Added Value of Radiomics on Mammography for Breast Cancer Diagnosis: A Feasibility Study-Reference-Cited by-同舟云学术

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Added Value of Radiomics on Mammography for Breast Cancer Diagnosis: A Feasibility Study

Published:2019-04 Issue:4 Volume:16 Page:485-491
ISSN:1546-1440
Container-title:Journal of the American College of Radiology
language:en
Short-container-title:Journal of the American College of Radiology

Author:

Mao Ning,Yin Ping,Wang Qinglin,Liu Meijie,Dong Jianjun,Zhang Xuexi,Xie Haizhu,Hong Nan

Funder

National Natural Science Foundation of China

Shandong Medical and Health Science and Technology Development

Natural Science Foundation of Shandong Province

Publisher

Elsevier BV

Subject

Radiology, Nuclear Medicine and imaging

Reference36 articles.

1. Feature selection in computer-aided breast cancer diagnosis via dynamic contrast-enhanced magnetic resonance images;Rakoczy;J Digit Imaging,2013

2. Digital mammography: an update;Schulz-Wendtland;Eur J Radiol,2009

3. An investigation of Bayes algorithm and neural networks for identifying the breast cancer;Udayakumar;Indian J Med Paediatr Oncol,2017

4. The preponderance of evidence supports computer-aided detection for screening mammography;Birdwell;Radiology,2009

5. Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions;Tan;Phys Med Biol,2017

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1. Decoding Breast Cancer: Using Radiomics to Non-Invasively Unveil Molecular Subtypes Directly from Mammographic Images;Journal of Imaging;2024-09-04

2. Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames;Frontiers in Endocrinology;2024-04-03

3. Machine learning and new insights for breast cancer diagnosis;Journal of International Medical Research;2024-04

4. Sistema de clasificación de tipo de tejido mamario con machine learning;Libros IC;2024-03-28

5. Adaptive Machine Learning Approach for Importance Evaluation of Multimodal Breast Cancer Radiomic Features;Journal of Imaging Informatics in Medicine;2024-03-13

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