Radiomics meets transformers: A novel approach to tumor segmentation and classification in mammography for breast cancer.

Journal: Journal of X-ray science and technology
Published Date: Jul 29, 2025

Abstract

ObjectiveThis study aimed to develop a robust framework for breast cancer diagnosis by integrating advanced segmentation and classification approaches. Transformer-based and U-Net segmentation models were combined with radiomic feature extraction and machine learning classifiers to improve segmentation precision and classification accuracy in mammographic images.Materials and MethodsA multi-center dataset of 8000 mammograms (4200 normal, 3800 abnormal) was used. Segmentation was performed using Transformer-based and U-Net models, evaluated through Dice Coefficient (DSC), Intersection over Union (IoU), Hausdorff Distance (HD95), and Pixel-Wise Accuracy. Radiomic features were extracted from segmented masks, with Recursive Feature Elimination (RFE) and Analysis of Variance (ANOVA) employed to select significant features. Classifiers including Logistic Regression, XGBoost, CatBoost, and a Stacking Ensemble model were applied to classify tumors into benign or malignant. Classification performance was assessed using accuracy, sensitivity, F1 score, and AUC-ROC. SHAP analysis validated feature importance, and Q-value heatmaps evaluated statistical significance.ResultsThe Transformer-based model achieved superior segmentation results with DSC (0.94 ± 0.01 training, 0.92 ± 0.02 test), IoU (0.91 ± 0.01 training, 0.89 ± 0.02 test), HD95 (3.0 ± 0.3 mm training, 3.3 ± 0.4 mm test), and Pixel-Wise Accuracy (0.96 ± 0.01 training, 0.94 ± 0.02 test), consistently outperforming U-Net across all metrics. For classification, Transformer-segmented features with the Stacking Ensemble achieved the highest test results: 93% accuracy, 92% sensitivity, 93% F1 score, and 95% AUC. U-Net-segmented features achieved lower metrics, with the best test accuracy at 84%. SHAP analysis confirmed the importance of features like Gray-Level Non-Uniformity and Zone Entropy.ConclusionThis study demonstrates the superiority of Transformer-based segmentation integrated with radiomic feature selection and robust classification models. The framework provides a precise and interpretable solution for breast cancer diagnosis, with potential for scalability to 3D imaging and multimodal datasets.

Authors

  • Mohamed J Saadh
    Faculty of Pharmacy, Middle East University, Amman, 11831, Jordan.
  • Qusay Mohammed Hussain
    College of Pharmacy, Alnoor University, Mosul, Iraq.
  • Rafid Jihad Albadr
    Ahl Al Bayt University, Kerbala, Iraq.
  • Hardik Doshi
    Marwadi University Research Center, Department of Computer Engineering, Faculty of Engineering & Technology Marwadi University, Rajkot, Gujarat, 360003, India.
  • M M Rekha
    Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to Be University), Bangalore, Karnataka, India.
  • Mayank Kundlas
    Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India.
  • Amrita Pal
    Department of Chemistry, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.
  • Jasur Rizaev
    Department of Public Health and Healthcare Management, Rector, Samarkand State Medical University, 18, Amir Temur Street, Samarkand, Uzbekistan.
  • Waam Mohammed Taher
    College of Nursing, National University of Science and Technology, Dhi Qar, Iraq.
  • Mariem Alwan
    Pharmacy College, Al-Farahidi University, Baghdad, Iraq.
  • Mahmod Jasem Jawad
    Department of Pharmacy, Al-Zahrawi University College, Karbala, Iraq.
  • Ali M Ali Al-Nuaimi
    Gilgamesh Ahliya University, Baghdad, Iraq.
  • Bagher Farhood
    Department of Medical Physics and Radiology, Faculty of Paramedical Sciences, Kashan University of Medical Sciences, Kashan, Iran. bffarhood@gmail.com.

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