Objective:

To develop and validate an mpMRI-based nomogram incorporating clinicopathological factors for predicting HER2 status in breast cancer patients.

Approach:

Key Findings:

• CA125, Ki-67, ADC-min, and early-phase ME significantly differed among HER2 subgroups.
• The nomogram achieved AUCs of 0.762 and 0.738 for differentiating HER2-over/HER2-low from HER2-zero in training and validation datasets, respectively.
• AUCs for differentiating HER2-over from HER2-low subtypes were 0.719 and 0.772.

Interpretation:

The nomogram effectively predicts HER2 expression in breast cancer patients, providing a noninvasive tool for guiding targeted therapy selection.

Limitations:

• The study is retrospective and may be subject to biases inherent in such designs.
• The sample size for the validation dataset was relatively small.

Conclusion:

The mpMRI-based nomogram is a promising tool for predicting HER2 status in breast cancer, aiding in targeted therapy decisions.

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