ArticleClinical pathology (Thousand Oaks, Ventura County, Calif.)
Deep Learning for Subtypes Identification of Pure Seminoma of the Testis.
Article in Clinical pathology (Thousand Oaks, Ventura County, Calif.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Single-cell profiling in ovarian germ cell and sex cord-stromal tumours.British journal of cancer · 2025Article
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4 authors at 1 institution in 1 country.
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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 2 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 2 distinct subtypes within pure seminoma but also unveils the presence of morphological differences between them that are imperceptible to the human eye.
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