# Research on the application of distinguishing between benign and malignant breast nodules using MRI and US radiomics

## TL;DR

This study developed a model using MRI and ultrasound radiomics to help distinguish between benign and malignant breast nodules, showing strong predictive performance.

## Contribution

A novel combined model using T2WI radiomic features and clinical data improves accuracy in diagnosing breast nodule malignancy.

## Key findings

- The T2WI radiomic model achieved the highest AUC of 0.950 in training and 0.871 in validation.
- The combined T2WI and clinical features model had the best performance with AUCs of 0.975 and 0.942.
- T2WI-based models outperformed other imaging modalities like DCE, DWI, and US in distinguishing benign from malignant nodules.

## Abstract

This study aims to develop and validate a model based on clinical and radiomic features to investigate its value in distinguishing between benign and malignant breast nodules.

The study included 139 patients with breast diseases, divided into a training set (n=111) and a validation set (n=28) at an 8:2 ratio. All patients’ dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and ultrasound (US) images were uploaded to the 3D Slicer software. Using a double-blind method, regions of interest (ROIs) were manually delineated on T1WI, T2WI, DWI, the first phase of DCE, and US images. Radiomic models were constructed using radiomic features. A comprehensive model was built by combining clinical and radiomic features through multivariate logistic regression and visualized as a nomogram. The area under the curve (AUC), accuracy, specificity, and sensitivity of five different radiomic models were compared to evaluate their discriminatory performance. A combined model was created using the T2WI radiomic model and clinical features, and the predictive performance of the clinical model, radiomic model, and combined model were compared and validated.

For the T1WI radiomic model, the AUC values for the training and test sets were 0.885 and 0.778, respectively. For the T2WI radiomic model, the AUC values were 0.950 and 0.871. For the DCE radiomic model, the AUC values were 0.854 and 0.749. For the DWI radiomic model, the AUC values were 0.878 and 0.763. For the US radiomic model, the AUC values were 0.878 and 0.737. The combined model using T2WI and clinical features achieved AUC values of 0.975 and 0.942 for the training and test sets, respectively.

The model combining T2WI and clinical features demonstrated higher value in non-invasively distinguishing between benign and malignant breast nodules.