Pathological evaluation of each tumor sample is the most crucial process in the clinical diagnosis workflow. Deep learning is a powerful approach that is widely used to increase accuracy and to simplify the diagnosis process. Previously we discovered clinically relevant subtypes (1 and 2) of pure seminoma, which is the most common histological type of testicular germ cell tumors (TGCTs). Here we developed deep learning decision making tool for identification of seminoma subtypes using histopathological slides. We used all available slides for pure seminoma samples from The Cancer Genome Atlas (TCGA). Seminoma regions of interest (ROIs) were annotated by a genitourinary pathologist. Verified ROIs were split into tiles 300x300 pixels, which were used for model training. The model achieved the highest accuracy at the validation step with 0.933 with 0.92 area under the ROC curve.

The authors have declared no competing interest.

This study was funded by the grants (to N.V.G.) from the National Institutes of Health GM127390 and the Welch Foundation I-1505

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

We used histopathological slides of seminoma samples available at The Cancer Genome Atlas (TCGA): https://portal.gdc.cancer.gov/

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes