Radiomics Model Based on DCE-MRI and Ultrasound Images for Breast Lesion Classification

Multi-modality Radiomics Diagnostic Model Based on DCE-MRI and Ultrasound Images for Benign and Malignant Breast Lesion Classification

To develop and compare multi-modality radiomics models based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions.

In this retrospective study,555 breast lesions from 555 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n =388) and testing (n = 167) datasets. Radiomics features were extracted from manually contoured images. The inter-class correlation coefficient (ICC), Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection and radiomics signature building.Nine radiomics models including four single-modality radiomics models (DCE-3D, DCE-2D, BMUS, and SE), four multi-modality radiomics models (BMUS + SE, DCE-3D + BMUS, DCE-3D + SE, and DCE-3D+SE+BMUS), and the combination diagnostic model(clinical features and DCE-3D+SE+BMUS features) were developed and evaluated by their discrimination, calibration, and clinical usefulness.

Inclusion Criteria:

Patients with breast lesions underwent biopsy or surgical resection between January 1, 2018 and March 30, 2024.

Exclusion Criteria:

1. pathologicalresult of biopsy or surgical specimen was unavailable for the target lesion;
2. patients without DCE-MRI, BMUS and SE examinations before biopsy or surgery within one month;
3. patients hadperformed radiotherapy, chemotherapy, or breast biopsy before MRI and ultrasound examinations;
4. patients without completeDICOM data for each examination;
5. patients with poor-qualityimages.