Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study - Wikidata - wikimedia - TriplyDB

Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study

Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study

http://www.wikidata.org/entity/Q34719906

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Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma.Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk Background parenchymal uptake on molecular breast imaging as a breast cancer risk factor: a case-control study Association Between Changes in Mammographic Image Features and Risk for Near-Term Breast Cancer Development A half-second glimpse often lets radiologists identify breast cancer cases even when viewing the mammogram of the opposite breast 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.Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status.Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations.Breast density estimation from high spectral and spatial resolution MRI.Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions.A novel and fully automated mammographic texture analysis for risk prediction: results from two case-control studies Applying a new bilateral mammographic density segmentation method to improve accuracy of breast cancer risk prediction.Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms.

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P2860

Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study

http://www.wikidata.org/entity/Q34719906

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2014 nî lūn-bûn

@nan

2014 թուականի Օգոստոսին հրատարակուած գիտական յօդուած

@hyw

2014 թվականի օգոստոսին հրատարակված գիտական հոդված

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2014年の論文

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2014年論文

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2014年論文

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2014年論文

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2014年論文

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2014年論文

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2014年论文

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P2860

Prevention of breast cancer in postmenopausal women: approaches to estimating and reducing risk

Mammographic density. Measurement of mammographic density

Some practical issues of experimental design and data analysis in radiological ROC studies.

Predicting the likelihood of carrying a BRCA1 or BRCA2 mutation: validation of BOADICEA, BRCAPRO, IBIS, Myriad and the Manchester scoring system using data from UK genetics clinics.

Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data.

Tolerability of breast ductal lavage in women from families at high genetic risk of breast cancer

Relationship of mammographic density and gene expression: analysis of normal breast tissue surrounding breast cancer.

Clinical management of hereditary breast cancer syndromes.

Power spectral analysis of mammographic parenchymal patterns for breast cancer risk assessment.

Mammographic density does not differ between unaffected BRCA1/2 mutation carriers and women at low-to-average risk of breast cancer.

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