Shi, Min

Abstract Measurable residual disease (MRD) assessment by flow cytometry (FC) plays an essential role in prognosis and therapy escalation of B-cell acute lymphoblastic leukemia (B-ALL). However, the high degree of expertise and manual analysis time required limits the availability of this assay. To overcome this limitation, we developed a data-enhancing artificial intelligence (AI) pipeline that accelerates and simplifies MRD analysis. Unaltered FC files from 171 B-ALL MRD-positive and 89 MRD-negative cases were processed through an AI pipeline trained with 31 expert-gated negative controls. Cluster-informed downsampling reduced FC files from 1.2 million to 155 884 cells per case, on average, (87% cellularity reduction), whereas preserving small MRD populations (median, 100% retention for MRD of <1%) and allowing for true percentage MRD estimates using a correction factor. A deep neural network cell classifier automatically identified normal hematopoietic subsets (macro-averaged F1 score of 0.86); and an AI measure of anomaly discriminated B-ALL from benign mononuclear (area under the curve [AUC] of 0.98) or B-lymphoid cells (AUC of 0.94). Manual analysis of AI-enhanced files was completed in only 1.01 minutes per case, on average (standard deviation of ±0.57); with 100% positive agreement with conventional analysis (for MRD of ≥0.01%), 100% negative agreement, and excellent quantitative correlation (R2 = 0.92). Our cloud-based AI-enhancement solution accelerates B-ALL MRD identification without compromising test performance and has the potential of facilitating B-ALL MRD analysis by more clinical laboratories.

Background: Flow cytometric detection of minimal residual disease (MRD) in chronic lymphocytic leukemia (CLL) is complex, time-consuming, and subject to inter-operator variability. Deep neural networks (DNNs) offer potential for standardization and efficiency improvement, but require rigorous validation and monitoring for safe clinical implementation. Methods: We evaluated a DNN-assisted human-in-the-loop approach for CLL MRD detection. Initial validation included method comparison against manual analysis (n = 240), precision studies, and analytical sensitivity verification. Post-implementation monitoring comprised four components: daily electronic quality control, input data drift detection, error analysis, and attribute acceptance sampling. Laboratory efficiency was assessed through a timing study of 161 cases analyzed by five technologists. Results: Method comparison demonstrated 97.5% concordance with manual analysis for qualitative classification (sensitivity 100%, specificity 95%) and excellent correlation for quantitative assessment (r = 0.99, Deming slope = 0.99). Precision studies confirmed high repeatability and within-laboratory precision across multiple operators. Analytical sensitivity was verified at 0.002% MRD. Post-implementation monitoring identified 2.97% of cases (26/874) with input data drift, primarily high-burden CLL and non-CLL neoplasms. Error analysis showed the DNN alone achieved 97% sensitivity compared to human-in-the-loop-reviewed results, with 13 missed cases (1.5%) showing atypical immunophenotypes. Attribute acceptance sampling confirmed 98.8% of reported negative cases were true negatives. The DNN-assisted workflow reduced average analysis time by 60.3% compared to manual analysis (4.2 ± 2.3 vs. 10.5 ± 5.8 min). Conclusions: The implementation of a DNN-assisted approach for CLL MRD detection in a clinical laboratory provides diagnostic performance equivalent to expert manual analysis while substantially reducing analysis time. Comprehensive performance monitoring ensures ongoing safety and effectiveness in routine clinical practice. This approach provides a model for responsible AI integration in clinical laboratories, balancing automation benefits with expert oversight.

Background/Objectives: Acute myeloid leukemia (AML) is a biologically heterogeneous malignancy with a variable prognosis. Despite many patients achieving complete remission, relapse remains common, underscoring the need for effective prognostic markers. Measurable residual disease (MRD) has emerged as a critical prognostic indicator, associated with higher relapse risk and shorter survival. This study reports on our initial experience of MRD detection by flow cytometry in 385 bone marrow samples from 126 AML patients. Methods: The flow cytometry MRD assay, validated according to stringent consensus recommendations, consists of a 3-tube, 10-color panel incorporating a broad spectrum of lineage differentiation markers. Analytical specificity, sensitivity, precision, and reproducibility were evaluated, demonstrating the assay’s robustness. Results: The results reveal distinct immunophenotypic aberrancies in all AML cases, with consistent identification of aberrant immunophenotypes in follow-up specimens. AML MRD was detected in 32 out of 126 patients (25%) and in 77 out of 385 analyses (20%), with a median aberrant blast percentage of 1.87% (range, 0.01–12). A change in immunophenotype was documented in 21% of the MRD-positive cases. MRD positivity detected in the first sample studied was associated with reduced overall survival (HR: 5.153; p < 0.0001). Conclusions: Our findings support the integration of flow cytometric MRD analysis into routine clinical practice to enhance risk stratification and treatment planning for AML patients, as currently recommended by professional guidelines.

Abstract Objectives Large granular lymphocytic leukemia (LGLL) represents a rare neoplasm of mature T cells or natural killer (NK) cells, with an indolent clinical course. Diagnosing LGLL can be challenging because of overlapping features with reactive processes and other mimickers. Methods By presenting 2 challenging cases, we elucidate the differentiation of LGLL from its mimics and highlight potential diagnostic pitfalls. A comprehensive review of the clinicopathologic features of LGLL was conducted. Results Large granular lymphocytic leukemia displays a diverse spectrum of clinical presentations, morphologies, flow cytometric immunophenotypes, and molecular profiles. These features are also encountered in reactive conditions, T-cell clones of uncertain significance, and NK cell clones of uncertain significance. Conclusions In light of the intricate diagnostic landscape, LGLL workup must encompass clinical, morphologic, immunophenotypic, clonal, and molecular findings. Meeting major and minor diagnostic criteria is imperative for the accurate diagnosis of LGLL.

Abstract Several reports of concurrent MYC, BCL2, BCL6, and CCND1 rearrangements in high-grade B-cell lymphoma (HGBL) have been recently described. Herein, we aimed to delineate the scope of this entity through a review of HGBL with a “quadruple-hit” genetic profile identified at our institution. We performed a retrospective review (2015-2023) at our institution of B-cell lymphoma (BCL) cases that were evaluated with concurrent MYC, BCL2, and BCL6 break-apart and IGH::MYC and IGH::CCND1 dual-color dual-fusion fluorescence in situ hybridization studies. Of 203 cases meeting inclusion criteria, 2 (1%) with a quadruple-hit genetic profile were identified. Case 1 represented a 59-year-old female with widespread lymphadenopathy and a diagnosis of HGBL who exhibited primary refractoriness to dose-adjusted etoposide, prednisone, vincristine, cyclophosphamide, doxorubicin, and rituximab (DA-EPOCH-R) chemotherapy. Case 2 represented a 58-year-old male with mediastinal and abdominal lymphadenopathy and a diagnosis of large BCL who died from disease after 1 cycle of DA-EPOCH-R chemotherapy. Similarly, a literature review of 7 previously reported cases of HGBL with a quadruple-hit profile also demonstrated aggressive disease behavior. Our study adds 2 new cases to the rarely encountered quadruple-hit HGBL, and a brief meta-analysis of the 9 available cases indicates aggressive disease behavior conferred by this constellation of genetic events.