A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients

[1]  M. C. Comes,et al.  Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features , 2021, Applied Sciences.

[2]  V. Cuccurullo,et al.  Second-Generation 3D Automated Breast Ultrasonography (Prone ABUS) for Dense Breast Cancer Screening Integrated to Mammography: Effectiveness, Performance and Detection Rates , 2021, Journal of personalized medicine.

[3]  Chao Zhang,et al.  Preoperative prediction of axillary lymph node metastasis in patients with breast cancer based on radiomics of gray-scale ultrasonography. , 2021, Gland surgery.

[4]  G. Iannello,et al.  3T MRI-Radiomic Approach to Predict for Lymph Node Status in Breast Cancer Patients , 2021, Cancers.

[5]  A. Paradiso,et al.  Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study , 2021, Cancers.

[6]  M. Huang,et al.  Could Ultrasound‐Based Radiomics Noninvasively Predict Axillary Lymph Node Metastasis in Breast Cancer? , 2020, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[7]  Yuanyuan Wang,et al.  Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer , 2020, Nature Communications.

[8]  Georg Langs,et al.  Introduction to Radiomics , 2020, The Journal of Nuclear Medicine.

[9]  N. Wolmark,et al.  National Surgical Adjuvant Breast and Bowel Project , 2020, Definitions.

[10]  Yuanshen Zhao,et al.  Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region , 2020, Frontiers in Oncology.

[11]  Ahmedin Jemal,et al.  Breast cancer statistics, 2019 , 2019, CA: a cancer journal for clinicians.

[12]  Yuwei Xia,et al.  Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer , 2019, Front. Oncol..

[13]  M. Ohlsson,et al.  Artificial neural network models to predict nodal status in clinically node-negative breast cancer , 2019, BMC Cancer.

[14]  Jingbo Yang,et al.  Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Mammography-Based Radiomics Method , 2019, Scientific Reports.

[15]  Abdul Rahim Abdullah,et al.  EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization , 2019, Comput..

[16]  Lior Rokach,et al.  Ensemble learning: A survey , 2018, WIREs Data Mining Knowl. Discov..

[17]  M. Delgado-Rodríguez,et al.  Systematic review and meta-analysis. , 2017, Medicina intensiva.

[18]  P. Lambin,et al.  Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.

[19]  M. Guida,et al.  Is immunohistochemistry of BRAF V600E useful as a screening tool and during progression disease of melanoma patients? , 2016, BMC Cancer.

[20]  H. Romanowicz,et al.  Intraoperative diagnosis of sentinel lymph node metastases in breast cancer treatment with one-step nucleic acid amplification assay (OSNA) , 2016, Archives of medical science : AMS.

[21]  A Tateo,et al.  Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool , 2015, Physics in medicine and biology.

[22]  A. Jemal,et al.  Cancer treatment and survivorship statistics, 2014 , 2014, CA: a cancer journal for clinicians.

[23]  D. Quail,et al.  Microenvironmental regulation of tumor progression and metastasis , 2014 .

[24]  W. Moon,et al.  Association of Tumour Stiffness on Sonoelastography with Axillary Nodal Status in T1 Breast Carcinoma Patients , 2013, European Radiology.

[25]  B. Newman,et al.  Incidence of unilateral arm lymphoedema after breast cancer: a systematic review and meta-analysis. , 2013, The Lancet. Oncology.

[26]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[27]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[28]  Benoit M. Macq,et al.  Segmentation using a region-growing thresholding , 2005, IS&T/SPIE Electronic Imaging.

[29]  Patrick Pérez,et al.  Region filling and object removal by exemplar-based image inpainting , 2004, IEEE Transactions on Image Processing.

[30]  Gavin C. Cawley,et al.  Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers , 2003, Pattern Recognit..

[31]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[32]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[33]  R. Massafra,et al.  Advancement study of CancerMath model as prognostic tools for predicting Sentinel lymph node metastasis in clinically negative T1 breast cancer patients. , 2021, Journal of B.U.ON. : official journal of the Balkan Union of Oncology.

[34]  R. Massafra,et al.  Decision support systems for the prediction of lymph node involvement in early breast cancer. , 2021, Journal of B.U.ON. : official journal of the Balkan Union of Oncology.

[35]  Deepak Kulhare,et al.  Image Processing (IP) Through Erosion and Dilation Methods , 2013 .

[36]  Bernard Fertil,et al.  Texture indexes and gray level size zone matrix. Application to cell nuclei classification , 2009 .

[37]  C. Waltz.  Validation study. , 1988, NLN publications.

[38]  A. F. Adams,et al.  The Survey , 2021, Dyslexia in Higher Education.