Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification

Journal Article · Sat Mar 15 04:00:00 EDT 2014 · Medical Physics

DOI:https://doi.org/10.1118/1.4866221· OSTI ID:22250960

Drukker, Karen; Giger, Maryellen L.; Li, Hui ^[1]; Duewer, Fred; Malkov, Serghei; Joe, Bonnie; Kerlikowske, Karla; Shepherd, John A. ^[2]; Flowers, Chris I. ^[3]; Drukteinis, Jennifer S. ^[4]

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1. Department of Radiology, University of Chicago, Chicago, Illinois 60637 (United States)
2. Radiology Department, University of California, San Francisco, California 94143 (United States)
3. Department of Radiology, University of South Florida, Tampa, Florida 33612 (United States)
4. Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612 (United States)

Purpose: To investigate whether biologic image composition of mammographic lesions can improve upon existing mammographic quantitative image analysis (QIA) in estimating the probability of malignancy. Methods: The study population consisted of 45 breast lesions imaged with dual-energy mammography prior to breast biopsy with final diagnosis resulting in 10 invasive ductal carcinomas, 5 ductal carcinomain situ, 11 fibroadenomas, and 19 other benign diagnoses. Analysis was threefold: (1) The raw low-energy mammographic images were analyzed with an established in-house QIA method, “QIA alone,” (2) the three-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 assessment included box plots, Bland–Altman plots, and Receiver Operating Characteristic (ROC) analysis. Results: The area under the ROC curve (AUC) for distinguishing between benign and malignant lesions (invasive and DCIS) was 0.81 (standard error 0.07) for the “QIA alone” method, 0.72 (0.07) for “3CB alone” method, and 0.86 (0.04) for “QIA+3CB” combined. The difference in AUC was 0.043 between “QIA + 3CB” and “QIA alone” but failed to reach statistical significance (95% confidence interval [–0.17 to + 0.26]). Conclusions: In this pilot study analyzing the new 3CB imaging modality, knowledge of the composition of breast lesions and their periphery appeared additive in combination with existing mammographic QIA methods for the distinction between different benign and malignant lesion types.

• OSTI ID: 22250960
• Journal Information: Medical Physics, Journal Name: Medical Physics Journal Issue: 3 Vol. 41; ISSN 0094-2405; ISSN MPHYA6
• Country of Publication: United States
• Language: English

Related Subjects

62 RADIOLOGY AND NUCLEAR MEDICINE
BIOMEDICAL RADIOGRAPHY
BIOPSY
CARCINOMAS
CLASSIFICATION
DIAGNOSIS
DIAGRAMS
IMAGE PROCESSING
IMAGES
LIPIDS
MAMMARY GLANDS
PROTEINS
THICKNESS