Publications by authors named "Jennifer K Kerner"
Article Synopsis
- Computational methods have enhanced pathology workflows for diagnostics and genomics but struggle with interpretability for clinical use.
- We developed a method using human-interpretable image features (HIFs) from histopathology images, trained on over 1.6 million annotations from certified pathologists.
- Our approach identifies specific cancer-related characteristics and predicts molecular signatures with similar accuracy to complex 'black-box' models, offering clear insights into tumor microenvironments.
Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics.
View Article and Find Full Text PDFA number of lesions have been documented to arise within congenital melanocytic nevi (CMNs). Although the most frequent malignancy arising within a CMN is melanoma, the association between rhabdomyosarcoma and CMN has rarely been documented. We present a case arising in a 4-month-old girl with a giant CMN.
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