Lesion-specific exposure parameters for breast cancer diagnosis on digital breast tomosynthesis and full-field digital mammography. (August 2022)

• Record Type: Journal Article
• Authors: Ma, Le; Liu, Hui; Lin, Xiaojia; Cai, Yuxing; Zhang, Ling; Chen, Weiguo; Qin, Genggeng
• Is Part Of: Biomedical signal processing and control. Volume 77(2022)
• Publication Date: 2022-08
• Subjects: Digital breast tomosynthesis -- Full-field digital mammography -- Breast lesion -- Exposure parameter; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103752
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The malignant group had greater kVp, mAs, CBT, and MGD compared to benign group. Breast thickness, density and lesion size were associated with the difference in MGD. Breast lesion type clearly influenced exposure parameters when using AEC. Our result can be helpful in distinguishing malignancies from benign lesions. Abstract: Objectives: To explore lesion-specific exposure parameters on digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) and evaluate their efficiency for breast cancer diagnosis. Methods: We reviewed the DBT and FFDM images of 492 women with unilateral breast lesions and compared the findings with a healthy group comprising the contralateral breasts of all patients. The tube voltage (kVp), tube load (mAs), compressed breast thickness (CBT), and mean glandular dose (MGD) for each image were retrieved from the DICOM metadata, and the difference in MGD between DBT and FFDM images (△MGD) was calculated. Three models were developed to discriminate breast cancer: logistic model, comprising traditional risk factors alone; FFDM model, comprising exposure parameters from FFDM images alone, and a hybrid model, comprising traditional risk factors and exposure parameters from DBT and FFDM images. Model performance was assessed in an independent dataset of 189 women by determining the area under the receiver operating characteristic curve (AUC). Results: The malignant group showed greater kVp, mAs, CBT, and MGD values than the benign and healthy groups on both FFDM and DBT. Breast thickness, density, and lesion size were independently associated with △MGD. The hybrid model showed a significantly higher AUC value (0.78 ± 0.04) than the logistic (0.68 ± 0.05) and FFDM models (0.56 ± 0.05). Conclusions: The breast lesion type has an influence on exposure parameters when using automatic exposure control. Our results provide evidence for lesion-specific exposure parameters, which can be helpful in distinguishing benign lesions from malignancies.