Ultrasound-based Detection and Malignancy Prediction of Breast Lesions Eligible for Biopsy: A Multi-center Clinical-scenario Study Using Nomograms, Large Language Models, and Radiologist Evaluation.

RATIONALE AND

OBJECTIVES

To develop and externally validate ultrasound nomograms combining BI-RADS features and quantitative morphometric characteristics, and to compare their performance with expert radiologists and large language models in biopsy recommendation and malignancy prediction for breast lesions.

METHODS

In this multi-center, multi-national study, 1747 women with breast lesions underwent ultrasound across three centers in Iran and Turkey. A total of 10 BIRADS and 26 morphological features were extracted from each lesion. Three nomograms based on BI-RADS, morphometric, and both feature sets were constructed. Three radiologists (one senior, two general) and two ChatGPTs including ChatGPT-o3 and o4-mini-high interpreted de-identified breast lesion images. Diagnostic performance for biopsy recommendation and malignancy prediction was assessed across all cohorts.

RESULTS

According to the pooled results, although the difference between the fused nomogram and the BI-RADS version was not statistically significant, the fused version consistently outperformed all models in biopsy recommendation and malignancy prediction (AUCs of 0.901 and 0.853, respectively) compared to BI-RADS nomogram (AUCs of 0.898 and 0.834), morphometric nomogram (AUCs of 0.825 and 0.708), radiologist1 (AUCs of 0.820 and 0.729), radiologist2 (AUCs of 0.605 and 0.719), radiologist3 (AUCs of 0.728 and 0.699), ChatGPT-o3 (AUCs of 0.729 and 0.689), and o4-mini-high (AUCs of 0.713 and 0.695).

CONCLUSIONS

The proposed BI-RADS-morphometric nomogram outperforms standalone nomogram models, LLMs, and radiologists in guiding biopsy decisions and predicting malignancy. The proposed novel fused nomogram has the potential to reduce unnecessary biopsies and enhance personalized decision-making in breast imaging.