Leukocyte Cell Population Data for Hematology Analyzer-Based Distinction of Clonal-versus-Non-Clonal Lymphocytosis: A Real-World Testing Experience

Automated blood counts revealing lymphocytosis necessitate smear reviews. Even expert morphological evaluation may however, fail to differentiate a benign-versus-malignant etiology without further testing. Automated analyser-derived quantitative data on leukocyte cell populations remain undertested for distinguishing such etiologies. Instrument manufacturers claim that if successful, they may be used to generate software flags that help under-resourced laboratories better triage hemogram specimens requiring further testing. We tested the diagnostic accuracy of volume-conductivity-scatter (VCS) indices together with complete blood count (CBC) parameters in such scenarios. We compared LH780-derived (Beckman Coulter, FL, USA) CBC + VCS parameters from patients with clonal lymphoproliferations (n = 42, including 30 chronic lymphocytic leukemia cases) versus 83 controls with absolute or relative lymphocytosis (derivation cohort). Diagnostic performances of 11 logistic regression equations derived were subsequently evaluated on two specific validation cohorts (n = 130 and n = 1465). Clonal lymphocytoses showed significantly lower hemoglobin and higher leukocyte counts but similar lymphocyte percentages (LY %) vis-à-vis controls. The most significant, albeit overlapping predictor of clonality was the absolute lymphocyte count, LY# (47.8 ± 48.4 × 10⁹/L vs. 2.9 ± 1.4 × 10⁹/L in clonal vs. benign cases). In eleven logistic regression equations constructed using four combinatorial approaches, only the models with LY# (highest sensitivity/specificity of 99.3%/100%) and the lymphocytic VCS parameters alone (highest sensitivity/specificity of 76.2%/90.2%) performed consistently in both validation cohorts. Lymphocytic VCS parameters were moderately successful in distinguishing benign-versus-malignant lymphocytes. Other approaches of CBC-plus-VCS parameters did not sustain their initial excellent performances in the validation cohorts, highlighting a need for careful appraisal and better standardization of automated cellular analysis technologies.