral blood samples.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Collectively, this study demonstrated that the CD45-ROR1 panel is a reliable method for MRD assessment of CLL with high sensitivity, reproducibility, and reliability.</p>\u0000 </section>\u0000 </div>","PeriodicalId":10883,"journal":{"name":"Cytometry Part B: Clinical Cytometry","volume":"106 3","pages":"181-191"},"PeriodicalIF":3.4,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.b.22170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140293076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Issue highlights—April 2024","authors":"Neil Came","doi":"10.1002/cyto.b.22171","DOIUrl":"https://doi.org/10.1002/cyto.b.22171","url":null,"abstract":"&lt;p&gt;This issue of &lt;i&gt;Cytometry Part B, Clinical Cytometry&lt;/i&gt; consists of four original articles and four letters to the Editor, bridged by a discussion forum. Although finding common themes between these works is not necessary, some naturally emerged for me under a simple but helpful way of thinking about clinical flow cytometry that I learned from Professor Alberto Orfao's education sessions. To paraphrase, in clinical flow cytometry, we are doing one of three things at any time: identifying, characterizing (as either normal or abnormal) or enumerating cell populations. Fourth, flow cytometry must be interpreted in a broader clinicopathological context. These principles assist in defining the indication and context of use of an assay, which in turn help determine panel design, and other pre-analytical, analytical and post-analytical components. Lastly, this journal recognizes the value of the single case report. While some journals have abandoned them, if well researched, relevant and succinct, they can serve as a useful educational tool or cautionary tale, illustrate the application, strengths or weakness of a guideline, or document rare, interesting cases and other novel phenomena.&lt;/p&gt;&lt;p&gt;Therefore, rather than in order of appearance, I introduce this issue's contents as follows:&lt;/p&gt;&lt;p&gt;Kumar et al. (&lt;span&gt;2024&lt;/span&gt;) provide a nice example of improving the identification of plasma cells for later characterization and enumeration, demonstrating substantial improvement in CD138 expression and, ultimately, plasma cell recovery using a gentler “stain-lyse-no-wash” sample preparation technique compared to their standard “(bulk) lyse-stain-wash” method in 36 paired bone marrow samples, with no adverse effect on the intensity of other antigens in the panel. They changed their practice, using this simpler technique for the surface marker tube on 244 additional samples over 6 years, reserving “lyse-stain-wash” preparation for the analysis of cytoplasmic light chains. Whether this can be applied to myeloma measurable residual disease (MRD) assessment remains to be tested.&lt;/p&gt;&lt;p&gt;The study by Ramalingam et al. (&lt;span&gt;2024&lt;/span&gt;) and letter from Placek et al. (&lt;span&gt;2024&lt;/span&gt;) reinforce that a masterful appreciation of normal B-cell maturation under various clinical conditions is critical for monitoring residual B-acute lymphoblastic leukemia (B-ALL). Ramalingam et al. provide a concise assessment of the immunophenotype of type 0 hematogones (by CD34, CD10, CD45, CD19, CD20, CD22 and CD24 expression) in 61 pediatric patients under various conditions and time points following CD19-targeting, conventional chemotherapy, and hematopoietic stem cell transplantation. While the existence of CD19-negative B-cell precursors (BCP) has been known for some time (Dworzak et al., &lt;span&gt;1998&lt;/span&gt;; Uckun &amp; Ledbetter, &lt;span&gt;1988&lt;/span&gt;), they have, until recently, remained under recognized within the confines of standard B-ALL MRD panels until Cherian et al. devel","PeriodicalId":10883,"journal":{"name":"Cytometry Part B: Clinical Cytometry","volume":"106 2","pages":"89-91"},"PeriodicalIF":3.4,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.b.22171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of cerebrospinal fluid for the diagnosis of CNS lymphoma: Comparison of the ESCCA/ISCCA protocol and real-world data of the CytHem/LOC French network","authors":"Agathe Debliquis,&nbsp;Guido Ahle,&nbsp;Caroline Houillier,&nbsp;Carole Soussain,&nbsp;Khê Hoang-Xuan,&nbsp;Magali Le Garff-Tavernier,&nbsp;CytHem and in partnership with the LOC Network","doi":"10.1002/cyto.b.22169","DOIUrl":"10.1002/cyto.b.22169","url":null,"abstract":"","PeriodicalId":10883,"journal":{"name":"Cytometry Part B: Clinical Cytometry","volume":"106 2","pages":"142-145"},"PeriodicalIF":3.4,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140049033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MAGIC-DR: An interpretable machine-learning guided approach for acute myeloid leukemia measurable residual disease analysis","authors":"Kevin Shopsowitz,&nbsp;Jack Lofroth,&nbsp;Geoffrey Chan,&nbsp;Jubin Kim,&nbsp;Makhan Rana,&nbsp;Ryan Brinkman,&nbsp;Andrew Weng,&nbsp;Nadia Medvedev,&nbsp;Xuehai Wang","doi":"10.1002/cyto.b.22168","DOIUrl":"10.1002/cyto.b.22168","url":null,"abstract":"<p>Multiparameter flow cytometry is widely used for acute myeloid leukemia minimal residual disease testing (AML MRD) but is time consuming and demands substantial expertise. Machine learning offers potential advancements in accuracy and efficiency, but has yet to be widely adopted for this application. To explore this, we trained single cell XGBoost classifiers from 98 diagnostic AML cell populations and 30 MRD negative samples. Performance was assessed by cross-validation. Predictions were integrated with UMAP as a heatmap parameter for an augmented/interactive AML MRD analysis framework, which was benchmarked against traditional MRD analysis for 25 test cases. The results showed that XGBoost achieved a median AUC of 0.97, effectively distinguishing diverse AML cell populations from normal cells. When integrated with UMAP, the classifiers highlighted MRD populations against the background of normal events. Our pipeline, MAGIC-DR, incorporated classifier predictions and UMAP into flow cytometry standard (FCS) files. This enabled a human-in-the-loop machine learning guided MRD workflow. Validation against conventional analysis for 25 MRD samples showed 100% concordance in myeloid blast detection, with MAGIC-DR also identifying several immature monocytic populations not readily found by conventional analysis. In conclusion, Integrating a supervised classifier with unsupervised dimension reduction offers a robust method for AML MRD analysis that can be seamlessly integrated into conventional workflows. Our approach can support and augment human analysis by highlighting abnormal populations that can be gated on for quantification and further assessment. This has the potential to speed up MRD analysis, and potentially improve detection sensitivity for certain AML immunophenotypes.</p>","PeriodicalId":10883,"journal":{"name":"Cytometry Part B: Clinical Cytometry","volume":"106 4","pages":"239-251"},"PeriodicalIF":2.3,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139982544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of a flow cytometry test for routine monitoring of B cell maturation antigen targeted CAR in peripheral blood","authors":"Won-Ho Lee,&nbsp;Charlotte E. Graham,&nbsp;Hadley R. Wiggin,&nbsp;Hannah K. Nolan,&nbsp;Kiana J. Graham,&nbsp;Felix Korell,&nbsp;Mark B. Leick,&nbsp;Alexis L. Barselau,&nbsp;Estelle Emmanuel-Alejandro,&nbsp;Michael A. Trailor,&nbsp;Juliane M. Gildea,&nbsp;Frederic Preffer,&nbsp;Matthew J. Frigault,&nbsp;Marcela V. Maus,&nbsp;Kathleen M. E. Gallagher","doi":"10.1002/cyto.b.22165","DOIUrl":"10.1002/cyto.b.22165","url":null,"abstract":"<p>Chimeric antigen receptor (CAR) modified T cell therapies targeting BCMA have displayed impressive activity in the treatment of multiple myeloma. There are currently two FDA licensed products, ciltacabtagene autoleucel and idecabtagene vicleucel, for treating relapsed and refractory disease. Although correlative analyses performed by product manufacturers have been reported in clinical trials, there are limited options for reliable BCMA CAR T detection assays for physicians and researchers looking to explore it as a biomarker for clinical outcome. Given the known association of CAR T cell expansion kinetics with toxicity and response, being able to quantify BCMA CAR T cells routinely and accurately in the blood of patients can serve as a valuable asset. Here, we optimized an accurate and sensitive flow cytometry test using a PE-conjugated soluble BCMA protein, with a lower limit of quantitation of 0.19% of CD3+ T cells, suitable for use as a routine assay for monitoring the frequency of BCMA CAR T cells in the blood of patients receiving either ciltacabtagene autoleucel or idecabtagene vicleucel.</p>","PeriodicalId":10883,"journal":{"name":"Cytometry Part B: Clinical Cytometry","volume":"106 3","pages":"162-170"},"PeriodicalIF":3.4,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139989514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recommendations for using artificial intelligence in clinical flow cytometry","authors":"David P. Ng,&nbsp;Paul D. Simonson,&nbsp;Attila Tarnok,&nbsp;Fabienne Lucas,&nbsp;Wolfgang Kern,&nbsp;Nina Rolf,&nbsp;Goce Bogdanoski,&nbsp;Cherie Green,&nbsp;Ryan R. Brinkman,&nbsp;Kamila Czechowska","doi":"10.1002/cyto.b.22166","DOIUrl":"10.1002/cyto.b.22166","url":null,"abstract":"<p>Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and traditionally requires close inspection of digital data by hematopathologists with expert domain knowledge. Advances in artificial intelligence (AI) are transferable to flow cytometry and have the potential to improve efficiency and prioritization of cases, reduce errors, and highlight fundamental, previously unrecognized associations with underlying biological processes. As a multidisciplinary group of stakeholders, we review a range of critical considerations for appropriately applying AI to clinical flow cytometry, including use case identification, low and high risk use cases, validation, revalidation, computational considerations, and the present regulatory frameworks surrounding AI in clinical medicine. In particular, we provide practical guidance for the development, implementation, and suggestions for potential regulation of AI-based methods in the clinical flow cytometry laboratory. We expect these recommendations to be a helpful initial framework of reference, which will also require additional updates as the field matures.</p>","PeriodicalId":10883,"journal":{"name":"Cytometry Part B: Clinical Cytometry","volume":"106 4","pages":"228-238"},"PeriodicalIF":2.3,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139971305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Translating the regulatory landscape of medical devices to create fit-for-purpose artificial intelligence (AI) cytometry solutions","authors":"Goce Bogdanoski,&nbsp;Fabienne Lucas,&nbsp;Wolfgang Kern,&nbsp;Kamila Czechowska","doi":"10.1002/cyto.b.22167","DOIUrl":"10.1002/cyto.b.22167","url":null,"abstract":"<p>The implementation of medical software and artificial intelligence (AI) algorithms into routine clinical cytometry diagnostic practice requires a thorough understanding of regulatory requirements and challenges throughout the cytometry software product lifecycle. To provide cytometry software developers, computational scientists, researchers, industry professionals, and diagnostic physicians/pathologists with an introduction to European Union (EU) and United States (US) regulatory frameworks. Informed by community feedback and needs assessment established during two international cytometry workshops, this article provides an overview of regulatory landscapes as they pertain to the application of AI, AI-enabled medical devices, and Software as a Medical Device in diagnostic flow cytometry. Evolving regulatory frameworks are discussed, and specific examples regarding cytometry instruments, analysis software and clinical flow cytometry in-vitro diagnostic assays are provided. An important consideration for cytometry software development is the modular approach. As such, modules can be segregated and treated as independent components based on the medical purpose and risk and become subjected to a range of context-dependent compliance and regulatory requirements throughout their life cycle. Knowledge of regulatory and compliance requirements enhances the communication and collaboration between developers, researchers, end-users and regulators. This connection is essential to translate scientific innovation into diagnostic practice and to continue to shape the development and revision of new policies, standards, and approaches.</p>","PeriodicalId":10883,"journal":{"name":"Cytometry Part B: Clinical Cytometry","volume":"106 4","pages":"294-307"},"PeriodicalIF":2.3,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139939780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}