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Lawrence B. Holzman

Lawrence B. Holzman

Verified

University of Pennsylvania · Rehabilitation Medicine

Active 1952–2024

h-index72
Citations15.7k
Papers22675 last 5y
Funding$91.2M2 active
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Research topics

  • Pathology
  • Medicine
  • Artificial Intelligence
  • Urology
  • Internal medicine
  • Computer Science
  • Anatomy
  • Radiology

Selected publications

  • Kidney Biopsy Features Most Predictive of Clinical Outcomes in the Spectrum of Minimal Change Disease and Focal Segmental Glomerulosclerosis

    Journal of the American Society of Nephrology · 2022 · 50 citations

    • Medicine
    • Pathology
    • Radiology

    BACKGROUND: Heterogeneity in disease course and treatment response among patients with MCD/FSGS necessitates a granular evaluation of kidney tissue features. This study aimed to identify histologic and ultrastructural descriptors of structural changes most predictive of clinical outcomes in the Nephrotic Syndrome Study Network (NEPTUNE). METHODS: Forty-eight histologic (37 glomerular, 9 tubulointerstitial, 2 vascular) and 20 ultrastructural descriptors were quantified by applying the NEPTUNE Digital Pathology Scoring System to NEPTUNE kidney biopsies. Outcomes included time from biopsy to disease progression, first complete remission of proteinuria, and treatment response. Relative importance of pathology and clinical predictors was obtained from random forest models, and predictive discrimination was assessed. RESULTS: , global sclerosis or segmental sclerosis and interstitial fibrosis/tubular atrophy-and novel features, including adhesion, interstitial foam cells, deflation, periglomerular fibrosis, mononuclear white blood cells, endothelial cell abnormalities, microvillous transformation, and acute tubular injury. CONCLUSIONS: The most predictive descriptors of clinical outcomes among MCD/FSGS patients reflected structural changes in multiple renal compartments. Reporting these descriptors should be standardized to guide prognostication of proteinuric glomerular diseases.

  • Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains

    Kidney International · 2020 · 193 citations

    • Artificial Intelligence
    • Computer Science
    • Pathology
  • Ultrastructural Characterization of Proteinuric Patients Predicts Clinical Outcomes

    Journal of the American Society of Nephrology · 2020 · 44 citations

    • Medicine
    • Internal medicine
    • Pathology

    BACKGROUND: The analysis and reporting of glomerular features ascertained by electron microscopy are limited to few parameters with minimal predictive value, despite some contributions to disease diagnoses. METHODS: , or ESKD), and linear mixed models for longitudinal eGFR measures. RESULTS: Intrarater and interrater reproducibility was >0.60 for 12 out of 12 and seven out of 12 descriptors, respectively. Individual podocyte descriptors such as effacement and microvillous transformation were associated with complete remission, whereas endothelial cell and glomerular basement membrane abnormalities were associated with progression. We identified six descriptor-based clusters with distinct electron microscopy profiles and clinical outcomes. Patients in a cluster with more prominent foot process effacement and microvillous transformation had the highest rates of complete proteinuria remission, whereas patients in clusters with extensive loss of primary processes and endothelial cell damage had the highest rates of the composite progression outcome. CONCLUSIONS: Systematic analysis of electron microscopic findings reveals clusters of findings associated with either proteinuria remission or disease progression.

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