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Machine Learning-Enabled Non-Invasive Screening of Tumor-Associated Circulating Transcripts for Early Detection of Colorectal Cancer

  • 0Department of Biomedical Laboratory Science, College of Software and Digital Healthcare Convergence, Yonsei University Mirae Campus, Wonju 26493, Republic of Korea.
International Journal of Molecular Sciences +

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February 26, 2025

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February 26, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study shows tumor-associated circulating transcripts (TACTs) combined with artificial intelligence (AI) models offer a promising non-invasive method for colorectal cancer (CRC) detection, improving early diagnosis and patient outcomes.

Area Of Science

  • Oncology
  • Biomarker Discovery
  • Computational Biology

Background

  • Colorectal cancer (CRC) remains a leading cause of cancer mortality globally.
  • Accurate, non-invasive diagnostic methods are crucial for early CRC detection and improved patient survival.
  • Tumor-associated circulating transcripts (TACTs) are emerging as potential biomarkers for cancer detection.

Purpose Of The Study

  • To evaluate the diagnostic utility of TACTs for colorectal cancer (CRC) detection.
  • To integrate TACT markers into machine learning (ML) models to enhance diagnostic accuracy.
  • To identify the optimal ML model for CRC detection using TACT biomarkers.

Main Methods

  • The study assessed five machine learning models: Generalized Linear Model, Random Forest, Gradient Boosting Machine, Deep Neural Network (DNN), and AutoML.
  • TACTs were utilized as biomarkers within these models for CRC detection.
  • Model performance was evaluated based on sensitivity and specificity.

Main Results

  • The Deep Neural Network (DNN) model demonstrated optimal performance.
  • The DNN model achieved a sensitivity of 85.7% and a specificity of 90.9% for CRC detection.
  • High diagnostic performance was particularly noted for early-stage colorectal cancer.

Conclusions

  • Combining TACT markers with AI-based analysis presents a scalable and precise approach for non-invasive CRC screening.
  • This integrated approach significantly advances non-invasive cancer diagnostics.
  • The findings support the potential of TACTs and AI for improving early CRC detection and patient outcomes.

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