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

Autor:	Leong LT; Department of Epidemiology and Population Sciences, University of Hawaii Cancer Center, Honolulu, HI USA.; Department Molecular Bioscience and Bioengineering, University of Hawaii at Manoa, Honolulu, HI USA., Malkov S; Departments Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA USA., Drukker K; Department of Radiology, University of Chicago, Chicago, IL USA., Niell BL; Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL USA., Sadowski P; Department of Information and Computer Science, University of Hawaii at Manoa, Honolulu, HI USA., Wolfgruber T; Department of Epidemiology and Population Sciences, University of Hawaii Cancer Center, Honolulu, HI USA., Greenwood HI; Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA USA., Joe BN; Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA USA., Kerlikowske K; Departments Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA USA.; Department of Medicine, University of California, San Francisco, San Francisco, CA USA., Giger ML; Department of Radiology, University of Chicago, Chicago, IL USA., Shepherd JA; Department of Epidemiology and Population Sciences, University of Hawaii Cancer Center, Honolulu, HI USA.
Jazyk:	angličtina
Zdroj:	Communications medicine [Commun Med (Lond)] 2021 Aug 31; Vol. 1, pp. 29. Date of Electronic Publication: 2021 Aug 31 (Print Publication: 2021).
DOI:	10.1038/s43856-021-00024-0
Abstrakt:	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. Competing Interests: 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. (© The Author(s) 2021.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu