Explainable artificial intelligence-driven prostate cancer screening using exosomal multi-marker based dual-gate FET biosensor

• Published in: Biosensors & bioelectronics 2025-01, Vol.267, p.116773, Article 116773
• Main Authors: Choi, Jae Yi, Park, Sungwook, Shim, Ji Sung, Park, Hyung Joon, Kuh, Sung Uk, Jeong, Youngdo, Park, Min Gu, Noh, Tae Il, Yoon, Sung Goo, Park, Yoo Min, Lee, Seok Jae, Kim, Hojun, Kang, Seok Ho, Lee, Kwan Hyi
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Biomarkers, Tumor - urine; Biosensing Techniques - instrumentation; Biosensing Techniques - methods; Cancer screening; Dual-gate field-effect-transistor sensor; Early Detection of Cancer - methods; Exosomes - chemistry; Explainable artificial intelligence; Humans; Male; Membrane Proteins; PI-RADS; Prostate cancer; Prostate-Specific Antigen - blood; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - urine; Transistors, Electronic; Urinary exosome
• ISSN: 0956-5663; 1873-4235; 1873-4235
• DOI: 10.1016/j.bios.2024.116773

Prostate Imaging Reporting and Data System (PI-RADS) score, a reporting system of prostate MRI cases, has become a standard prostate cancer (PCa) screening method due to exceptional diagnosis performance. However, PI-RADS 3 lesions are an unmet medical need because PI-RADS provides diagnosis accuracy of only 30–40% at most, accompanied by a high false-positive rate. Here, we propose an explainable artificial intelligence (XAI) based PCa screening system integrating a highly sensitive dual-gate field-effect transistor (DGFET) based multi-marker biosensor for ambiguous lesions identification. This system produces interpretable results by analyzing sensing patterns of three urinary exosomal biomarkers, providing a possibility of an evidence-based prediction from clinicians. In our results, XAI-based PCa screening system showed a high accuracy with an AUC of 0.93 using 102 blinded samples with the non-invasive method. Remarkably, the PCa diagnosis accuracy of patients with PI-RADS 3 was more than twice that of conventional PI-RADS scoring. Our system also provided a reasonable explanation of its decision that TMEM256 biomarker is the leading factor for screening those with PI-RADS 3. Our study implies that XAI can facilitate informed decisions, guided by insights into the significance of visualized multi-biomarkers and clinical factors. The XAI-based sensor system can assist healthcare professionals in providing practical and evidence-based PCa diagnoses.