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CT radiomics-based machine learning model for differentiating between enchondroma and low-grade chondrosarcoma.

Mustafa Yildirim1, Hanefi Yildirim

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Machine learning models using 3D computed tomography (CT) radiomics effectively differentiate enchondroma from low-grade chondrosarcoma. This advanced analysis aids in distinguishing benign cartilage tumors from malignant ones, improving diagnostic accuracy.

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Area of Science:

  • Medical imaging
  • Radiology
  • Machine learning in oncology

Background:

  • Distinguishing enchondroma from low-grade chondrosarcoma radiologically is challenging.
  • Accurate differentiation is crucial for appropriate patient management and treatment.
  • Current imaging modalities may have limitations in definitively differentiating these conditions.

Purpose of the Study:

  • To develop and evaluate machine learning models for differentiating low-grade chondrosarcoma from enchondroma.
  • To utilize 3D computed tomography (CT)-based radiomics analysis for this diagnostic task.
  • To assess the performance of various machine learning algorithms in this classification.

Main Methods:

  • Retrospective study including 30 enchondroma and 26 chondrosarcoma cases.
  • Manual tumor segmentation by two musculoskeletal radiologists.
  • Extraction of 107 radiomic features from 3D CT scans.
  • Feature reduction using information gain, identifying the top 5 features.
  • Classification using seven machine learning models with all features and the top 5 features.

Main Results:

  • Good to excellent interobserver agreement was achieved for radiomic feature extraction.
  • The Naive Bayes model achieved an Area Under the Curve (AUC) of 0.950 using all features.
  • The Random Forest model achieved the highest AUC of 0.967 using the top 5 features.
  • Both approaches demonstrated high classification performance.

Conclusions:

  • Machine learning models incorporating CT-based radiomics show significant potential for differentiating low-grade chondrosarcoma from enchondroma.
  • Radiomics analysis can enhance diagnostic accuracy in distinguishing benign from low-grade malignant cartilage tumors.
  • This approach may aid clinicians in making more informed treatment decisions.