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Objective <p>To develop a multi-modality machine learning-based radiomics model utilizing Magnetic Resonance Imaging (MRI), Ultrasound (US), and Mammography (MMG) for the differentiation of benign and malignant breast nodules.</p> Methods <p>This study retrospectively collected data from 204 patients across three hospitals, including MRI, US, and MMG imaging data along with confirmed pathological diagnoses. Lesions on 2D US, 2D MMG, and 3D MRI images were selected to outline the areas of interest, which were then automatically expanded outward by 3&#xa0;mm, 5&#xa0;mm, and 8&#xa0;mm to extract radiomic features within and around the tumor. ANOVA, the maximum correlation minimum redundancy (mRMR) algorithm, and the least absolute shrinkage and selection operator (LASSO) were used to select features for breast cancer diagnosis through logistic regression analysis. The performance of the radiomics models was evaluated using receiver operating characteristic (ROC) curve analysis, curves decision curve analysis (DCA), and calibration curves.</p> Results <p>Among the various radiomics models tested, the MRI_US_MMG multi-modality logistic regression model with 5&#xa0;mm peritumoral features demonstrated the best performance. In the test cohort, this model achieved an AUC of 0.905(95% confidence interval [CI]: 0.805–1). These results suggest that the inclusion of peritumoral features, specifically at a 5&#xa0;mm expansion, significantly enhanced the diagnostic efficiency of the multi-modality radiomics model in differentiating benign from malignant breast nodules.</p> Conclusions <p>The multi-modality radiomics model based on MRI, ultrasound, and mammography can predict benign and malignant breast lesions.</p>

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Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月4日

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  • Jiao Wu,
  • YongXin Li,
  • Wanqing Gong,
  • Qian Li,
  • Xue Han,
  • Tingting Zhang

摘要

Objective

To develop a multi-modality machine learning-based radiomics model utilizing Magnetic Resonance Imaging (MRI), Ultrasound (US), and Mammography (MMG) for the differentiation of benign and malignant breast nodules.

Methods

This study retrospectively collected data from 204 patients across three hospitals, including MRI, US, and MMG imaging data along with confirmed pathological diagnoses. Lesions on 2D US, 2D MMG, and 3D MRI images were selected to outline the areas of interest, which were then automatically expanded outward by 3 mm, 5 mm, and 8 mm to extract radiomic features within and around the tumor. ANOVA, the maximum correlation minimum redundancy (mRMR) algorithm, and the least absolute shrinkage and selection operator (LASSO) were used to select features for breast cancer diagnosis through logistic regression analysis. The performance of the radiomics models was evaluated using receiver operating characteristic (ROC) curve analysis, curves decision curve analysis (DCA), and calibration curves.

Results

Among the various radiomics models tested, the MRI_US_MMG multi-modality logistic regression model with 5 mm peritumoral features demonstrated the best performance. In the test cohort, this model achieved an AUC of 0.905(95% confidence interval [CI]: 0.805–1). These results suggest that the inclusion of peritumoral features, specifically at a 5 mm expansion, significantly enhanced the diagnostic efficiency of the multi-modality radiomics model in differentiating benign from malignant breast nodules.

Conclusions

The multi-modality radiomics model based on MRI, ultrasound, and mammography can predict benign and malignant breast lesions.