Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI.
about
Exploring nonlinear feature space dimension reduction and data representation in breast Cadx with Laplacian eigenmaps and t-SNE.Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.Residual analysis of the water resonance signal in breast lesions imaged with high spectral and spatial resolution (HiSS) MRI: a pilot study.Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data.Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data setIncorporating a vascular term into a reference region model for the analysis of DCE-MRI data: a simulation studyDynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology: Theory, Data Acquisition, Analysis, and ExamplesMechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response.Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers.Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques.Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement.Small lesions evaluation based on unsupervised cluster analysis of signal-intensity time courses in dynamic breast MRI.STEP: spatiotemporal enhancement pattern for MR-based breast tumor diagnosis.Principal component analysis of breast DCE-MRI adjusted with a model-based methodComputerized assessment of breast lesion malignancy using DCE-MRI robustness study on two independent clinical datasets from two manufacturers.Computerized three-class classification of MRI-based prognostic markers for breast cancer.Potential of computer-aided diagnosis of high spectral and spatial resolution (HiSS) MRI in the classification of breast lesionsQuantitative analysis of clinical dynamic contrast-enhanced MR imaging for evaluating treatment response in human breast cancer.Combined use of T2-weighted MRI and T1-weighted dynamic contrast-enhanced MRI in the automated analysis of breast lesions.Semi-automatic region-of-interest segmentation based computer-aided diagnosis of mass lesions from dynamic contrast-enhanced magnetic resonance imaging based breast cancer screeningA multichannel Markov random field framework for tumor segmentation with an application to classification of gene expression-based breast cancer recurrence risk.Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility studyA Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores.Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.The diverse pathology and kinetics of mass, nonmass, and focus enhancement on MR imaging of the breast.Quantification of heterogeneity as a biomarker in tumor imaging: a systematic reviewBreast DCE-MRI Kinetic Heterogeneity Tumor Markers: Preliminary Associations With Neoadjuvant Chemotherapy Response.Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage.Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma.Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles.Using quantitative image analysis to classify axillary lymph nodes on breast MRI: a new application for the Z 0011 Era.Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI.Texture Feature Ratios from Relative CBV Maps of Perfusion MRI Are Associated with Patient Survival in Glioblastoma.Associating spatial diversity features of radiologically defined tumor habitats with epidermal growth factor receptor driver status and 12-month survival in glioblastoma: methods and preliminary investigationSpectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging.Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses in Breast DCE-MRI.DCEMRI of breast lesions: is kinetic analysis equally effective for both mass and nonmass-like enhancement?A clinically feasible method to estimate pharmacokinetic parameters in breast cancer.MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.Classification of small contrast enhancing breast lesions in dynamic magnetic resonance imaging using a combination of morphological criteria and dynamic analysis based on unsupervised vector-quantization.
P2860
Q30477890-90C2E061-7C33-42FF-A7C8-CDEE8734A0DDQ30485015-EE9974A6-337E-4B08-BDC1-458ED970A9FFQ30725785-873DE6DA-674A-4E1E-A593-8F15DDED3F2DQ31042155-031FAB1F-806A-4193-82A4-2B8F1DD4BFF0Q31142731-8B5F3980-D68C-49EF-870C-09350E7C4AA8Q31153802-761D2E9F-6816-423C-AD8F-5BA6532E3B26Q33510695-910249FD-F338-440D-8695-5A487B4370A3Q33632105-2EA076C0-4DD8-4983-8A43-71122B3774F1Q33679786-1C1C2650-AB05-48CF-979F-389BCDADEFE7Q33705338-0AFFD966-BA74-44DB-9EA9-85C150C27051Q33715702-8D9F1751-506C-4EFA-93B2-389512349F84Q33771945-D775D44D-CDEB-4671-BF78-8E90D9ECAC84Q33780623-ED0A7CF2-8B23-4107-8A9E-844A2E0D7D43Q33856135-81243782-25F8-402B-AFF5-D056BD652277Q34010957-327F4837-4637-462B-8724-67FB2F81CBDBQ34051695-AF223ECB-5EF3-488C-881B-008AE83D2B1CQ34084804-DA51D5FF-BFD5-416B-B90F-6E344EC4E11EQ34138733-B1D68385-1C6D-4834-A201-98B2ADC6688BQ34145104-23B2B453-57F4-4FE0-817B-C6B1C0AB66D6Q34226505-39F0AA56-C375-49C4-A79B-41C65862EAD0Q34342315-71D88ADB-4AF3-430F-BCE2-A397FB0DAAEBQ34409655-0FE237AE-64B4-421B-83F8-53DA34F90A5DQ34514621-22568F54-4898-45D9-A85A-CBBD793ADD4FQ34970999-A38AEBD8-DD2F-4D44-8D64-6B4B20D434DBQ34993938-A5FF3CF0-85F6-4E95-8D9A-B4EB4C4339DDQ35350679-45FB3AA8-02CF-46EE-B81B-2C670376E8C8Q35804283-CD51A81A-D1AE-483E-93D8-C524FB80C3F5Q35855936-0C3287CA-C849-405E-B348-06452F1D59C6Q35861706-26B63F70-50FB-448C-803A-B539B1D453BBQ36041569-9562C068-F508-4517-B34C-ECD8D5EEC1ABQ36234612-DBB48DA6-4A31-405B-AB18-859C4AC29636Q36252402-5D40943D-6B99-46B6-81B8-FCE49E6BD7F5Q36463578-0E2222F1-C84C-4EDB-8264-81B057EE5C39Q36478699-C43D4D5A-7EA6-471A-A3BC-99B8B817E286Q36688273-E711401C-3535-477C-84C3-B5BBD4E936FCQ36932481-745F17E7-16E6-4DB3-B683-1FE24D183C05Q37170158-D0305353-8860-4A9E-B8A5-D1E9766E1DF7Q37309502-51938588-4C21-4D15-B291-C63B2D4775C2Q37348196-BF8C975B-F5DF-430F-974B-79289B8832BFQ37379778-49DE8982-B40F-4BB9-9523-851579448A9C
P2860
Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI.
description
2006 nî lūn-bûn
@nan
2006年の論文
@ja
2006年学术文章
@wuu
2006年学术文章
@zh
2006年学术文章
@zh-cn
2006年学术文章
@zh-hans
2006年学术文章
@zh-my
2006年学术文章
@zh-sg
2006年學術文章
@yue
2006年學術文章
@zh-hant
name
Automatic identification and c ...... of breast lesions on DCE-MRI.
@en
Automatic identification and c ...... of breast lesions on DCE-MRI.
@nl
type
label
Automatic identification and c ...... of breast lesions on DCE-MRI.
@en
Automatic identification and c ...... of breast lesions on DCE-MRI.
@nl
prefLabel
Automatic identification and c ...... of breast lesions on DCE-MRI.
@en
Automatic identification and c ...... of breast lesions on DCE-MRI.
@nl
P2860
P356
P1433
P1476
Automatic identification and c ...... of breast lesions on DCE-MRI.
@en
P2093
Gillian M Newstead
Weijie Chen
P2860
P304
P356
10.1118/1.2210568
P407
P577
2006-08-01T00:00:00Z