Early detection of chemotherapy-refractory patients by monitoring textural alterations in diffuse optical spectroscopic images.
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Ali Sadeghi-Naini | Gregory J Czarnota | Martin J Yaffe | Sonal Gandhi | Lee Chin | Omar Falou | M. Yaffe | W. Tran | O. Falou | F. Wright | G. Czarnota | A. Sadeghi-Naini | S. Gandhi | L. Chin | Frances C Wright | William T Tran | Eric Vorauer | E. Vorauer
[1] G. Hortobagyi,et al. Comprehensive management of locally advanced breast cancer , 1990, Cancer.
[2] Peter Gibbs,et al. Texture analysis in assessment and prediction of chemotherapy response in breast cancer , 2013, Journal of magnetic resonance imaging : JMRI.
[3] Jie Li,et al. DW-MRI ADC values can predict treatment response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy , 2011, Medical Oncology.
[4] 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.
[5] Shan Tan,et al. Spatial-temporal [¹⁸F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy. , 2013, International journal of radiation oncology, biology, physics.
[6] Martin J. Yaffe,et al. Imaging innovations for cancer therapy response monitoring , 2012 .
[7] M. Christian,et al. [New guidelines to evaluate the response to treatment in solid tumors]. , 2000, Bulletin du cancer.
[8] A. Darzi,et al. Diffuse optical imaging of the healthy and diseased breast: A systematic review , 2008, Breast Cancer Research and Treatment.
[9] Sarah E Bohndiek,et al. Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment , 2014, Magnetic resonance in medicine.
[10] G. Hortobagyi,et al. Outcome after pathologic complete eradication of cytologically proven breast cancer axillary node metastases following primary chemotherapy. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[11] X. Intes. Time-domain optical mammography SoftScan: initial results. , 2005, Academic radiology.
[12] Richard Su,et al. Feasibility of optoacoustic visualization of high-intensity focused ultrasound-induced thermal lesions in live tissue. , 2010, Journal of biomedical optics.
[13] M. Yaffe,et al. Functional Imaging Using Diffuse Optical Spectroscopy of Neoadjuvant Chemotherapy Response in Women with Locally Advanced Breast Cancer , 2010, Clinical Cancer Research.
[14] Isabelle Thomassin,et al. Diffusion-weighted MRI in pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer , 2013, European Radiology.
[15] G. Hortobagyi,et al. Clinical course of breast cancer patients with complete pathologic primary tumor and axillary lymph node response to doxorubicin-based neoadjuvant chemotherapy. , 1999, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[16] T. Powles,et al. Good clinical response of breast cancers to neoadjuvant chemoendocrine therapy is associated with improved overall survival. , 2005, Annals of oncology : official journal of the European Society for Medical Oncology.
[17] Michael C. Kolios,et al. Conventional frequency ultrasonic biomarkers of cancer treatment response in vivo. , 2013, Translational oncology.
[18] J. Gralow,et al. Neoadjuvant chemotherapy for locally advanced breast cancer. , 2009, Seminars in radiation oncology.
[19] Anna L. Brown,et al. Effect of preoperative chemotherapy on the outcome of women with operable breast cancer. , 1998, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[20] G. Hortobagyi,et al. Locally advanced breast cancer. , 1999, Hematology/oncology clinics of North America.
[21] Carsten Denkert,et al. Response-guided neoadjuvant chemotherapy for breast cancer. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[22] B. Tromberg,et al. Imaging in breast cancer: Diffuse optics in breast cancer: detecting tumors in pre-menopausal women and monitoring neoadjuvant chemotherapy , 2005, Breast Cancer Research.
[23] Kevin M Brindle,et al. Imaging tumour cell metabolism using hyperpolarized 13C magnetic resonance spectroscopy. , 2010, Biochemical Society transactions.
[24] M. Giger,et al. Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images , 2007, Magnetic resonance in medicine.
[25] Xia Li,et al. Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. , 2014, Translational oncology.
[26] H. Sagili,et al. Study of tumour cellularity in locally advanced breast carcinoma on neo-adjuvant chemotherapy. , 2014, Journal of clinical and diagnostic research : JCDR.
[27] K. Miles,et al. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. , 2012, Clinical radiology.
[28] Michael E Phelps,et al. Positron emission tomography scanning: current and future applications. , 2002, Annual review of medicine.
[29] Hany Soliman,et al. Diffuse optical spectroscopy evaluation of treatment response in women with locally advanced breast cancer receiving neoadjuvant chemotherapy. , 2012, Translational oncology.
[30] 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.
[31] P. Fumoleau,et al. [18F]FDG-PET predicts complete pathological response of breast cancer to neoadjuvant chemotherapy , 2007, European Journal of Nuclear Medicine and Molecular Imaging.
[32] Omar Falou,et al. Quantitative ultrasound visualization of cell death: Emerging clinical applications for detection of cancer treatment response , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[33] Hon J. Yu,et al. Predicting pathologic response to neoadjuvant chemotherapy in breast cancer by using MR imaging and quantitative 1H MR spectroscopy. , 2009, Radiology.
[34] Salim Djeziri,et al. Optical tomography as adjunct to x-ray mammography: methods and results , 2007, SPIE BiOS.
[35] W. Han,et al. Early metabolic response using FDG PET/CT and molecular phenotypes of breast cancer treated with neoadjuvant chemotherapy , 2011, BMC Cancer.
[36] J. Bradley,et al. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[37] S. Giordano,et al. Update on locally advanced breast cancer. , 2003, The oncologist.
[38] Chih-Kuang Yeh,et al. Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and Nakagami-parameter images. , 2011, Medical physics.
[39] Martin J. Yaffe,et al. Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture , 2014, Oncotarget.
[40] Omar Falou,et al. Quantitative ultrasound spectral parametric maps: Early surrogates of cancer treatment response , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[41] E. Wolin,et al. Texture analysis of medical images in radiotherapy , 2016 .
[42] Q. Chu,et al. Neoadjuvant Chemotherapy in Stage III Breast Cancer , 2005, The American surgeon.
[43] A. Nishioka,et al. Early prediction of response to neoadjuvant chemotherapy in patients with breast cancer using diffusion-weighted imaging and gray-scale ultrasonography , 2014, Oncology reports.
[44] G. Hortobagyi,et al. Prognostic value of pathologic complete response after primary chemotherapy in relation to hormone receptor status and other factors. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[45] B. Tromberg,et al. Diffuse optical monitoring of blood flow and oxygenation in human breast cancer during early stages of neoadjuvant chemotherapy. , 2007, Journal of biomedical optics.
[46] G. Hortobagyi,et al. Pathological assessment of response to induction chemotherapy in breast cancer. , 1986, Cancer research.
[47] M. Tozaki,et al. Predicting pathological response to neoadjuvant chemotherapy in breast cancer with quantitative 1H MR spectroscopy using the external standard method , 2010, Journal of magnetic resonance imaging : JMRI.
[48] Ali Sadeghi-Naini,et al. Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties. , 2014, Medical physics.
[49] Michael E. Phelps,et al. Usefulness of 3′-[F-18]Fluoro-3′-deoxythymidine with Positron Emission Tomography in Predicting Breast Cancer Response to Therapy , 2005, Molecular Imaging and Biology.
[50] Kevin Brindle,et al. New approaches for imaging tumour responses to treatment , 2008, Nature Reviews Cancer.
[51] M. Helvie,et al. Locally advanced breast carcinoma: accuracy of mammography versus clinical examination in the prediction of residual disease after chemotherapy. , 1996, Radiology.
[52] B. Tromberg,et al. Optical imaging of breast cancer oxyhemoglobin flare correlates with neoadjuvant chemotherapy response one day after starting treatment , 2011, Proceedings of the National Academy of Sciences.
[53] R. M. Haralick,et al. Textural features for image classification. IEEE Transaction on Systems, Man, and Cybernetics , 1973 .
[54] Ming-Ting Wu,et al. Monitoring breast cancer response to neoadjuvant systemic chemotherapy using parametric contrast-enhanced MRI: a pilot study. , 2007, Academic radiology.
[55] F. Gallagher,et al. Detecting treatment response in a model of human breast adenocarcinoma using hyperpolarised [1-13C]pyruvate and [1,4-13C2]fumarate , 2010, British Journal of Cancer.
[56] Michael C. Kolios,et al. Low-frequency quantitative ultrasound imaging of cell death in vivo. , 2013, Medical Physics (Lancaster).
[57] Ali Sadeghi-Naini,et al. Evaluation of neoadjuvant chemotherapy response in women with locally advanced breast cancer using ultrasound elastography. , 2013, Translational oncology.
[58] M J Yaffe,et al. Whole‐specimen histopathology: a method to produce whole‐mount breast serial sections for 3‐D digital histopathology imaging , 2007, Histopathology.
[59] M. van Glabbeke,et al. New guidelines to evaluate the response to treatment in solid tumors , 2000, Journal of the National Cancer Institute.
[60] M. Hatt,et al. Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer , 2011, The Journal of Nuclear Medicine.
[61] David Hsiang,et al. Diffuse optical spectroscopy measurements of healing in breast tissue after core biopsy: case study. , 2009, Journal of biomedical optics.
[62] P. Flamen,et al. Heterogeneity of metabolic response to systemic therapy in metastatic breast cancer patients. , 2010, Clinical oncology (Royal College of Radiologists (Great Britain)).
[63] M. Campone,et al. FDG PET evaluation of early axillary lymph node response to neoadjuvant chemotherapy in stage II and III breast cancer patients , 2011, European Journal of Nuclear Medicine and Molecular Imaging.
[64] Brandon Whitcher,et al. DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6 , 2011, British Journal of Cancer.
[65] B. Tromberg,et al. Predicting response to breast cancer neoadjuvant chemotherapy using diffuse optical spectroscopy , 2007, Proceedings of the National Academy of Sciences.
[66] V. Goh,et al. Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. , 2011, Radiology.
[67] Vicky Goh,et al. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis , 2012, European Journal of Nuclear Medicine and Molecular Imaging.
[68] Jason B. Nikas,et al. Prognosis of Treatment Response (Pathological Complete Response) in Breast Cancer , 2012, Biomarker insights.
[69] D. Mankoff,et al. Monitoring the response of patients with locally advanced breast carcinoma to neoadjuvant chemotherapy using [technetium 99m]‐sestamibi scintimammography , 1999, Cancer.
[70] X. Intes. Time-Domain Optical Mammography SoftScan , 2005 .
[71] Michael C. Kolios,et al. Quantitative Ultrasound Evaluation of Tumor Cell Death Response in Locally Advanced Breast Cancer Patients Receiving Chemotherapy , 2013, Clinical Cancer Research.
[72] Xinmai Yang,et al. Real-time monitoring of high-intensity focused ultrasound ablations with photoacoustic technique: an in vitro study. , 2011, Medical physics.
[73] Ewert Bengtsson,et al. 3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading , 2014, Comput. Math. Methods Medicine.
[74] R. Sen,et al. Histopathologic changes following neoadjuvant chemotherapy in locally advanced breast cancer. , 2013, Indian journal of cancer.
[75] B. Tromberg,et al. Sources of absorption and scattering contrast for near-infrared optical mammography. , 2001, Academic radiology.
[76] F E Turkheimer,et al. Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography , 2013, Physics in medicine and biology.