Semi-Supervised, Attention-Based Deep Learning for Predicting TMPRSS2:ERG Fusion Status in Prostate Cancer Using Whole Slide Images.

Mohamed N Omar Zhuoran Xu Sophie B Rand Mohammad K Alexanderani Daniela C Salles Itzel Valencia Edward M Schaeffer Brian D Robinson Tamara L Lotan Massimo Loda Luigi Marchionni

Published in: Molecular cancer research : MCR (2024)

Our study illuminates the potential of deep learning in effectively inferring key prostate cancer genetic alterations from the tissue morphology depicted in routinely available histology slides, offering a cost-effective method that could revolutionize diagnostic strategies in oncology.

Keyphrases

deep learning
prostate cancer
radical prostatectomy
machine learning
convolutional neural network
artificial intelligence
working memory
genome wide
dna methylation
gene expression
risk assessment
optical coherence tomography
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