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Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT

Soyoung Kim1, Jae-Hoon Lee2, Eun Jung Park3

  • 1Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.

Yonsei Medical Journal
|April 28, 2023
PubMed
Summary

Preoperative 18F-fluorodeoxyglucose (FDG) PET/CT radiomics effectively predicts microsatellite instability (MSI) status in colorectal cancer (CRC) patients. This machine learning approach demonstrated superior performance compared to conventional PET parameters.

Keywords:
Colorectal cancerimage analysismachine learningmicrosatellite instabilitypositron emission tomography

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging
  • Machine Learning

Background:

  • Microsatellite instability (MSI) is a crucial biomarker in colorectal cancer (CRC) prognosis and treatment selection.
  • Accurate preoperative prediction of MSI status in CRC is essential for personalized therapeutic strategies.
  • 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) offers valuable metabolic information in oncology.

Purpose of the Study:

  • To evaluate the feasibility of using preoperative FDG PET/CT radiomics combined with machine learning to predict MSI status in CRC patients.
  • To develop and validate a radiomics signature for MSI status prediction.
  • To compare the predictive performance of the radiomics signature against conventional PET parameters.

Main Methods:

  • A cohort of 233 CRC patients undergoing preoperative FDG PET/CT was retrospectively analyzed.
  • Patients were divided into training (n=139) and testing (n=94) sets.
  • A PET-based radiomics signature (rad_score) was developed using machine learning and validated by area under the receiver operating characteristic curve (AUROC) and logistic regression.

Main Results:

  • The radiomics signature (rad_score) achieved high AUROC values for MSI status prediction in both training (0.815) and testing (0.867) sets.
  • The rad_score was identified as an independent predictor of MSI status in CRC patients.
  • The rad_score demonstrated superior predictive performance compared to metabolic tumor volume (AUROC 0.867 vs. 0.794, p=0.015).

Conclusions:

  • Preoperative FDG PET/CT radiomics, integrated with machine learning, provides a robust method for predicting MSI status in colorectal cancer.
  • The developed radiomics model offers improved predictive accuracy over conventional PET imaging parameters.
  • This approach holds potential for non-invasively guiding treatment decisions in CRC management.