What question did this study set out to answer?

The aim is to create and validate radiomics models that predict breast cancer molecular subtypes using ultrasound and DCE MRI.

The radiomics models based on ultrasound and DCE MRI for predicting molecular subtypes of breast cancer

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This analysis predicts molecular subtypes of breast cancer using radiomics models, suggesting a new non-invasive method for treatment decisions.

The aim is to create and validate radiomics models that predict breast cancer molecular subtypes using ultrasound and DCE MRI.
Collected data from 655 breast cancer patients from two centers.
Divided 551 patients from one center into training and internal testing sets.
Employed eight machine learning algorithms to develop ultrasound and MRI radiomics models.
Tested models for performance in both internal and external validation sets.
The combined model (R MRI+US) achieved the highest predictive performance with AUC of 0.973 in the training set.
In the internal testing set, the AUC was 0.872, outperforming single-modality models.
The external validation set showed an AUC of 0.835 for R MRI+US, indicating strong predictive capability.

Mao et al. (2026) studied this question.

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This analysis predicts molecular subtypes of breast cancer using radiomics models, suggesting a new non-invasive method for treatment decisions.