This study aims to develop a new computational pathology approach that
automates the identification and quantification of myocardial inflammatory
infiltration in digital HE-stained images to provide a quantitative
histological diagnosis of myocarditis.898 HE-stained whole slide images (WSIs)
of myocardium from 154 heart transplant patients diagnosed with myocarditis or
dilated cardiomyopathy (DCM) were included in this study. An automated DL-based
computational pathology approach was developed to identify nuclei and detect
myocardial inflammatory infiltration, enabling the quantification of the
lymphocyte nuclear density (LND) on myocardial WSIs. A cutoff value based on
the quantification of LND was proposed to determine if the myocardial
inflammatory infiltration was present. The performance of our approach was
evaluated with a five-fold cross-validation experiment, tested with an internal
test set from the myocarditis group, and confirmed by an external test from a
double-blind trial group. An LND of 1.02/mm2 could distinguish WSIs with
myocarditis from those without. The accuracy, sensitivity, specificity, and
area under the receiver operating characteristic curve (AUC) in the five-fold
cross-validation experiment were 0.899 plus or minus 0.035, 0.971 plus or minus
0.017, 0.728 plus or minus 0.073 and 0.849 plus or minus 0.044, respectively.
For the internal test set, the accuracy, sensitivity, specificity, and AUC were
0.887, 0.971, 0.737, and 0.854, respectively. The accuracy, sensitivity,
specificity, and AUC for the external test set reached 0.853, 0.846, 0.858, and
0.852, respectively. Our new approach provides accurate and reliable
quantification of the LND of myocardial WSIs, facilitating automated
quantitative diagnosis of myocarditis with HE-stained images.Comment: 21 pages,5 figures,6 Tables, 25 reference