Episode 76: Deep Learning for Predicting Prostate Cancer Molecular Subtype on H&E Images

In this episode, Dr. Tamara Lotan from Johns Hopkins University discusses the potential of deep-learning (DL) algorithms trained on H&E-stained whole slide images (WSI) to screen for clinically relevant genomic alterations in prostate cancer (PCA).
Dr. Lotan reviews her team’s recent publication in Modern Pathology, where they were able to create DL algorithms to identify PCA with underlying ERG fusions or PTEN deletions. By applying the algorithms to multiple radical prostatectomy and needle biopsy cohorts, the authors demonstrated the ability of DL models to accurately predict ERG/PTEN status from H&E stained WSI.
doi.org/10.1016/j.modpat.2023...