Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data-Reference-Cited by-同舟云学术

Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data

Published:2015-09-23 Issue:4 Volume:2 Page:041007
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:en
Short-container-title:J. Med. Imag

Author:

Guo Wentian¹,Li Hui²,Zhu Yitan³,Lan Li²,Yang Shengjie³,Drukker Karen²,Morris Elizabeth⁴,Burnside Elizabeth⁵,Whitman Gary⁶,Giger Maryellen L.²,Ji Yuan⁷,TCGA Breast Phenotype Research Group ⁸

Affiliation:

1. University of Chicago, Department of Public Health Sciences, 5841 South Maryland Avenue MC2000, Chicago, Illinois 60637, United StatesdFudan University, School of Public Health, 130 Dongan Road, Shanghai 200032, China

2. University of Chicago, Department of Radiology, 5841 South Maryland Avenue, Chicago, Illinois 60637, United States

3. NorthShore University Health System, Program of Computational Genomics & Medicine, 1001 University Place, Evanston, Illinois 60201, United States

4. Memorial Sloan Kettering Cancer Center, Department of Radiology, 1275 York Avenue, New York, New York 10065, United States

5. University of Wisconsin, School of Medicine and Public Health, Department of Radiology, E3/366 Clinical Science Center, 600 Highland Avenue, Madison, Wisconsin 53792-3252, United States

6. MD Anderson, 1515 Holcombe Boulevard, Houston, Texas 77030, United States

7. University of Chicago, Department of Public Health Sciences, 5841 South Maryland Avenue MC2000, Chicago, Illinois 60637, United StatescNorthShore University Health System, Program of Computational Genomics & Medicine, 1001 University Place, Evanston, Illi

8. https://wiki.cancerimagingarchive.net/display/Public/TCGA+Breast+Phenotype+Research+Group

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology Nuclear Medicine and imaging

Reference36 articles.

1. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

2. Radiogenomics: Radiobiology Enters the Era of Big Data and Team Science

3. Promise and pitfalls of quantitative imaging in oncology clinical trials

4. Predicting outcomes in radiation oncology—multifactorial decision support systems

5. TNM Classification of Malignant Tumours;Sobin,2011

Cited by 130 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Cross-Modality Calibration in Multi-Input Network for Axillary Lymph Node Metastasis Evaluation;IEEE Transactions on Artificial Intelligence;2024-09

2. Impact of MRI radiomic feature normalization for prognostic modelling in uterine endometrial and cervical cancers;Scientific Reports;2024-07-22

3. Impact of MRI radiomic feature normalization for prognostic modelling in uterine endometrial and cervical cancers.;2024-02-19

4. Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis;Frontiers in Oncology;2024-01-11

5. Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer;Frontiers in Oncology;2023-10-24