Lesion Composition and Quantitative Imaging Analysis on Breast Cancer Diagnosis
Lesion Composition and Quantitative Imaging Analysis on Breast Cancer Diagnosis
批准号:
10316696
负责人:
Maryellen L. Giger
金额:
$69.8万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-08-09 至 2026-07-31
关键词:
AddressAgeBenignBiologicalBiological MarkersBiopsyBreastBreast Cancer DetectionBreast Cancer Early DetectionCancer DetectionCharacteristicsClinicalCommunitiesComplementComputer AssistedContrast MediaDiagnosisDiagnosticDiagnostic ImagingDiagnostic SpecificityDigital Breast TomosynthesisDigital MammographyEffectivenessEmerging TechnologiesFDA approvedGoalsHealthHormone ReceptorImageImage AnalysisLesionLipidsMachine LearningMalignant - descriptorMalignant NeoplasmsMammary Gland ParenchymaMammographyMeasuresMethodsMissionModelingMorphologyOutcomePainParticipantPerformancePersonal SatisfactionProbabilityProceduresProteinsProtocols documentationPublic HealthReaderRecommendationResearchResearch SupportRisk FactorsSensitivity and SpecificitySpecificitySystemTechnologyTextureTissuesUnited States National Institutes of HealthWaterWomanbreast cancer diagnosisbreast densitybreast imagingbreast lesioncancer subtypescancer typeclinical riskclinical translationcomputer aided detectioncontrast enhanceddeep learningdesigndiagnostic accuracydiagnostic screeningdisorder preventionexperiencehuman diseaseimaging systemimprovedinnovationinsightmalignant breast neoplasmmedical specialtiesprogramsprospectivequantitative imagingradiologistresearch clinical testingscreeningstandard of caretomosynthesistool
中文摘要
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英文摘要
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 composition with
DBT and how it complements existing computer aided diagnostic programs paired with clinical radiologists
providing evidence ready for clinical translation of this unique and emerging technology.
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Lesion Composition and Quantitative Imaging Analysis on Breast Cancer Diagnosis
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批准号:10674035
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项目类别:
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资助金额:$61.7万
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财政年份:2021
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负责人:Maryellen L. Giger
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依托单位:
Protected Radiomics Analysis Commons for Deep Learning in Biomedical Discovery
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批准号:9494294
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资助金额:$33.89万
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财政年份:2018
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负责人:Maryellen L. Giger
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依托单位:
Quantitative Image Analysis for Assessing Response to Breast Cancer Therapy
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批准号:8889341
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项目类别:
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资助金额:$50.37万
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财政年份:2015
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负责人:Maryellen L. Giger
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依托单位:
Quantitative Image Analysis for Assessing Response to Breast Cancer Therapy
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批准号:9249507
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项目类别:
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资助金额:$50.37万
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财政年份:2015
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负责人:Maryellen L. Giger
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依托单位:
Lesion Composition and Quantitative Imaging Analysis on Breast Cancer Diagnosis
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批准号:8439678
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项目类别:
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资助金额:$63.52万
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财政年份:2013
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负责人:Maryellen L. Giger
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依托单位:
Lesion Composition and Quantitative Imaging Analysis on Breast Cancer Diagnosis
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批准号:8835068
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项目类别:
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资助金额:$59.27万
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财政年份:2013
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负责人:Maryellen L. Giger
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依托单位:
Lesion Composition and Quantitative Imaging Analysis on Breast Cancer Diagnosis
-
批准号:8978083
-
项目类别:
-
资助金额:$6.99万
-
财政年份:2013
-
负责人:Maryellen L. Giger
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依托单位:
Lesion Composition and Quantitative Imaging Analysis on Breast Cancer Diagnosis
-
批准号:9438084
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项目类别:
-
资助金额:$6.5万
-
财政年份:2013
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负责人:Maryellen L. Giger
-
依托单位:
PROGRAM 5 (ADVANCED IMAGING)
-
批准号:7714259
-
项目类别:
-
资助金额:$2.61万
-
财政年份:2008
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负责人:Maryellen L. Giger
-
依托单位:
Optimization of CAD Output in Breast Imaging
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批准号:7268049
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项目类别:
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资助金额:$16.99万
-
财政年份:2006
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负责人:Maryellen L. Giger
-
依托单位:
Optimization of CAD Output in Breast Imaging
-
批准号:7034963
-
项目类别:
-
资助金额:$14.56万
-
财政年份:2006
-
负责人:Maryellen L. Giger
-
依托单位:
Optimization of CAD Output in Breast Imaging
-
批准号:7813811
-
项目类别:
-
资助金额:$42.16万
-
财政年份:2006
-
负责人:Maryellen L. Giger
-
依托单位:
Imaging-based Determination of Breast Cancer Risk
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批准号:7198287
-
项目类别:
-
资助金额:$21.59万
-
财政年份:2006
-
负责人:Maryellen L. Giger
-
依托单位:
Optimization of CAD Output in Breast Imaging
-
批准号:7622964
-
项目类别:
-
资助金额:$40.77万
-
财政年份:2006
-
负责人:Maryellen L. Giger
-
依托单位:
Optimization of CAD Output in Breast Imaging
-
批准号:7630517
-
项目类别:
-
资助金额:$41.82万
-
财政年份:2006
-
负责人:Maryellen L. Giger
-
依托单位:
COMPUTER AIDED DIAGNOSIS IN BREAST IMAGING
-
批准号:6232388
-
项目类别:
-
资助金额:$31.98万
-
财政年份:2001
-
负责人:Maryellen L. Giger
-
依托单位:
COMPUTER AIDED DIAGNOSIS IN BREAST IMAGING
-
批准号:6633915
-
项目类别:
-
资助金额:$29.87万
-
财政年份:2001
-
负责人:Maryellen L. Giger
-
依托单位:
COMPUTER AIDED DIAGNOSIS IN BREAST IMAGING
-
批准号:6724936
-
项目类别:
-
资助金额:$29.87万
-
财政年份:2001
-
负责人:Maryellen L. Giger
-
依托单位:
COMPUTER AIDED DIAGNOSIS IN BREAST IMAGING
-
批准号:6514858
-
项目类别:
-
资助金额:$31.14万
-
财政年份:2001
-
负责人:Maryellen L. Giger
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依托单位:
COMPUTER AIDED DIAGNOSIS IN BREAST IMAGING
-
批准号:6875573
-
项目类别:
-
资助金额:$29.87万
-
财政年份:2001
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负责人:Maryellen L. Giger
-
依托单位:
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