Abstract
The most critical step in the clinical diagnosis workflow is the pathological evaluation of each tumor sample. Deep learning is a powerful approach that is widely used to enhance diagnostic accuracy and streamline the diagnosis process. In our previous study using omics data, we identified two distinct subtypes of pure seminoma. Seminoma is the most common histological type of testicular germ cell tumors (TGCTs). Here we developed a deep learning decision making tool for the identification of seminoma subtypes using histopathological slides. We used all available slides for pure seminoma samples from The Cancer Genome Atlas (TCGA). The developed model showed an area under the ROC curve of 0.896. Our model not only confirms the presence of two distinct subtypes within pure seminoma but also unveils the presence of morphological differences between them that are imperceptible to the human eye.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
The study is supported by the grants from the National Institute of General Medical Sciences of the National Institutes of Health GM127390 (to N.V.G.), the Welch Foundation I-1505 (to N.V.G.), and the National Science Foundation DBI 2224128 (to N.V.G.).
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We used histopathological slides of seminoma samples available at The Cancer Genome Atlas (TCGA): https://portal.gdc.cancer.gov/
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Footnotes
Results section updated with nuclei segmentation. Added Figure 5
Data Availability
All data produced are available online at https://github.com/kirmedvedev/seminoma-subtypes