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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10985477 | PMC |
| http://dx.doi.org/10.1158/1541-7786.MCR-23-0639 | DOI Listing |
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