Deep Learning for Subtypes Identification of Pure Seminoma of the Testis

Kirill E Medvedev; Paul H Acosta; Liwei Jia; Nick V Grishin

doi:10.1177/2632010X241232302

Deep Learning for Subtypes Identification of Pure Seminoma of the Testis

Clin Pathol. 2024 Feb 18:17:2632010X241232302. doi: 10.1177/2632010X241232302. eCollection 2024 Jan-Dec.

Authors

Kirill E Medvedev¹, Paul H Acosta², Liwei Jia³, Nick V Grishin^{1

4}

Affiliations

¹ Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
² Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
³ Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
⁴ Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX, USA.

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.

Keywords: Bioinformatics; computational biology; deep learning; seminoma; subtypes.