Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions.

Background

While breast imaging such as full-field digital mammography and digital breast tomosynthesis have helped to reduced breast cancer mortality, issues with low specificity exist resulting in unnecessary biopsies. The fundamental information used in diagnostic decisions are primarily based in lesion morphology. We explore a dual-energy compositional breast imaging technique known as three-compartment breast (3CB) to show how the addition of compositional information improves malignancy detection.

Methods

Women who presented with Breast Imaging-Reporting and Data System (BI-RADS) diagnostic categories 4 or 5 and who were scheduled for breast biopsies were consecutively recruited for both standard mammography and 3CB imaging. Computer-aided detection (CAD) software was used to assign a morphology-based prediction of malignancy for all biopsied lesions. Compositional signatures for all lesions were calculated using 3CB imaging and a neural network evaluated CAD predictions with composition to predict a new probability of malignancy. CAD and neural network predictions were compared to the biopsy pathology.

Results

The addition of 3CB compositional information to CAD improves malignancy predictions resulting in an area under the receiver operating characteristic curve (AUC) of 0.81 (confidence interval (CI) of 0.74-0.88) on a held-out test set, while CAD software alone achieves an AUC of 0.69 (CI 0.60-0.78). We also identify that invasive breast cancers have a unique compositional signature characterized by reduced lipid content and increased water and protein content when compared to surrounding tissues.

Conclusion

Clinically, 3CB may potentially provide increased accuracy in predicting malignancy and a feasible avenue to explore compositional breast imaging biomarkers.

Informations de copyright

© The Author(s) 2021.

Déclaration de conflit d'intérêts

Competing interestsK.D. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: receives royalties from Hologic. Other relationships: disclosed no relevant relationships. M.G. Activities related to the present article: disclosed no relevant relationships. Activities related to the present article: is a stockholder in R2/Hologic; is a co-founder in Quantitative Insights (now advisor to Qlarity Imaging); receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba; receives royalties through institution is a licensee on patents. Other relationships: disclosed no relevant relationships. J.S. Activities related to the present article: in kind equipment support from Hologic and iCAD. Activities not related to the present article: investigator-initiated grant from Hologic. Other relationships: disclosed no relevant relationships. All other authors have no competing interests to declare.