Radiomics Analysis of Breast Lesions in Combination with Coronal Plane of ABVS and Strain Elastography

Qianqing Ma; Chunyun Shen; Yankun Gao; Yayang Duan; Wanyan Li; Gensheng Lu; Xiachuan Qin; Chaoxue Zhang; Junli Wang

doi:10.2147/BCTT.S410356

Radiomics Analysis of Breast Lesions in Combination with Coronal Plane of ABVS and Strain Elastography

Breast Cancer (Dove Med Press). 2023 May 26:15:381-390. doi: 10.2147/BCTT.S410356. eCollection 2023.

Authors

Qianqing Ma^#¹, Chunyun Shen^#², Yankun Gao^#³, Yayang Duan¹, Wanyan Li⁴, Gensheng Lu⁵, Xiachuan Qin¹, Chaoxue Zhang¹, Junli Wang²

Affiliations

¹ Department of Ultrasound, the First Affiliated Hospital of Anhui Medical University, Hefei, People's Republic of China.
² Department of Ultrasound, Wuhu No. 2 People's Hospital, Wuhu, People's Republic of China.
³ Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, People's Republic of China.
⁴ Department of Ultrasound, Linquan Country People's Hospital, Fuyang, People's Republic of China.
⁵ Department of Pathology, Wuhu No. 2 People's Hospital, Wuhu, People's Republic of China.

^# Contributed equally.

Abstract

Background: Breast cancer is the most common tumor globally. Automated Breast Volume Scanner (ABVS) and strain elastography (SE) can provide more useful breast information. The use of radiomics combined with ABVS and SE images to predict breast cancer has become a new focus. Therefore, this study developed and validated a radiomics analysis of breast lesions in combination with coronal plane of ABVS and SE to improve the differential diagnosis of benign and malignant breast diseases.

Patients and methods: 620 pathologically confirmed breast lesions from January 2017 to August 2021 were retrospectively analyzed and randomly divided into a training set (n=434) and a validation set (n=186). Radiomic features of the lesions were extracted from ABVS, B-ultrasound, and strain elastography (SE) images, respectively. These were then filtered by Gradient Boosted Decision Tree (GBDT) and multiple logistic regression. The ABVS model is based on coronal plane features for the breast, B+SE model is based on features of B-ultrasound and SE, and the multimodal model is based on features of three examinations. The evaluation of the predicted performance of the three models used the receiver operating characteristic (ROC) and decision curve analysis (DCA).

Results: The area under the curve, accuracy, specificity, and sensitivity of the multimodal model in the training set are 0.975 (95% CI:0.959-0.991),93.78%, 92.02%, and 96.49%, respectively, and 0.946 (95% CI:0.913 -0.978), 87.63%, 83.93%, and 93.24% in the validation set, respectively. The multimodal model outperformed the ABVS model and B+SE model in both the training (P < 0.001, P = 0.002, respectively) and validation sets (P < 0.001, P = 0.034, respectively).

Conclusion: Radiomics from the coronal plane of the breast lesion provide valuable information for identification. A multimodal model combination with radiomics from ABVS, B-ultrasound, and SE could improve the diagnostic efficacy of breast masses.

Keywords: automated breast volume scanner; breast cancer; radiomics; strain elastography; ultrasound.