Karen Drukker PhD, Presenter: Royalties, Hologic, Inc

Serghei Malkov PhD, Abstract Co-Author: Nothing to Disclose

Maryellen L. Giger PhD, Abstract Co-Author: Stockholder, Hologic, Inc Shareholder, Quantitative Insights, Inc Royalties, Hologic, Inc Royalties, General Electric Company Royalties, MEDIAN Technologies Royalties, Riverain Technologies, LLC Royalties, Mitsubishi Corporation Royalties, Toshiba Corporation Researcher, Koninklijke Philips NV Researcher, U-Systems, Inc

Bonnie N. Joe MD, PhD, Abstract Co-Author: Nothing to Disclose

Karla Kerlikowske MD, Abstract Co-Author: Nothing to Disclose

Jennifer S. Drukteinis MD, Abstract Co-Author: Nothing to Disclose

Dorota Jakubowski Wisner MD, PhD, Abstract Co-Author: Nothing to Disclose

Malesa M. Pereira BS, Abstract Co-Author: Nothing to Disclose

Leila Kazemi RT, Abstract Co-Author: Nothing to Disclose

Gregor Krings MD, PhD, Abstract Co-Author: Nothing to Disclose

Bhavika Kantilal Patel MD, Abstract Co-Author: Nothing to Disclose

John A. Shepherd PhD, Abstract Co-Author: Nothing to Disclose

To investigate whether knowledge of the biologic composition of breast lesions and the embedding parenchyma, derived through three-compartment breast (3CB) imaging, can improve upon existing mammographic quantitative image analysis (QIA) in estimating the probability of malignancy.

3CB imaging is a novel imaging technique that derived biologic tissue composition measures from dual-energy mammography and a thickness phantom at about 110% of the dose of a regular mammogram. The study population consisted of 96 patients with 102 breast lesions imaged with dual-energy mammography prior to breast biopsy with final diagnosis resulting in 16 invasive ductal carcinomas, 10 ductal carcinoma in situ (DCIS), and 76 benign diagnoses. Analysis was three-fold: 1) The raw low-energy mammographic images were analyzed with an established in-house QIA method, ‘QIA alone’, 2) the 3-compartment breast (3CB) composition measure – derived from the dual-energy mammography – of water, lipid, and protein thickness were assessed, ‘3CB alone’), and 3) information from QIA and 3CB was combined, ‘QIA+3CB’. Analysis was initiated from radiologist-indicated lesion centers and was otherwise fully automated. Steps of the QIA and 3CB methods were lesion segmentation, characterization, and subsequent classification for malignancy in leave-one-case-out cross-validation. Performance was assessed using Receiver Operating Characteristic (ROC) analysis with the area under the ROC curve (AUC) as figure of merit.

The AUC for distinguishing between benign and malignant lesions (invasive and DCIS) was 0.78 (standard error 0.06) for the ‘QIA alone’ method, 0.66 (0.06) for ‘3CB alone’ method, and improved to 0.85 (0.05) for ‘QIA+3CB’ combined (p=0.05 with respect to ‘QIA alone’).

Combining knowledge of the composition of breast lesions and their periphery with an existing mammographic QIA method improved the distinction between benign and malignant lesions, which could help prevent unnecessary biopsies and improve diagnostic decision making.

Three-Compartment Breast Imaging quantitatively assesses tissue composition of breast lesions and parenchyma and yields information largely independent from what can be gleaned from mammography alone, which could help increase biopsy yield while reducing unnecessary biopsies.

Drukker, K, Malkov, S, Giger, M, Joe, B, Kerlikowske, K, Drukteinis, J, Wisner, D, Pereira, M, Kazemi, L, Krings, G, Patel, B, Shepherd, J, Three-Compartment Breast Imaging and Quantitative Mammographic Image Analysis: Synergy for Improved Diagnosis.  Radiological Society of North America 2014 Scientific Assembly and Annual Meeting, - ,Chicago IL. http://archive.rsna.org/2014/14011500.html