Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI

[1]  A. Sgura,et al.  Quantitative relationships between acentric fragments and micronuclei: new models and implications for curve fitting , 2020, International journal of radiation biology.

[2]  Quazi Abidur Rahman,et al.  Interpretability and Class Imbalance in Prediction Models for Pain Volatility in Manage My Pain App Users: Analysis Using Feature Selection and Majority Voting Methods , 2019, JMIR medical informatics.

[3]  E. McDermott,et al.  Use of contrast-enhanced Magnetic Resonance Imaging (MRI) to predict pathological response after trastuzumab (T) – based neoadjuvant chemotherapy (NAC) for HER2-positive breast cancer (HER2BrCa) , 2018 .

[4]  Adrien Depeursinge,et al.  Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging , 2017, Journal of medical imaging.

[5]  Caroline Reinhold,et al.  Features from Computerized Texture Analysis of Breast Cancers at Pretreatment MR Imaging Are Associated with Response to Neoadjuvant Chemotherapy. , 2017, Radiology.

[6]  Lihua Li,et al.  Radiomic analysis of DCE-MRI for prediction of response to neoadjuvant chemotherapy in breast cancer patients. , 2017, European journal of radiology.

[7]  D. Rubin,et al.  Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer. , 2017, Radiology.

[8]  G. Santamaría,et al.  Neoadjuvant Systemic Therapy in Breast Cancer: Association of Contrast-enhanced MR Imaging Findings, Diffusion-weighted Imaging Findings, and Tumor Subtype with Tumor Response. , 2017, Radiology.

[9]  A. Madabhushi,et al.  Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI , 2017, Breast Cancer Research.

[10]  N. Harbeck,et al.  Breast cancer , 2017, The Lancet.

[11]  Wei Huang,et al.  DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response , 2017, Tomography.

[12]  Eun Sook Ko,et al.  Breast cancer heterogeneity: MR Imaging Texture Analysis and Survival Outcomes1 , 2016 .

[13]  Ruijiang Li,et al.  Intratumor partitioning and texture analysis of dynamic contrast‐enhanced (DCE)‐MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy , 2016, Journal of magnetic resonance imaging : JMRI.

[14]  Peter Gibbs,et al.  Pretreatment Prognostic Value of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Vascular, Texture, Shape, and Size Parameters Compared With Traditional Survival Indicators Obtained From Locally Advanced Breast Cancer Patients , 2016, Investigative radiology.

[15]  Virginia G Kaklamani,et al.  Prospective Validation of a 21-Gene Expression Assay in Breast Cancer. , 2015, The New England journal of medicine.

[16]  Elena Provenzano,et al.  Neoadjuvant trials in early breast cancer: pathological response at surgery and correlation to longer term outcomes – what does it all mean? , 2015, BMC Medicine.

[17]  Steinar Lundgren,et al.  Dynamic contrast‐enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer , 2014, NMR in biomedicine.

[18]  Hiroshi Fujita,et al.  Automated Detection of Architectural Distortion Using Improved Adaptive Gabor Filter , 2014, Digital Mammography / IWDM.

[19]  F. Cabanillas,et al.  Abstract P3-14-17: Results of a novel neoadjuvant chemotherapy (NAC) for breast cancer , 2013 .

[20]  Daniel L. Rubin,et al.  Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[21]  Yusuke Kajiwara,et al.  LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data , 2013, BioData Mining.

[22]  Rangaraj M. Rangayyan,et al.  Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms , 2013, International Journal of Computer Assisted Radiology and Surgery.

[23]  Daniel F Hayes,et al.  Defining the benefits of neoadjuvant chemotherapy for breast cancer. , 2012, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[24]  P. Fasching,et al.  Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. , 2012, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[25]  Siliang Ma,et al.  Nonlinear filtering based on 3D wavelet transform for MRI denoising , 2012, EURASIP Journal on Advances in Signal Processing.

[26]  L. Esserman,et al.  MRI measurements of breast tumor volume predict response to neoadjuvant chemotherapy and recurrence-free survival. , 2005, AJR. American journal of roentgenology.

[27]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[28]  Pranab Kumar Dutta,et al.  Efficient automated detection of mitotic cells from breast histological images using deep convolution neutral network with wavelet decomposed patches , 2019, Comput. Biol. Medicine.

[29]  S. Z. Mohd Hashim,et al.  Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis , 2019, Comput. Biol. Medicine.

[30]  A. Jemal,et al.  Cancer statistics, 2016 , 2016, CA: a cancer journal for clinicians.

[31]  Dimitri Van De Ville,et al.  Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities , 2014, Medical Image Anal..

[32]  H. Iwase,et al.  [Breast cancer]. , 2006, Nihon rinsho. Japanese journal of clinical medicine.

[33]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[34]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[35]  N. Dubrawsky.  Cancer statistics , 1989, CA: a cancer journal for clinicians.