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Related Experiment Video

Updated: May 28, 2026

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Prioritising Data Quality Governance for AI in Prostate Cancer: A Methodological Proof-of-Concept Study Using Neural

Vanessa Talavera-Cobo1, Jose Enrique Robles-Garcia1, Francisco Guillen-Grima2,3,4,5

  • 1Department of Urology, Clinica Universidad de Navarra, 31008 Pamplona, Spain.

Diagnostics (Basel, Switzerland)
|May 27, 2026
PubMed
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This summary is machine-generated.

This study introduces the DQG-AI framework for creating reliable artificial neural networks (ANNs) for prostate cancer risk stratification, even with small datasets. The framework, not the model

Area of Science:

  • Urology and Artificial Intelligence
  • Prostate Cancer Management
  • Data Quality Governance for AI

Background:

  • Accurate D'Amico risk stratification is crucial for prostate cancer (PCa) management.
  • Integrating clinical nomograms with AI requires robust data governance.
  • Small sample sizes pose challenges for developing reliable AI models.

Purpose of the Study:

  • To establish a methodological framework for generating reliable artificial neural networks (ANNs) for PCa risk stratification.
  • To integrate validated clinical nomograms with strict data-quality governance.
  • To demonstrate the feasibility of creating AI-ready datasets even with limited patient cohorts.

Main Methods:

  • Retrospective analysis of a curated cohort of 49 PCa patients.
Keywords:
AI-readinessBriganti nomogramD’Amico risk stratificationFAIR principlesISUP gradeartificial neural networkdata quality governancemultilayer perceptronproof-of-conceptprostate cancerreproducibility

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Last Updated: May 28, 2026

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Published on: June 10, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Published on: July 11, 2025

  • Development of a multilayer perceptron (MLP) using 11 variables, including ISUP grade and Briganti nomogram.
  • Application of the DQG-AI framework (data quality governance for AI-readiness) with FAIR principles for data curation.
  • Sensitivity analysis across three data partitioning scenarios (20/80, 34/66, 39/61).

    • Main Results:

    • The DQG-AI framework successfully curated a high-quality dataset of 49 patients from an initial pool of 76.
    • The MLP achieved mathematically perfect discrimination (AUC 1.000) on a small internal test set (N=9) in the 20/80 configuration.
    • This perfect accuracy is interpreted as a methodological artifact of the small, curated dataset and validation constraints, not generalizable clinical utility.

    Conclusions:

    • The DQG-AI framework is a strict, repeatable approach for producing AI-ready urological datasets.
    • The MLP showed a robust internal signal for risk discrimination, but its flawless accuracy is non-generalizable.
    • The primary deliverable requiring external validation is the DQG-AI framework itself, not the model's performance metrics.
    • A multi-institutional validation roadmap is proposed to operationalize external validation.