AI-based strategies in breast mass ≤ 2 cm classification with mammography and tomosynthesis

Abstract

Purpose: To evaluate the diagnosis performance of digital mammography (DM) and digital breast tomosynthesis (DBT), DM combined DBT with AI-based strategies for breast mass ≤ 2 cm. Materials and methods: DM and DBT images in 483 patients including 512 breast masses were acquired from November 2018 to November 2019. Malignant and benign tumours were determined by biopsies using histological analysis and follow-up within 24 months. The radiomics and deep learning methods were employed to extract the breast mass features in images and finally for benign and malignant classification. The DM, DBT and DM combined DBT (DM + DBT) images were fed into radiomics and deep learning models to construct corresponding models, respectively. The area under the receiver operating characteristic curve (AUC) was employed to estimate model performance. An external dataset of 146 patients from March 2021 to December 2022 from another center was enrolled for external validation. Results: In the internal testing dataset, compared with the DM model and the DBT model, the DM + DBT models based on radiomics and deep learning both showed statistically significant higher AUCs [0.810 (RA-DM), 0.823 (RA-DBT) and 0.869 (RA-DM + DBT), P ≤ 0.001; 0.867 (DL-DM), 0.871 (DL-DBT) and 0.908 (DL-DM + DBT), P = 0.001]. The deep learning models present superior to the radiomics models in the experiments with only DM (0.867 vs 0.810, P = 0.001), only DBT (0.871 vs 0.823, P = 0.001) and DM + DBT (0.908 vs 0.869, P = 0.003). Conclusions: DBT has a clear additional value for diagnosing breast mass less than 2 cm compared with only DM. AI-based methods, especially deep learning, can help achieve excellent performance.

Keywords

• Breast mass
• Mammography
• Digital breast tomosynthesis
• Radiomics
• Deep learning