MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer
暂无分享,去创建一个
Michael C. Kolios | G. Stanisz | A. Sahgal | F. Wright | M. Trudeau | G. Czarnota | A. Sadeghi-Naini | S. Gandhi | B. Curpen | L. Sannachi | D. Dicenzo | A. Dasgupta | H. Suraweera | Christopher Kolios | Nicole Look-Hong | D. DiCenzo
[1] W. Tran,et al. Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer , 2020, Oncotarget.
[2] Michael C. Kolios,et al. Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results , 2020, PloS one.
[3] G. Czarnota,et al. A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning , 2020, Scientific Reports.
[4] Michael C. Kolios,et al. Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi‐institutional study , 2020, Cancer medicine.
[5] Jie Tian,et al. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges , 2019, Theranostics.
[6] T. Helbich,et al. Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients , 2019, Investigative radiology.
[7] Vivek Verma,et al. Response rates and pathologic complete response by breast cancer molecular subtype following neoadjuvant chemotherapy , 2018, Breast Cancer Research and Treatment.
[8] S. Steinberg,et al. Long-term outcomes for neoadjuvant versus adjuvant chemotherapy in early breast cancer: meta-analysis of individual patient data from ten randomised trials , 2018, The Lancet. Oncology.
[9] P. Lambin,et al. Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.
[10] R. Lerski,et al. Interim heterogeneity changes measured using entropy texture features on T2-weighted MRI at 3.0 T are associated with pathological response to neoadjuvant chemotherapy in primary breast cancer , 2017, European Radiology.
[11] Mehrdad J. Gangeh,et al. A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound , 2017, Scientific Reports.
[12] S. Nam,et al. Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes. , 2017, Radiology.
[13] S. Han,et al. Takeaways from Pre-Contrast T1 and T2 Breast Magnetic Resonance Imaging in Women with Recently Diagnosed Breast Cancer , 2016, Iranian journal of radiology : a quarterly journal published by the Iranian Radiological Society.
[14] W. Peng,et al. Changes of T2 Relaxation Time From Neoadjuvant Chemotherapy in Breast Cancer Lesions , 2016, Iranian journal of radiology : a quarterly journal published by the Iranian Radiological Society.
[15] Eun Sook Ko,et al. Assessment of Invasive Breast Cancer Heterogeneity Using Whole-Tumor Magnetic Resonance Imaging Texture Analysis , 2016, Medicine.
[16] 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.
[17] N. Michoux,et al. Texture analysis on MR images helps predicting non-response to NAC in breast cancer , 2015, BMC Cancer.
[18] 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.
[19] Peter Gibbs,et al. Texture analysis in assessment and prediction of chemotherapy response in breast cancer , 2013, Journal of magnetic resonance imaging : JMRI.
[20] A. Tutt,et al. Recommendations from an International Consensus Conference on the Current Status and Future of Neoadjuvant Systemic Therapy in Primary Breast Cancer , 2012, Annals of Surgical Oncology.
[21] Andrzej Materka,et al. Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: an application-oriented study. , 2009, Medical physics.
[22] M. Giger,et al. Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images , 2007, Magnetic resonance in medicine.
[23] L. Turnbull,et al. Neoadjuvant chemotherapy in breast cancer: early response prediction with quantitative MR imaging and spectroscopy , 2006, British Journal of Cancer.
[24] C. Meyers,et al. Side-effects of chemotherapy and quality of life in ovarian and breast cancer patients , 2006, Current opinion in obstetrics & gynecology.
[25] S. Giordano,et al. Update on locally advanced breast cancer. , 2003, The oncologist.
[26] E Grabbe,et al. Breast carcinoma: effect of preoperative contrast-enhanced MR imaging on the therapeutic approach. , 1999, Radiology.
[27] B. Scholkopf,et al. Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).
[28] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[29] Dimitri Van De Ville,et al. Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities , 2014, Medical Image Anal..
[30] V. Goh,et al. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. , 2013, Radiology.
[31] Anil K. Jain,et al. 39 Dimensionality and sample size considerations in pattern recognition practice , 1982, Classification, Pattern Recognition and Reduction of Dimensionality.