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A novel classification of testicular sex cord-stromal tumours from the Testicular Sex Cord-Stromal Tumour (TESST) group: a collaboration of the Genitourinary Pathology Society (GUPS) and the International Society of Urological Pathology (ISUP).

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A Unified Machine Learning Model for Relapse Prediction in Clinical Stage I Testicular Cancer.

Thomas Wagner1, Ramtin Zargari Marandi2, Jakob Lauritsen3

  • 1Department of Pathology, Copenhagen University Hospital, Herlev and Gentofte Hospital, Copenhagen, Denmark.

Andrology
|February 19, 2026
PubMed
Summary

A new machine learning model accurately predicts relapse in stage I testicular cancer, regardless of subtype. This tool is valuable for ruling out relapse, especially in non-seminoma cases.

Keywords:
clinical stage I diseaseinterpretable machine learningprognostic factorsrelapserisk predictiontesticular cancer

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Area of Science:

  • Oncology
  • Machine Learning
  • Biostatistics

Background:

  • Approximately 25% of clinical stage I testicular cancer patients experience relapse.
  • Traditional risk stratification uses limited variables and separate models for seminomas and non-seminomas.
  • Machine learning offers potential for novel risk factor discovery by analyzing large datasets.

Purpose of the Study:

  • To develop and validate a unified machine learning model for predicting relapse in clinical stage I testicular cancer.
  • To create a model applicable across both seminoma and non-seminoma subtypes.
  • To leverage nationwide histopathological and clinical data for enhanced prediction.

Main Methods:

  • A population-based cohort study of 1377 patients with clinical stage I testicular cancer (2013-2018).
  • Utilized tree-based classifiers (CatBoost, LightGBM) and a random survival forest model.
  • Data split into training (80%) and testing (20%) sets, balanced for subtype and outcome.

Main Results:

  • The CatBoost model achieved an AUC of 0.74 and a negative predictive value of 0.86.
  • Random survival forest showed a concordance index of 0.71.
  • Key predictors included lymphovascular invasion, tumor necrosis, and elevated biomarkers; tumor necrosis and LVI location were novel predictors.

Conclusions:

  • A unified machine learning model for testicular cancer relapse prediction is feasible with moderate accuracy.
  • The model is effective for ruling out relapse, particularly in non-seminoma.
  • Findings suggest a framework for validation and highlight important predictive features for future research.