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April 5, 2026Cancer Research

AI-Enhanced Non-Invasive Urinary Metabolite Analysis for Early Cancer Detection

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Authors

JKJee-Hee KimYong In UniversityHCHyungseok ChoiYong In UniversityEKEun Hye KohYong In University

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Implication

Retrospective clinical study demonstrates effective cancer detection in urine samples through AI-SERS technology, indicating significant diagnostic potential.

Key Points

  • The study aims to improve early cancer detection through non-invasive analysis of urinary metabolites using AI-SERS technology.
  • Enrolled 287 urine samples from patients across five cancer types and normal controls.
  • Utilized a patented SERS sensor for minimally invasive sample analysis.
  • Applied a convolutional neural network (CNN) to analyze the SERS spectra for classification.
  • Achieved 96.6% accuracy, 99.3% sensitivity, and 94.0% specificity in distinguishing all cancer types from normal controls.
  • Individual cancer types showed high predictive performance with specific accuracy and sensitivity: 98.0% accuracy for prostate cancer, 97.9% for ovarian cancer, 97.8% for lung cancer, and 98.9% for breast cancer.
  • Demonstrated strong potential for early cancer diagnosis through non-invasive methods.

Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/69d1fd3da79560c99a0a3153https://doi.org/10.1158/1538-7445.am2026-2532
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Also Consider

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  1. 1Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques2024 · 23 citations
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  4. 4Whole urine-based multiple cancer diagnosis and metabolite profiling using 3D evolutionary gold nanoarchitecture combined with machine learning-assisted SERS2024 · 28 citations
  5. 5Abstract 7620: PanGIA Analysis System, a novel machine learning platform for non-invasive diagnosis of multiple cancers through urine2026