# Lesion Composition and Quantitative Imaging Analysis on Breast Cancer Diagnosis > **NIH NIH R01** · UNIVERSITY OF HAWAII AT MANOA · 2021 · $697,982 ## Abstract Project Summary/Abstract. Women with dense breast have not been shown to benefit by increased cancer detection of volumetric digital breast tomosynthesis (DBT) but may benefit by lower recall rates. DBT screening biopsy rates are similar to 2D digital mammography; higher for first screening exams, lower thereafter with adjustment for age and breast density. In the U.S., 71% of biopsies do not result in a breast cancer diagnosis among women ages 40-79 who undergo breast cancer screening. To address the high rate of unnecessary biopsies, an innovative way to use FDA-approved breast imaging protocols has been developed to acquire multispectral images to measure the lipid/water/protein (L/W/P) composition of suspicious breast lesions. Malignant breast tissue has unique L/W/P composition fractions when compared to normal or benign breast tissue. This proposal aims to increase biopsy yield (BI-RADS-PPV3) through combining L/W/P biological biomarkers with quantitative morphological and textural image analysis. This combination of composition and physical descriptions of suspicious breast lesions is called q3CB. The benefits of adding q3CB to the current DBT screening/diagnostic imaging paradigm, that may already include computer aided detection, is not known. This study is designed to compare the expected biopsy yield with and without q3CB in a clinical reader study and explore how q3CB may be combine with existing technologies. The central hypothesis is that biological L/W/P fractions in breast tissue in combination with analysis of morphological and textural tissue characteristics will yield significantly higher breast cancer specificity than conventional interpretation of DBT alone. The objective is to better identify suspicious breast lesions that need to be biopsied for malignancy in women currently recommended for biopsy. The long-term goal is to reduce unnecessary biopsies and increase biopsy yield. Our rationale for the proposed research is that biological L/W/P descriptions of breast lesions will lead to more specific biopsy decisions and a better understanding of cancer types. Specifically, the project aims are 1) develop q3CB lesion signatures for distinguishing breast cancer lesions from benign lesions, using 600 prospectively-acquired DBT exams of women recommended to undergo biopsy; 2) conduct a clinical reader study to compare radiologists' performance on standard-of-care FFDM or DBT without and with the inclusion of q3CB signatures; 3) Investigate the utility of q3CB lesion signatures in a screening paradigm to improve sensitivity and specificity on CADe-identified suspicious lesions in the tasks of assessing malignancy as well as in associating with their association with cancer subtypes; Exploratory) explore the added sensitivity and specificity of dual-energy DBT in phantom studies that explore lesion size, composition, and breast density. The innovation of this study is the full characterization of lipid/water/protein lesion composit... ## Key facts - **NIH application ID:** 10316696 - **Project number:** 1R01CA257652-01A1 - **Recipient organization:** UNIVERSITY OF HAWAII AT MANOA - **Principal Investigator:** Maryellen L. Giger - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $697,982 - **Award type:** 1 - **Project period:** 2021-08-09 → 2026-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10316696 ## Citation > US National Institutes of Health, RePORTER application 10316696, Lesion Composition and Quantitative Imaging Analysis on Breast Cancer Diagnosis (1R01CA257652-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10316696. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*