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Related Experiment Video

Updated: Jun 13, 2025

Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients
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Explainable artificial intelligence-driven prostate cancer screening using exosomal multi-marker based dual-gate FET

Jae Yi Choi1, Sungwook Park2, Ji Sung Shim3

  • 1Center for Advanced Biomolecular Recognition, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea; Department of Medical Device Engineering and Management, College of Medicine, Yonsei University, Seoul, 06229, Republic of Korea.

Biosensors & Bioelectronics
|September 15, 2024
PubMed
Summary

A novel explainable AI (XAI) system using a biosensor improves prostate cancer (PCa) detection, especially for ambiguous PI-RADS 3 lesions. This non-invasive method offers higher accuracy and interpretable results for clinical decisions.

Keywords:
Cancer screeningDual-gate field-effect-transistor sensorExplainable artificial intelligencePI-RADSProstate cancerUrinary exosome

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Oncology Diagnostics

Background:

  • Prostate cancer (PCa) screening relies on Prostate Imaging Reporting and Data System (PI-RADS) MRI, but PI-RADS 3 lesions have low diagnostic accuracy (30-40%) and high false positives.
  • Ambiguous PI-RADS 3 lesions represent a significant unmet need in accurate PCa diagnosis.
  • Current methods lack interpretability, hindering evidence-based clinical decision-making.

Purpose of the Study:

  • To develop and validate an explainable artificial intelligence (XAI) based PCa screening system.
  • To integrate a highly sensitive dual-gate field-effect transistor (DGFET) multi-marker biosensor for identifying ambiguous lesions.
  • To provide interpretable results for improved PCa diagnosis, particularly for PI-RADS 3 cases.

Main Methods:

  • Development of an XAI system analyzing sensing patterns from three urinary exosomal biomarkers using a DGFET biosensor.
  • Validation of the system using 102 blinded samples.
  • Evaluation of diagnostic accuracy and interpretability of the XAI system's predictions.

Main Results:

  • The XAI-based PCa screening system achieved a high accuracy with an Area Under the Curve (AUC) of 0.93.
  • PCa diagnosis accuracy for PI-RADS 3 patients was more than double that of conventional PI-RADS scoring.
  • The system identified TMEM256 as a leading biomarker for screening PI-RADS 3 cases, providing an interpretable decision basis.

Conclusions:

  • The XAI-based biosensor system significantly enhances PCa screening accuracy, especially for PI-RADS 3 lesions.
  • The system's interpretability facilitates evidence-based clinical decisions by highlighting key biomarker significance.
  • This non-invasive approach offers a practical tool to assist healthcare professionals in accurate PCa diagnosis.