Online citations, reference lists, and bibliographies.
Please confirm you are human
(Sign Up for free to never see this)
← Back to Search

Preoperative Prediction Of Sentinel Lymph Node Metastasis In Breast Cancer By Radiomic Signatures From Dynamic Contrast‐enhanced MRI

C. Liu, J. Ding, Karl D Spuhler, Yi Gao, Mario Serrano Sosa, M. Moriarty, S. Hussain, X. He, Changhong Liang, Chuan Huang
Published 2019 · Medicine

Save to my Library
Download PDF
Analyze on Scholarcy
Sentinel lymph node (SLN) status is an important prognostic factor for patients with breast cancer, which is currently determined in clinical practice by invasive SLN biopsy.
This paper references
Dong-hui Xu (2004)
Predicting sentinel lymph node metastasis in a Chinese breast cancer population: assessment of an existing nomogram and a new predictive nomogram
Jia-ying Chen (2012)
Textured image segmentation. University of Southern California Los Angeles Image Processing INST
K I Laws (1980)
Validation over time of a nomogram including HER2 status to predict the sentinel node positivity in early breast carcinoma.
C. Ngô (2012)
A validated risk assessment of sentinel lymph node involvement in breast cancer patients
J Veerapong (2011)
Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI
Y. Dong (2017)
Risk factors for sentinel lymph node metastasis and validation study of the MSKCC nomogram in breast cancer patients.
Pengfei Qiu (2012)
Cancer statistics, 2018
R. Siegel (2018)
Sentinel Lymph Node Biopsy in Breast Cancer: Indications, Contraindications, and Controversies
G. Manca (2016)
Textural Features for Image Classification
R. Haralick (1973)
Axillary lymph nodes and breast
A Recht
Textural features corresponding to textural properties
Moses Amadasun (1989)
Radiogenomics: what it is and why it is important.
M. Mazurowski (2015)
Textured Image Segmentation
Kenneth I. Laws (1980)
The Impact of Flip Angle and TR on the Enhancement Ratio of Dynamic Gadobutrol-enhanced MR Imaging: In Vivo VX2 Tumor Model and Computer Simulation.
Po-Chou Chen (2015)
High expression of macrophage colony-stimulating factor in peritumoral liver tissue is associated with poor survival after curative resection of hepatocellular carcinoma.
Xiao-Dong Zhu (2008)
Tumor microvessel density, p53 expression, tumor size, and peritumoral lymphatic vessel invasion are relevant prognostic markers in node-negative breast carcinoma.
G. Gasparini (1994)
Shape and Texture Indexes Application to Cell nuclei Classification
G. Thibault (2013)
Cancer treatment and survivorship statistics ,
CE DeSantis (2014)
The Sentinel Node Procedure in Breast Cancer: Nuclear Medicine as the Starting Point
E. Hindie (2011)
Prediction of malignancy by a radiomic signature from contrast agent‐free diffusion MRI in suspicious breast lesions found on screening mammography.
S. Bickelhaupt (2017)
Rapid Texture Identification
Kenneth I. Laws (1980)
Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings
P. Prasanna (2016)
Axillary lymph nodes and breast cancer. A review
A. Recht (1995)
Objective assessment of lymphedema, shoulder function and sensory deficit after sentinel node biopsy for invasive breast cancer: ALMANAC trial
R Mansel (2004)
Sentinel Lymph Node Biopsy for Patients With Early-Stage Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline Update.
G. Lyman (2017)
Role of patient and tumor characteristics in sentinel lymph node metastasis in patients with luminal early breast cancer: an observational study
N. L. La Verde (2016)
Significance of lymphatic invasion combined with size of primary tumor for predicting sentinel lymph node metastasis in patients with breast cancer.
T. Fujii (2015)
Effect of Axillary Dissection vs No Axillary Dissection on 10-Year Overall Survival Among Women With Invasive Breast Cancer and Sentinel Node Metastasis: The ACOSOG Z0011 (Alliance) Randomized Clinical Trial
A. Giuliano (2017)
The role of MRI in axillary lymph node imaging in breast cancer patients: a systematic review
V. J. L. Kuijs (2015)
A freeware for tumor heterogeneity characterization in PET, SPECT, CT, MRI and US to accelerate advances in radiomics
C Nioche (2017)
Cancer treatment and survivorship statistics, 2014
Carol E DeSantis (2014)
Technical appendix, local image features extraction
F Orlhac (2016)
Radiomics: the bridge between medical imaging and personalized medicine
P. Lambin (2017)
Technical appendix, local image features extraction, LIFEx
F Orlhac (2016)
Assessment of the Memorial Sloan-Kettering Cancer Center nomogram to predict sentinel lymph node metastases in a Dutch breast cancer population.
R. F. D. van la Parra (2013)
Surgical Complications Associated With Sentinel Lymph Node Biopsy: Results From a Prospective International Cooperative Group Trial
L. Wilke (2006)
Quality of Life After Sentinel Lymph Node Biopsy or Axillary Lymph Node Dissection in Stage I/II Breast Cancer Patients: A Prospective Longitudinal Study
J. Kootstra (2008)
Cancer statistics, 2018
Rebecca L. Siegel Mph (2018)
The impact of flip angle and TR on the enhancement ratio of dynamic gadobutrol-enhanced MR imaging: in vivo VX2 tumor model and computer simulation
Chen P-C (2015)
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
Haibo He (2008)
Radiomics: Images Are More than Pictures, They Are Data
R. Gillies (2016)
Good Prediction of the Likelihood for Sentinel Lymph Node Metastasis by Using the MSKCC Nomogram in a German Breast Cancer Population
M. Klar (2009)
Cancer treatment and survivorship
CE DeSantis (2014)
Focal breast edema associated with malignancy on T2-weighted images of breast MRI: peritumoral edema, prepectoral edema, and subcutaneous edema
T. Uematsu (2014)
The Molecular Subtype Classification Is a Determinant of Sentinel Node Positivity in Early Breast Carcinoma
F. Reyal (2011)
Doctor, what are my chances of having a positive sentinel node? A validated nomogram for risk estimation.
J. Bevilacqua (2007)
Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.
Yanqi Huang (2016)
Society of Surgical Oncology annual cancer symposium
Lidia Siemaszkiewicz (2008)
A Review of the
Robert Wolpert (1985)
A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
Shaoxu Wu (2017)
Prediction of venous metastases, recurrence, and prognosis in hepatocellular carcinoma based on a unique immune response signature of the liver microenvironment.
A. Budhu (2006)
Tumor-infiltrating lymphocytes in breast cancer: ready for prime time?
A. Ocaña (2015)
Radiomics: extracting more information from medical images using advanced feature analysis.
P. Lambin (2012)
Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI
Nathaniel Braman (2017)

This paper is referenced by
Evaluation of human epidermal growth factor receptor 2 status of breast cancer using preoperative multidetector computed tomography with deep learning and handcrafted radiomics features
X. Yang (2020)
Artificial Intelligence in Medical Imaging
E. Ranschaert (2019)
Radiomics Signatures Based on Multiparametric MRI for the Preoperative Prediction of the HER2 Status of Patients with Breast Cancer.
J. Zhou (2020)
Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region
Qiuchang Sun (2020)
Breast MRI radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients
K. Drukker (2019)
A multi-scanner study of MRI radiomics in uterine cervical cancer: prediction of in-field tumor control after definitive radiotherapy based on a machine learning method including peritumoral regions
Akiyo Takada (2020)
Preoperative Ultrasound Radiomics Signatures for Noninvasive Evaluation of Biological Characteristics of Intrahepatic Cholangiocarcinoma.
Yu-ting Peng (2019)
Deep Learning Signature Based on Staging CT for Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer.
X. Yang (2019)
Review of the Role of Radiomics in Tumour Risk Classification and Prognosis of Cancer
Yeo LI WEN (2020)
Radiomics-Based Non-Invasive Lymph Node Metastases Prediction in Breast Cancer
E. Cordelli (2020)
Influence of temporal parameters of DCE‐MRI on the quantification of heterogeneity in tumor vascularization
A. Crombé (2019)
Development and Validation of a Preoperative Magnetic Resonance Imaging Radiomics–Based Signature to Predict Axillary Lymph Node Metastasis and Disease-Free Survival in Patients With Early-Stage Breast Cancer
Y. Yu (2020)
Machine learning in breast MRI
B. Reig (2019)
Dynamic contrast‐enhanced and diffusion‐weighted MRI of invasive breast cancer for the prediction of sentinel lymph node status
E. Choi (2019)
Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis
W. Wang (2020)
Computerized evaluation scheme to detect metastasis in sentinel lymph nodes using contrast-enhanced computed tomography before breast cancer surgery
Hiroshi Ashiba (2018)
Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential.
S. Paidi (2020)
Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
M. Sollini (2019)
Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art
Alessia Angela Maria Orlando (2020)
Multi-parametric MRI lesion heterogeneity biomarkers for breast cancer diagnosis.
Marialena I. Tsarouchi (2020)
MRI Radiomic Features: Association with Disease-Free Survival in Patients with Triple-Negative Breast Cancer
S. Kim (2020)
Could Ultrasound‐Based Radiomics Noninvasively Predict Axillary Lymph Node Metastasis in Breast Cancer?
Xiaoying Qiu (2020)
The Application of Radiomics in Breast MRI: A Review
Dong-Man Ye (2020)
Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast‐enhanced‐MRI‐based radiomics
Zhuangsheng Liu (2019)
Radiomic Nomogram for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer: Predictive Value of Staging Contrast-enhanced CT.
Xiaomei Huang (2020)
Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer
M. Liu (2020)
Bag-of-features-based radiomics for differentiation of ocular adnexal lymphoma and idiopathic orbital inflammation from contrast-enhanced MRI
Yuqing Hou (2020)
High‐Grade Soft‐Tissue Sarcomas: Can Optimizing Dynamic Contrast‐Enhanced MRI Postprocessing Improve Prognostic Radiomics Models?
A. Crombé (2020)
Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer.
Jie Ding (2020)
Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
J. Liu (2019)
Task‐based assessment of a convolutional neural network for segmenting breast lesions for radiomic analysis
Karl D Spuhler (2019)
Robustness of radiomic features in magnetic resonance imaging: review and a phantom study
Renee Cattell (2019)
See more
Semantic Scholar Logo Some data provided by SemanticScholar