Real-world impact of differences in the WHO and ICC classifications of non-Hodgkin lymphoma: a LEO Cohort Study analysis.

Recent introduction of two different lymphoma classifications has raised concerns about consistency in diagnosis, management, and clinical trial enrollment. Data from a large cohort reflecting real-world clinical practice suggest that differences between the classifications will impact <1% of non-Hodgkin lymphomas.

The unnecessary use of short tandem repeat testing on bone marrow samples in patients after 1 year following allogeneic hematopoietic stem cell transplant.

OBJECTIVES: To determine whether the information provided by short tandem repeat (STR) testing and bone marrow (BM) biopsy specimens following hematopoietic stem cell transplant (HSCT) provides redundant information, leading to test overutilization, without additional clinical benefit. METHODS: Cases with synchronous STR and flow cytometric immunophenotyping (FCI) testing, as part of the BM evaluation, were assessed for STR/FCI concordance. RESULTS: Of 1199 cases (410 patients), we found the overall concordance between STR and FCI was 93%, with most cases (1063) classified as STR-/FCI-. Of all discordant cases, 75 (6%) were STR+/FCI-, with only 5 (6.7%) cases best explained as identification of disease relapse. Eight cases were STR-/FCI+, representing relapsed/residual disease. Analysis of cases 1 year or more from transplant (54% of all cases) indicated only 9 (1.5%) were STR+/FCI-, and none uniquely identified relapse. CONCLUSIONS: These data suggest that STR analysis performed 1 year or more post-HSCT does not identify unknown cases of relapse. Furthermore, while STR testing is critical for identifying graft failure/rejection within the first year posttransplant, FCI appears superior to STR at detecting late relapses with low-level disease. Therefore, STR testing from patients 1 year or more post-HSCT may be unnecessary, as BM biopsy evaluation is sufficient to identify disease relapse.

SAMHD1 expression contributes to doxorubicin resistance and predicts survival outcomes in diffuse large B-cell lymphoma patients.

Diffuse large B-cell lymphoma (DLBCL) is a commonly diagnosed, aggressive non-Hodgkin's lymphoma. While R-CHOP chemoimmunotherapy is potentially curative, about 40% of DLBCL patients will fail, highlighting the need to identify biomarkers to optimize management. SAMHD1 has a dNTPase-independent role in promoting resection to facilitate DNA double-strand break (DSB) repair by homologous recombination. We evaluated the relationship of SAMHD1 levels with sensitivity to DSB-sensitizing agents in DLBCL cells and the association of SAMHD1 expression with clinical outcomes in 79 DLBCL patients treated with definitive therapy and an independent cohort dataset of 234 DLBCL patients. Low SAMHD1 expression, Vpx-mediated, or siRNA-mediated degradation/depletion in DLBCL cells was associated with greater sensitivity to doxorubicin and PARP inhibitors. On Kaplan-Meier log-rank survival analysis, low SAMHD1 expression was associated with improved overall survival (OS), which on subset analysis remained significant only in patients with advanced stage (III-IV) and moderate to high risk (2-5 International Prognostic Index (IPI)). The association of low SAMHD1 expression with improved OS remained significant on multivariate analysis independent of other adverse factors, including IPI, and was validated in an independent cohort. Our findings suggest that SAMHD1 expression mediates doxorubicin resistance and may be an important prognostic biomarker in advanced, higher-risk DLBCL patients.

The Lymphoma Epidemiology of Outcomes cohort study: Design, baseline characteristics, and early outcomes.

To address the current and long-term unmet health needs of the growing population of non-Hodgkin lymphoma (NHL) patients, we established the Lymphoma Epidemiology of Outcomes (LEO) cohort study (NCT02736357; https://leocohort.org/). A total of 7735 newly diagnosed patients aged 18 years and older with NHL were prospectively enrolled from 7/1/2015 to 5/31/2020 at 8 academic centers in the United States. The median age at diagnosis was 62 years (range, 18-99). Participants came from 49 US states and included 538 Black/African-Americans (AA), 822 Hispanics (regardless of race), 3386 women, 716 age <40 years, and 1513 rural residents. At study baseline, we abstracted clinical, pathology, and treatment data; banked serum/plasma (N = 5883, 76.0%) and germline DNA (N = 5465, 70.7%); constructed tissue microarrays for four major NHL subtypes (N = 1189); and collected quality of life (N = 5281, 68.3%) and epidemiologic risk factor (N = 4489, 58.0%) data. Through August 2022, there were 1492 deaths. Compared to population-based SEER data (2015-2019), LEO participants had a similar distribution of gender, AA race, Hispanic ethnicity, and NHL subtype, while LEO was underrepresented for patients who were Asian and aged 80 years and above. Observed overall survival rates for LEO at 1 and 2 years were similar to population-based SEER rates for indolent B-cell (follicular and marginal zone) and T-cell lymphomas, but were 10%-15% higher than SEER rates for aggressive B-cell subtypes (diffuse large B-cell and mantle cell). The LEO cohort is a robust and comprehensive national resource to address the role of clinical, tumor, host genetic, epidemiologic, and other biologic factors in NHL prognosis and survivorship.

Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning.

BACKGROUND: c-MYC and BCL2 positivity are important prognostic factors for diffuse large B-cell lymphoma. However, manual quantification is subject to significant intra- and inter-observer variability. We developed an automated method for quantification in whole-slide images of tissue sections where manual quantification requires evaluating large areas of tissue with possibly heterogeneous staining. We train this method using annotations of tumor positivity in smaller tissue microarray cores where expression and staining are more homogeneous and then translate this model to whole-slide images. METHODS: Our method applies a technique called attention-based multiple instance learning to regress the proportion of c-MYC-positive and BCL2-positive tumor cells from pathologist-scored tissue microarray cores. This technique does not require annotation of individual cell nuclei and is trained instead on core-level annotations of percent tumor positivity. We translate this model to scoring of whole-slide images by tessellating the slide into smaller core-sized tissue regions and calculating an aggregate score. Our method was trained on a public tissue microarray dataset from Stanford and applied to whole-slide images from a geographically diverse multi-center cohort produced by the Lymphoma Epidemiology of Outcomes study. RESULTS: In tissue microarrays, the automated method had Pearson correlations of 0.843 and 0.919 with pathologist scores for c-MYC and BCL2, respectively. When utilizing standard clinical thresholds, the sensitivity/specificity of our method was 0.743 / 0.963 for c-MYC and 0.938 / 0.951 for BCL2. For double-expressors, sensitivity and specificity were 0.720 and 0.974. When translated to the external WSI dataset scored by two pathologists, Pearson correlation was 0.753 & 0.883 for c-MYC and 0.749 & 0.765 for BCL2, and sensitivity/specificity was 0.857/0.991 & 0.706/0.930 for c-MYC, 0.856/0.719 & 0.855/0.690 for BCL2, and 0.890/1.00 & 0.598/0.952 for double-expressors. Survival analysis demonstrates that for progression-free survival, model-predicted TMA scores significantly stratify double-expressors and non double-expressors (p = 0.0345), whereas pathologist scores do not (p = 0.128). CONCLUSIONS: We conclude that proportion of positive stains can be regressed using attention-based multiple instance learning, that these models generalize well to whole slide images, and that our models can provide non-inferior stratification of progression-free survival outcomes.

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry.

The current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models that make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia (area under the receiver operating characteristic curve [AUROC] 0.961) and accurately differentiates AML vs B- and T-lymphoblastic leukemia (AUROC 0.965). Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) (AUROC 0.814), and NPM1 variants (AUROC 0.807). Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.

Immunosuppression is a conserved driver of tuberculosis susceptibility.

Mycobacterium tuberculosis ( Mtb ) causes 1.6 million deaths a year 1 . However, no individual mouse model fully recapitulates the hallmarks of human tuberculosis disease. Here we report that a comparison across three different susceptible mouse models identifies Mtb -induced gene signatures that predict active TB disease in humans significantly better than a signature from the standard C57BL/6 mouse model. An increase in lung myeloid cells, including neutrophils, was conserved across the susceptible mouse models, mimicking the neutrophilic inflammation observed in humans 2,3 . Myeloid cells in the susceptible models and non-human primates exhibited high expression of immunosuppressive molecules including the IL-1 receptor antagonist, which inhibits IL-1 signaling. Prior reports have suggested that excessive IL-1 signaling impairs Mtb control 4-6 . By contrast, we found that enhancement of IL-1 signaling via deletion of IL-1 receptor antagonist promoted bacterial control in all three susceptible mouse models. IL-1 signaling enhanced cytokine production by lymphoid and stromal cells, suggesting a mechanism for IL-1 signaling in promoting Mtb control. Thus, we propose that myeloid cell expression of immunosuppressive molecules is a conserved mechanism exacerbating Mtb disease in mice, non-human primates, and humans.

Early cellular mechanisms of type I interferon-driven susceptibility to tuberculosis.

Mycobacterium tuberculosis (Mtb) causes 1.6 million deaths annually. Active tuberculosis correlates with a neutrophil-driven type I interferon (IFN) signature, but the cellular mechanisms underlying tuberculosis pathogenesis remain poorly understood. We found that interstitial macrophages (IMs) and plasmacytoid dendritic cells (pDCs) are dominant producers of type I IFN during Mtb infection in mice and non-human primates, and pDCs localize near human Mtb granulomas. Depletion of pDCs reduces Mtb burdens, implicating pDCs in tuberculosis pathogenesis. During IFN-driven disease, we observe abundant DNA-containing neutrophil extracellular traps (NETs) described to activate pDCs. Cell-type-specific disruption of the type I IFN receptor suggests that IFNs act on IMs to inhibit Mtb control. Single-cell RNA sequencing (scRNA-seq) indicates that type I IFN-responsive cells are defective in their response to IFNγ, a cytokine critical for Mtb control. We propose that pDC-derived type I IFNs act on IMs to permit bacterial replication, driving further neutrophil recruitment and active tuberculosis disease.

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia using Flow Cytometry.

Current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in both the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models which make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1,820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia [AUROC 0.961] and accurately differentiates AML versus B- and T-lymphoblastic leukemia [AUROC 0.965]. Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) [AUROC 0.814], and NPM1 variants [AUROC 0.807]. Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.

Regulation of antigen-specific T cell infiltration and spatial architecture in multiple myeloma and premalignancy.

Entry of antigen-specific T cells into human tumors is critical for immunotherapy, but the underlying mechanisms are poorly understood. Here, we combined high-dimensional spatial analyses with in vitro and in vivo modeling to study the mechanisms underlying immune infiltration in human multiple myeloma (MM) and its precursor monoclonal gammopathy of undetermined significance (MGUS). Clustered tumor growth was a feature of MM but not MGUS biopsies, and this growth pattern was reproduced in humanized mouse models. MM biopsies exhibited intralesional as well as spatial heterogeneity, with coexistence of T cell-rich and T cell-sparse regions and the presence of areas of T cell exclusion. In vitro studies demonstrated that T cell entry into MM clusters was regulated by agonistic signals and CD2-CD58 interactions. Upon adoptive transfer, antigen-specific T cells localized to the tumor site but required in situ DC-mediated antigen presentation for tumor entry. C-type lectin domain family 9 member A-positive (CLEC9A+) DCs appeared to mark portals of entry for gradients of T cell infiltration in MM biopsies, and their proximity to T cell factor 1-positive (TCF1+) T cells correlated with disease state and risk status. These data illustrate a role for tumor-associated DCs and in situ activation in promoting the infiltration of antigen-specific T cells in MM and provide insights into spatial alterations in tumor/immune cells with malignant evolution.

Profiling endogenous, environmental, and infectious disease mutational signatures in blastic plasmacytoid dendritic cell neoplasms.

BACKGROUND: Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare hematopoietic disease derived from plasmacytoid dendritic lineage cells. The disease typically shows skin as well as frequent bone marrow and peripheral blood involvement. However, the pathogenesis of this disease is still not well understood. While somatic point mutations and genetic rearrangements have been described in BPDCN, the types and origins of these mutations and relationships to other cancer types is not well understood. MATERIALS AND METHODS: To probe the origins of BPDCN, we analyzed the exome sequence data of 9 tumor-normal pair cases of BPDCN. We utilized SignatureAnalyzer, SigProfiler and a custom microbial analysis pipeline to understand the relevance of endogenous and environmental mutagenic processes. RESULTS: Our results identified a significant tobacco exposure and aging genetic signature as well as signatures related to nucleotide excision repair deficiency, ultra violet (UV) exposure, and endogenous deamination in BPDCN. We also assessed the samples for microbial infectious disease organisms but did not find a link to a microbial etiology. CONCLUSION: The identification of a tobacco exposure and aging genetic signature in patients with BPDCN suggests that environmental and endogenous genetic changes may be central to the oncogenesis of BPDCN.

An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears.

The pathologic diagnosis of bone marrow disorders relies in part on the microscopic analysis of bone marrow aspirate (BMA) smears and the manual counting of marrow nucleated cells to obtain a differential cell count (DCC). This manual process has significant limitations, including the analysis of only a small subset of optimal slide areas and nucleated cells, as well as interobserver variability due to differences in cell selection and classification. To address these shortcomings, we developed an automated machine learning-based pipeline for obtaining 11-component DCCs on whole-slide BMAs. This pipeline uses a sequential process of identifying optimal BMA regions with high proportions of marrow nucleated cells, detecting individual cells within these optimal areas, and classifying these cells into 1 of 11 DCC components. Convolutional neural network models were trained on 396,048 BMA region, 28,914 cell boundary, and 1,510,976 cell class images from manual annotations. The resulting automated pipeline produced 11-component DCCs that demonstrated a high statistical and diagnostic concordance with manual DCCs among a heterogeneous group of testing BMA slides with varying pathologies and cellularities. Additionally, we demonstrated that an automated analysis can reduce the intraslide variance in DCCs by analyzing the whole slide and marrow nucleated cells within all optimal regions. Finally, the pipeline outputs of region classification, cell detection, and cell classification can be visualized using whole-slide image analysis software. This study demonstrates the feasibility of a fully automated pipeline for generating DCCs on scanned whole-slide BMA images, with the potential for improving the current standard of practice for utilizing BMA smears in the laboratory analysis of hematologic disorders.

Lightweight, open source, easy-use algorithm and web service for paraprotein screening using spatial frequency domain analysis of electrophoresis studies.

Introduction: Serum protein electrophoresis (SPEP) is commonly used to detect monoclonal paraproteins to meet laboratory diagnostic criteria for plasma cell neoplasms. We propose an automated screening method for paraprotein detection that uses minimal computational resources for training and deployment. Methods: A model screening for paraproteins based on the presence of high-frequency components in the spatial frequency spectrum of the SPEP densitometry curve was calibrated on a set of 330 samples, and evaluated on representative (n=110) and external (n=1,321) test sets. The model takes as input a patient's serum densitometry curve and a standardized control curve and outputs a prediction of whether a paraprotein is present. We built an interactive web application allowing users to easily perform paraprotein screening given inputs for densitometry curves, as well as a macro-enabled spreadsheet for easy automated screening. Results: When tuned to maximize likelihood ratio with minimum sensitivity 0.90, the model achieved AUC 0.90, sensitivity 0.90, positive-predictive value 0.64, specificity 0.55, and accuracy 0.72 in the representative test set. In the external test set, the model achieved AUC 0.90, sensitivity 0.97, positive-predictive value 0.42, specificity 0.29, and accuracy 0.52. A subset analysis showed sensitivities of 0.90, 0.96, and 1.0 in detecting low (0.1-0.5 g/dL), medium (0.5-3.0 g/dL), and high paraprotein levels (≥3.0 g/dL), respectively. We have released a web service allowing users to score their own SPEP data, and also released the algorithm and application programming interface in an open-source package allowing users to customize the model to their needs. Conclusions: We developed a proof of concept for an automated method for paraprotein screening using only the characteristics of the SPEP curve. Future work should focus on testing the method with other laboratory data including immunofixation gels, as well as incorporation of outside data sources including clinical data.

Herpes simplex virus lymphadenitis is associated with tumor reduction in a patient with chronic lymphocytic leukemia.

BackgroundHerpes simplex virus lymphadenitis (HSVL) is an unusual presentation of HSV reactivation in patients with chronic lymphocytic leukemia (CLL) and is characterized by systemic symptoms and no herpetic lesions. The immune responses during HSVL have not, to our knowledge, been studied.MethodsPeripheral blood and lymph node (LN) samples were obtained from a patient with HSVL. HSV-2 viral load, antibody levels, B and T cell responses, cytokine levels, and tumor burden were measured.ResultsThe patient showed HSV-2 viremia for at least 6 weeks. During this period, she had a robust HSV-specific antibody response with neutralizing and antibody-dependent cellular phagocytotic activity. Activated (HLA-DR+, CD38+) CD4+ and CD8+ T cells increased 18-fold, and HSV-specific CD8+ T cells in the blood were detected at higher numbers. HSV-specific B and T cell responses were also detected in the LN. Markedly elevated levels of proinflammatory cytokines in the blood were also observed. Surprisingly, a sustained decrease in CLL tumor burden without CLL-directed therapy was observed with this and also a prior episode of HSVL.ConclusionHSVL should be considered part of the differential diagnosis in patients with CLL who present with signs and symptoms of aggressive lymphoma transformation. An interesting finding was the sustained tumor control after 2 episodes of HSVL in this patient. A possible explanation for the reduction in tumor burden may be that the HSV-specific response served as an adjuvant for the activation of tumor-specific or bystander T cells. Studies in additional patients with CLL are needed to confirm and extend these findings.FundingNIH grants 4T32CA160040, UL1TR002378, and 5U19AI057266 and NIH contracts 75N93019C00063 and HHSN261200800001E. Neil W. and William S. Elkin Fellowship (Winship Cancer Institute).

Venetoclax sensitivity in multiple myeloma is associated with B-cell gene expression.

Venetoclax is a highly potent, selective BCL2 inhibitor capable of inducing apoptosis in cells dependent on BCL2 for survival. Most myeloma is MCL1-dependent; however, a subset of myeloma enriched for translocation t(11;14) is codependent on BCL2 and thus sensitive to venetoclax. The biology underlying this heterogeneity remains poorly understood. We show that knockdown of cyclin D1 does not induce resistance to venetoclax, arguing against a direct role for cyclin D1 in venetoclax sensitivity. To identify other factors contributing to venetoclax response, we studied a panel of 31 myeloma cell lines and 25 patient samples tested for venetoclax sensitivity. In cell lines, we corroborated our previous observation that BIM binding to BCL2 correlates with venetoclax response and further showed that knockout of BIM results in decreased venetoclax sensitivity. RNA-sequencing analysis identified expression of B-cell genes as enriched in venetoclax-sensitive myeloma, although no single gene consistently delineated sensitive and resistant cells. However, a panel of cell surface makers correlated well with ex vivo prediction of venetoclax response in 21 patient samples and may serve as a biomarker independent of t(11;14). Assay for transposase-accessible chromatin sequencing of myeloma cell lines also identified an epigenetic program in venetoclax-sensitive cells that was more similar to B cells than that of venetoclax-resistant cells, as well as enrichment for basic leucine zipper domain-binding motifs such as BATF. Together, these data indicate that remnants of B-cell biology are associated with BCL2 dependency and point to novel biomarkers of venetoclax-sensitive myeloma independent of t(11;14).

Chromatin Accessibility Identifies Regulatory Elements Predictive of Gene Expression and Disease Outcome in Multiple Myeloma.

PURPOSE: Multiple myeloma is a malignancy of plasma cells. Extensive genetic and transcriptional characterization of myeloma has identified subtypes with prognostic and therapeutic implications. In contrast, relatively little is known about the myeloma epigenome. EXPERIMENTAL DESIGN: CD138+CD38+ myeloma cells were isolated from fresh bone marrow aspirate or the same aspirate after freezing for 1-6 months. Gene expression and chromatin accessibility were compared between fresh and frozen samples by RNA sequencing (RNA-seq) and assay for transpose accessible chromatin sequencing (ATAC-seq). Chromatin accessible regions were used to identify regulatory RNA expression in more than 700 samples from newly diagnosed patients in the Multiple Myeloma Research Foundation CoMMpass trial (NCT01454297). RESULTS: Gene expression and chromatin accessibility of cryopreserved myeloma recapitulated that of freshly isolated samples. ATAC-seq performed on a series of biobanked specimens identified thousands of chromatin accessible regions with hundreds being highly coordinated with gene expression. More than 4,700 of these chromatin accessible regions were transcribed in newly diagnosed myelomas from the CoMMpass trial. Regulatory element activity alone recapitulated myeloma gene expression subtypes, and in particular myeloma subtypes with immunoglobulin heavy chain translocations were defined by transcription of distal regulatory elements. Moreover, enhancer activity predicted oncogene expression implicating gene regulatory mechanisms in aggressive myeloma. CONCLUSIONS: These data demonstrate the feasibility of using biobanked specimens for retrospective studies of the myeloma epigenome and illustrate the unique enhancer landscapes of myeloma subtypes that are coupled to gene expression and disease progression.

Clinician-ordered peripheral blood smears have low reimbursement and variable clinical value: a three-institution study, with suggestions for operational efficiency.

BACKGROUND: Peripheral blood smears are performed to evaluate a variety of hematologic and non-hematologic disorders. At the authors' institutions, clinician requests for pathologist-performed blood smear reviews have increased in recent years. Blood smears may contribute significantly to pathologists' workloads, yet their clinical value is variable, and professional reimbursement rates are low. This study aimed to identify clinical scenarios in which smear review is likely to provide value beyond automated laboratory testing. METHODS: Blood smear review practices at three institutions were examined, and the indications for and interpretations of clinician-initiated smears were reviewed to determine the percentage of smears with potential added clinical value. A smear review was classified as having added clinical value if the pathologist's interpretation included a morphologic abnormality that had the potential to impact patient management, and that could not be diagnosed by automated complete blood count with white blood cell differential or automated iron studies alone. RESULTS: Among 515 consecutive clinician-requested smears performed during the study timeframes, 23% yielded interpretations with potential added clinical value. When sorted by indication, 25, 19, and 13% of smear reviews requested for white blood cell abnormalities, red blood cell abnormalities, and platelet abnormalities, respectively, had findings with potential added clinical value. The proportion of smears with potential clinical value differed significantly across these three categories (p = 0.0375). CONCLUSIONS: Smear review ordering practices across three institutions resulted in a minority of smears with potential added clinical value. The likelihood of value varied according to the indication for which the smear was requested. Given this, efforts to improve the utilization and efficiency of smear review are worthwhile. Solutions are discussed, including engaging laboratory staff, educating clinicians, and modifying technology systems.