Biomarkers and Imaging of Breast Cancer.
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[1] Matthew Ingham,et al. Cell-Cycle Therapeutics Come of Age. , 2017, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[2] 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.
[3] Lihua Li,et al. Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer , 2017, PloS one.
[4] Stuart A. Taylor,et al. Imaging biomarker roadmap for cancer studies , 2016, Nature Reviews Clinical Oncology.
[5] A. Verbeek,et al. Breast cancer screening effect across breast density strata: A case–control study , 2017, International journal of cancer.
[6] A. Bardia,et al. Neoadjuvant Endocrine Therapy for Estrogen Receptor-Positive Breast Cancer: A Systematic Review and Meta-analysis. , 2016, JAMA oncology.
[7] S. Kurozumi,et al. Clinical Significance of 18F-FDG-PET in Invasive Lobular Carcinoma. , 2016, Anticancer research.
[8] Hee Jung Shin,et al. Tumor apparent diffusion coefficient as an imaging biomarker to predict tumor aggressiveness in patients with estrogen‐receptor‐positive breast cancer , 2016, NMR in biomedicine.
[9] T. Fujii,et al. Clinicopathological Features of Cases with Primary Breast Cancer not Identified by 18F-FDG-PET. , 2016, Anticancer research.
[10] Lars J. Grimm,et al. Breast MRI radiogenomics: Current status and research implications , 2016, Journal of magnetic resonance imaging : JMRI.
[11] Erich P Huang,et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set , 2016, npj Breast Cancer.
[12] Erich P Huang,et al. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. , 2016, Radiology.
[13] R. Mangano,et al. Phase 2 multicentre trial investigating intermittent and continuous dosing schedules of the poly(ADP-ribose) polymerase inhibitor rucaparib in germline BRCA mutation carriers with advanced ovarian and breast cancer , 2016, British Journal of Cancer.
[14] Anant Madabhushi,et al. A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores , 2016, Scientific Reports.
[15] W. Weber,et al. Molecular Imaging of Biomarkers in Breast Cancer , 2016, The Journal of Nuclear Medicine.
[16] Paul Kinahan,et al. Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.
[17] I. Nabipour,et al. Precision medicine and molecular imaging: new targeted approaches toward cancer therapeutic and diagnosis. , 2016, American journal of nuclear medicine and molecular imaging.
[18] Hiroko Yamashita,et al. Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study , 2015, PloS one.
[19] Joseph O. Deasy,et al. Breast cancer subtype intertumor heterogeneity: MRI‐based features predict results of a genomic assay , 2015, Journal of magnetic resonance imaging : JMRI.
[20] J. Thrall. Moreton Lecture: Imaging in the Age of Precision Medicine. , 2015, Journal of the American College of Radiology : JACR.
[21] Erich P Huang,et al. Metrology Standards for Quantitative Imaging Biomarkers. , 2015, Radiology.
[22] J. Loscalzo,et al. Systems medicine: evolution of systems biology from bench to bedside , 2015, Wiley interdisciplinary reviews. Systems biology and medicine.
[23] Thomas E Yankeelov,et al. Multiparametric Magnetic Resonance Imaging for Predicting Pathological Response After the First Cycle of Neoadjuvant Chemotherapy in Breast Cancer , 2015, Investigative radiology.
[24] Karen Drukker,et al. Using quantitative image analysis to classify axillary lymph nodes on breast MRI: a new application for the Z 0011 Era. , 2015, European journal of radiology.
[25] Neema Jamshidi,et al. Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis. , 2015, Radiology.
[26] T. Helbich,et al. Quantitative Apparent Diffusion Coefficient as a Noninvasive Imaging Biomarker for the Differentiation of Invasive Breast Cancer and Ductal Carcinoma In Situ , 2015, Investigative radiology.
[27] P. Fumoleau,et al. Role of positron emission tomography for the monitoring of response to therapy in breast cancer. , 2015, The oncologist.
[28] Breast imaging reporting and data system (BI-RADS) lexicon for breast MRI: interobserver variability in the description and assignment of BI-RADS category. , 2015, European journal of radiology.
[29] Katherine Karakasis,et al. Outcomes and endpoints in trials of cancer treatment: the past, present, and future. , 2015, The Lancet. Oncology.
[30] Thomas E Yankeelov,et al. Methods and challenges in quantitative imaging biomarker development. , 2015, Academic radiology.
[31] H. Y. Kim,et al. Evaluation of Malignancy Risk Stratification of Microcalcifications Detected on Mammography: A Study Based on the 5th Edition of BI-RADS , 2015, Annals of Surgical Oncology.
[32] K. Lukong,et al. Signaling pathways in breast cancer: therapeutic targeting of the microenvironment. , 2014, Cellular signalling.
[33] Kenneth G. A. Gilhuijs,et al. Association between rim enhancement of breast cancer on dynamic contrast-enhanced MRI and patient outcome: impact of subtype , 2014, Breast Cancer Research and Treatment.
[34] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[35] Shigehiko Kanaya,et al. Systems Biology in the Context of Big Data and Networks , 2014, BioMed research international.
[36] R. Stockley. Biomarkers in chronic obstructive pulmonary disease: confusing or useful? , 2014, International journal of chronic obstructive pulmonary disease.
[37] Martin J Yaffe,et al. Density and breast cancer risk. , 2013, Radiology.
[38] Yun-Hsuan Lee,et al. Total Tumor Volume Is a Better Marker of Tumor Burden in Hepatocellular Carcinoma Defined by the Milan Criteria , 2013, World Journal of Surgery.
[39] T. Fleming,et al. Biomarkers and surrogate endpoints in clinical trials , 2012, Statistics in medicine.
[40] Andre Dekker,et al. Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.
[41] Wendy B DeMartini,et al. BREAST IMAGING : Positive Predictive Value of BI-RADS MR Imaging , 2012 .
[42] Jae-Hun Kim,et al. Imaging-Based Tumor Treatment Response Evaluation: Review of Conventional, New, and Emerging Concepts , 2012, Korean journal of radiology.
[43] J. Aronson. Research priorities in biomarkers and surrogate end-points. , 2012, British journal of clinical pharmacology.
[44] Giuseppe Curigliano,et al. New drugs for breast cancer subtypes: targeting driver pathways to overcome resistance. , 2012, Cancer treatment reviews.
[45] Eun-Kyung Kim,et al. Triple-negative invasive breast cancer on dynamic contrast-enhanced and diffusion-weighted MR imaging: comparison with other breast cancer subtypes , 2012, European Radiology.
[46] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[47] Scoring System Based on BI-RADS Lexicon to Predict Probability of Malignancy in Suspicious Microcalcifications , 2012, Annals of Surgical Oncology.
[48] H. Hricak. Oncologic imaging: a guiding hand of personalized cancer care. , 2011, Radiology.
[49] K. Strimbu,et al. What are biomarkers? , 2010, Current opinion in HIV and AIDS.
[50] Christine M. Micheel,et al. Evaluation of Biomarkers and Surrogate Endpoints in Chronic Disease , 2010 .
[51] Anwar R. Padhani,et al. Diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for monitoring anticancer therapy , 2010, Targeted Oncology.
[52] M. J. van de Vijver,et al. A simple system for grading the response of breast cancer to neoadjuvant chemotherapy. , 2010, Annals of oncology : official journal of the European Society for Medical Oncology.
[53] E. Burnside,et al. The ACR BI-RADS experience: learning from history. , 2009, Journal of the American College of Radiology : JACR.
[54] R. Simon. Advances in clinical trial designs for predictive biomarker discovery and validation , 2009 .
[55] Jeanne Kowalski,et al. Assessment of Interobserver Reproducibility in Quantitative 18F-FDG PET and CT Measurements of Tumor Response to Therapy , 2009, Journal of Nuclear Medicine.
[56] A. Tutt,et al. Phase II trial of the oral PARP inhibitor olaparib in BRCA-deficient advanced breast cancer. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[57] T. Uematsu,et al. Triple-negative breast cancer: correlation between MR imaging and pathologic findings. , 2009, Radiology.
[58] L. Schwartz,et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). , 2009, European journal of cancer.
[59] Kathryn Trinkaus,et al. PET-based estradiol challenge as a predictive biomarker of response to endocrine therapy in women with estrogen-receptor-positive breast cancer , 2009, Breast Cancer Research and Treatment.
[60] Ruth M Pfeiffer,et al. A Model Free Approach to Combining Biomarkers , 2008, Biometrical journal. Biometrische Zeitschrift.
[61] R. Weissleder,et al. Imaging in the era of molecular oncology , 2008, Nature.
[62] Woo Kyung Moon,et al. Index terms: Breast neoplasms Breast, MR , 2006 .
[63] Lonie R. Salkowski,et al. Use of microcalcification descriptors in BI-RADS 4th edition to stratify risk of malignancy. , 2007, Radiology.
[64] Andreas Makris,et al. Inter‐ and intraobserver variability in the evaluation of dynamic breast cancer MRI , 2006, Journal of magnetic resonance imaging : JMRI.
[65] R. Castellino,et al. Computer aided detection (CAD): an overview , 2005, Cancer imaging : the official publication of the International Cancer Imaging Society.
[66] J. Aronson. Biomarkers and surrogate endpoints. , 2005, British journal of clinical pharmacology.
[67] J. Baker,et al. BI-RADS for sonography: positive and negative predictive values of sonographic features. , 2005, AJR. American journal of roentgenology.
[68] C. Wood,et al. Computer Aided Detection (CAD) for Breast MRI , 2005, Technology in cancer research & treatment.
[69] Mark J Ratain,et al. Phase II studies of modern drugs directed against new targets: if you are fazed, too, then resist RECIST. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[70] Jennifer A Harvey,et al. Quantitative assessment of mammographic breast density: relationship with breast cancer risk. , 2004, Radiology.
[71] James H Thrall,et al. Biomarkers in imaging: realizing radiology's future. , 2003, Radiology.
[72] P. Langenberg,et al. Breast Imaging Reporting and Data System: inter- and intraobserver variability in feature analysis and final assessment. , 2000, AJR. American journal of roentgenology.
[73] S. Orel,et al. BI-RADS categorization as a predictor of malignancy. , 1999, Radiology.
[74] L. Liberman,et al. The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categories. , 1998, AJR. American journal of roentgenology.
[75] D. Callahan. Managed Care and the Goals of Medicine , 1998, Journal of the American Geriatrics Society.
[76] P. Hartge,et al. The risk of cancer associated with specific mutations of BRCA1 and BRCA2 among Ashkenazi Jews. , 1997, The New England journal of medicine.
[77] D. Owens,et al. Assessment of Diagnostic Technology in Health Care , 1989 .