Clear cell sarcoma overlaps histologically and immunophenotypically with melanoma. Molecular testing for EWSR1 rearrangement aids in their distinction but may not be readily available. We aim to construct machine learning-based classifiers using supervised and deep learning. We digitized hematoxylin-and-eosin-stained slides from clear cell sarcomas and melanomas with confirmatory testing, constructed nuclear morphometric-based and deep learning-based classifiers (using CLAM/ResNet-50, CTransPath, and UNI models), and evaluated their performance using an independent external validation set. Morphometric analysis of 1,954,194 nuclei (1,308,124 from clear cell sarcomas, 646,070 from melanomas; 8700-70,726 median 32,472 nuclei per slide) yielded two optimal classifiers using single-node decision tree models; both pertained to nuclear perimeter and included interquartile/interdecile range normalized to median. Factors associated with inaccurate predictions included < 10,000 nuclei/sample and altered morphology in clear cell sarcoma due to therapy. In the external validation set, comparable to the prediction accuracies by four pathologists (median 80%; range 60%-100%), the two nuclear morphometric-based classifiers achieved accuracies of 80%-90%, and the optimal deep learning-based classifier CLAM/CTransPath showed an accuracy of 90%. In conclusion, we have derived interpretable nuclear morphometric- and deep learning-based classifiers to distinguish clear cell sarcoma from melanoma. Quantitative morphometric analysis with machine learning holds the potential as a diagnostic adjunct.
Moran et al. (Sun,) studied this question.