Differential cardiac remodeling: patterns of cardiomyocyte morphometry in multiple patient conditions

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ID: 323007
2026
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Abstract
AIMS: Cardiac remodeling drives heart failure (HF), yet microstructural drivers in human tissue remain poorly defined due to the limited resolution of clinical imaging. This study sought to quantitatively assess cardiomyocyte morphometry and its relationship with clinical phenotypes. METHODS AND RESULTS: We developed MyoSAM, a deep learning framework for quantitative morphometric characterization of H&E-stained whole-slide images (N = 334 patients). Cardiomyocyte size was quantified using the Myocyte Maximum Inscribed Circle (MMIC) radius, along with measures of nuclear size and perinuclear-halo size, interstitial and macrodomain stromal fractions. Independent associations between these histologic features and clinical variables were assessed using multivariable regression and patient-level linear mixed-effects models to account for clustering of cellular measurements within individuals.MMIC radius was the strongest histological predictor of left ventricular mass index (LVMI; p < 0.001) and was positively correlated with clinical HF (Pearson's r = 0.77, p < 0.001). In severe coronary artery disease (CAD), the association between LVMI and MMIC radius was significantly attenuated (p = 0.001), indicating that myocyte hypertrophy was diminished despite increased mass; instead, remodeling shifted toward greater interstitial stromal expansion (p = 0.025). Males exhibited larger MMIC radii than females (FC 1.08, p = 0.003). Distinct etiology-specific signatures emerged: anthracycline-treated hearts showed large MMIC radii and perinuclear halos with minimal stroma, while non-ischemic cardiomyopathy exhibited the greatest nuclear enlargement and stromal macrodomain expansion. CONCLUSIONS: Computational pathology enables high-resolution characterization of myocardial remodeling, revealing distinct microstructural phenotypes across sex, coronary disease burden, and HF etiology that may improve cardiac phenotyping and risk stratification.
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Authors Sandeep Manandhar, Yuxin Wu, Tilak Pathak, Kenneth B. Margulies, Anant Madabhushi, Eliot Peyster
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Journal cardiovascular research
Year 2026
DOI
10.1093/cvr/cvag166
URL
https://doi.org/10.1093/cvr/cvag166
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