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Book ChapterDOI

Radiomics Based Analysis of Breast Tumors in DCE-MRI due to Neoadjuvant Treatment Therapy

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TLDR
In this article, Radiomic analysis was performed on 20 studies of 10 patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for early prediction of breast cancer.
Abstract
Breast cancer is the utmost frequent cancer amid women. The efficiency of cancer treatments on tumor development is carried out using a most sensitive method dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Neoadjuvant chemotherapy (NAC) treatment is widely used in patients for early prediction of breast cancer. The development of extraction of high throughput of features is referred to radiomics. The purpose of this study is to determine the capability of radiomic features in the breast region respond to the treatment. Radiomic analysis was performed on 20 studies of 10 patients using DCE-MRI. A total of 94 three-dimensional radiomic features were extracted and examined statistically. Results explain that the five features 90 and 99 percentile of histogram intensity, mean 3D, variance 3D, and short-run emphasis are significant with p-value from 0.0025 to 0.0694. Hence, these radiomic features are potency to identify the ambivert changes in the breast region during the follow-ups.

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Journal ArticleDOI

Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients With MRI-Radiomics: A Systematic Review and Meta-analysis.

TL;DR: A systematic review and a meta-analysis of studies using MRI-radiomics for predicting the pathological complete response in breast cancer patients undergoing neoadjuvant therapy , and evaluated their methodological quality using the radiomics quality score (RQS) as mentioned in this paper .
References
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Journal ArticleDOI

Textural Features for Image Classification

TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI

Cancer statistics, 2019.

TL;DR: The overall cancer death rate dropped continuously from 1991 to 2016 by a total of 27%, translating into approximately 2,629,200 fewer cancer deaths than would have been expected if death rates had remained at their peak.
Journal ArticleDOI

Radiomics: Images Are More than Pictures, They Are Data.

TL;DR: This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
Journal ArticleDOI

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

TL;DR: The management tasks and user support model for TCIA is described, an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer.
Journal ArticleDOI

Radiomics: the process and the challenges

TL;DR: "Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging, leading to a very large potential subject pool.
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