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Updated: Apr 30, 2026

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A Stage IA and IB Endometrial Cancer Classification Method Using Genetic Programming on MRI Images.

Chunxia Chen1, Yuequan Shi1, Wenting Cao1

  • 1Department of Radiology, Fujian Maternity and Child Health Hospital & Fujian Key Laboratory of Women and Children's Critical Diseases Research, 350001 Fuzhou, Fujian, China.

Frontiers in Bioscience (Landmark Edition)
|April 29, 2026
PubMed
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This summary is machine-generated.

Genetic programming (GP) effectively classifies early-stage endometrial cancer (EC) using limited MRI data. This approach automatically extracts key features for accurate FIGO stage IA/IB differentiation, aiding clinical decisions.

Area of Science:

  • Medical imaging analysis
  • Machine learning in oncology
  • Computational intelligence

Background:

  • Accurate staging of endometrial cancer (EC) is vital for treatment.
  • Differentiating FIGO stage IA from IB EC is challenging with current MRI due to morphological variations and limited annotated data.

Purpose of the Study:

  • To develop and evaluate a genetic programming (GP)-based framework for classifying FIGO stage IA and IB EC.
  • To address the challenge of limited annotated MRI data for robust model training.

Main Methods:

  • A GP-based framework was developed, including automatic region of interest (ROI) detection using a fast single-shot detector (SSD).
  • Four GP variants (COGP, IDGP, FlexGP, FELGP) were used for feature extraction and construction from MRI ROIs.
  • Classifiers were trained using discriminative features generated by the best-performing GP individuals.
Keywords:
endometrial neoplasmsgenetic programmingimage analysismagnetic resonance imaging

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Main Results:

  • GP-based methods demonstrated competitive performance against neural and traditional machine learning approaches.
  • Classification accuracies reached up to 0.92 on diffusion-weighted imaging (DWI) and 0.87 on T2-weighted imaging (T2WI).
  • The framework achieved high accuracy on cropped axial and sagittal MRI views of EC patients.

Conclusions:

  • GP-based methods offer an effective solution for classifying FIGO stage IA and IB EC using limited MRI data.
  • The framework automatically extracts discriminative and interpretable features, enhancing transparency in EC staging.
  • This highlights the potential of GP in medical image analysis and clinical decision support for gynecologic cancers.