DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response

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A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings

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Q58804561-732C8032-DA17-4D54-BE09-D96558811D39

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DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response

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2017 nî lūn-bûn

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2017年學術文章

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2017年學術文章

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name

DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response

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label

DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response

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prefLabel

DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response

@en

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DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response

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Alina Tudorica

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Aneela Afzal

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Arpana Naik

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Guillaume Thibault

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Karen Y Oh

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Kathleen A Kemmer

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Megan L Holtorf

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Megan L Troxell

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Neda Jafarian

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Nicole Roy

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A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images

Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge

Variation of the relaxographic "shutter-speed" for transcytolemmal water exchange affects the CR bolus-tracking curve shape.

Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results.

Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data.

Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers.

Pathological complete response after neoadjuvant chemotherapy is an independent predictive factor irrespective of simplified breast cancer intrinsic subtypes: a landmark and two-step approach analyses from the EORTC 10994/BIG 1-00 phase III trial.

Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols.

Computerized assessment of breast lesion malignancy using DCE-MRI robustness study on two independent clinical datasets from two manufacturers.

Use of new imaging techniques to predict tumour response to therapy.

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23-32

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scholarly article

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10.18383/J.TOM.2016.00241

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2017-03-01T00:00:00Z

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28691102

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5500247

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DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response

about

P2860

P2860

DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response

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type

label

prefLabel

P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P577

P698

P932

P356

P1433

P1476

P2093

P2860

P304

P31

P356

P433

P478

P577

P698

P932