# Enhanced HER-2 prediction in breast cancer through synergistic integration of deep learning, ultrasound radiomics, and clinical data

## TL;DR

This study improves HER-2 prediction in breast cancer by combining deep learning, ultrasound imaging features, and clinical data for better treatment strategies.

## Contribution

The novel contribution is the synergistic integration of deep learning, ultrasound radiomics, and clinical data to enhance HER-2 status prediction.

## Key findings

- The combined model integrating clinical, radiomics, and deep learning features achieved the highest AUC values for HER-2 prediction.
- Deep learning models like ResNet101 and LightGBM showed strong performance, but the combined model outperformed them.
- The study demonstrates that fusing diverse data sources significantly improves diagnostic accuracy for HER-2 status in breast cancer.

## Abstract

This study integrates ultrasound Radiomics with clinical data to enhance the diagnostic accuracy of HER-2 expression status in breast cancer, aiming to provide more reliable treatment strategies for this aggressive disease. We included ultrasound images and clinicopathologic data from 210 female breast cancer patients, employing a Generative Adversarial Network (GAN) to enhance image clarity and segment the region of interest (ROI) for Radiomics feature extraction. Features were optimized through Z-score normalization and various statistical methods. We constructed and compared multiple machine learning models, including Linear Regression, Random Forest, and XGBoost, with deep learning models such as CNNs (ResNet101, VGG19) and Transformer technology. The Grad-CAM technique was used to visualize the decision-making process of the deep learning models. The Deep Learning Radiomics (DLR) model integrated Radiomics features with deep learning features, and a combined model further integrated clinical features to predict HER-2 status. The LightGBM and ResNet101 models showed high performance, but the combined model achieved the highest AUC values in both training and testing, demonstrating the effectiveness of integrating diverse data sources. The study successfully demonstrates that the fusion of deep learning with Radiomics analysis significantly improves the prediction accuracy of HER-2 status, offering a new strategy for personalized breast cancer treatment and prognostic assessments.

The online version contains supplementary material available at 10.1038/s41598-025-12825-7.