Identifying in vivo DCE MRI parameters correlated with ex vivo quantitative microvessel architecture: A radiohistomorphometric approach
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Anant Madabhushi | Mark Rosen | Michael Feldman | John E. Tomaszeweski | Rachel Sparks | Mirabela Rusu | Natalie Shih | Asha Singanamalli | Amy Ziober | A. Madabhushi | R. Sparks | M. Feldman | M. Rosen | M. Rusu | A. Ziober | J. Tomaszeweski | N. Shih | A. Singanamalli
[1] W. I. Tseng,et al. Washout gradient in dynamic contrast‐enhanced MRI is associated with tumor aggressiveness of prostate cancer , 2012, Journal of magnetic resonance imaging : JMRI.
[2] D. Hanahan,et al. The Hallmarks of Cancer , 2000, Cell.
[3] John V Frangioni,et al. New technologies for human cancer imaging. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[4] George Lee,et al. Computer-aided prognosis: Predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data , 2011, Comput. Medical Imaging Graph..
[5] A. Madabhushi,et al. Histopathological Image Analysis: A Review , 2009, IEEE Reviews in Biomedical Engineering.
[6] D J Collins,et al. Evaluation of response to treatment using DCE-MRI: the relationship between initial area under the gadolinium curve (IAUGC) and quantitative pharmacokinetic analysis , 2006, Physics in medicine and biology.
[7] Min-Ying Lydia Su. Analyzing DCE-MRI data , 2008 .
[8] K. Lee,et al. Cancer Research Campaign , 1991 .
[9] H. Hricak,et al. Correlation of MR imaging and MR spectroscopic imaging findings with Ki-67, phospho-Akt, and androgen receptor expression in prostate cancer. , 2009, Radiology.
[10] Fabio Grizzi,et al. Microvessel density estimate: friend or foe in the light of prostate vascular system complexity? , 2010, World Journal of Urology.
[11] Fred L. Bookstein,et al. Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..
[12] Henkjan J Huisman,et al. Discrimination of prostate cancer from normal peripheral zone and central gland tissue by using dynamic contrast-enhanced MR imaging. , 2003, Radiology.
[13] D. Gleason,et al. Histologic grading of prostate cancer: a perspective. , 1992, Human pathology.
[14] Gordon Whiteley,et al. Bringing diagnostic technologies to the clinical laboratory: Rigor, regulation, and reality , 2008, Proteomics. Clinical applications.
[15] R Novario,et al. Microvessel density in prostate carcinoma , 2002, Prostate Cancer and Prostatic Diseases.
[16] Jorge S Reis-Filho,et al. Molecular profiling currently offers no more than tumour morphology and basic immunohistochemistry , 2010, Breast Cancer Research.
[17] Anant Madabhushi,et al. HistoStitcher©: An interactive program for accurate and rapid reconstruction of digitized whole histological sections from tissue fragments , 2011, Comput. Medical Imaging Graph..
[18] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[19] J. Epstein,et al. Interobserver reproducibility of Gleason grading of prostatic carcinoma: general pathologist. , 2001, Human pathology.
[20] R. Lenkinski,et al. Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2‐weighted MR imagery , 2012, Journal of magnetic resonance imaging : JMRI.
[21] Y. Collan,et al. Prognostic factors in prostate cancer , 2006, Diagnostic pathology.
[22] Alan Jackson,et al. Dynamic contrast-enhanced magnetic resonance imaging in oncology , 2005 .
[23] Gyan Bhanot,et al. Computerized Image-Based Detection and Grading of Lymphocytic Infiltration in HER2+ Breast Cancer Histopathology , 2010, IEEE Transactions on Biomedical Engineering.
[24] Anant Madabhushi,et al. Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer , 2012, BMC Bioinformatics.
[25] Anant Madabhushi,et al. Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS , 2013, Medical Image Anal..
[26] Andrew Janowczyk,et al. High-Throughput Biomarker Segmentation on Ovarian Cancer Tissue Microarrays via Hierarchical Normalized Cuts , 2012, IEEE Transactions on Biomedical Engineering.
[27] B. Nicolas Bloch,et al. An illustration of the potential for mapping MRI/MRS parameters with genetic over-expression profiles in human prostate cancer , 2008, Magnetic Resonance Materials in Physics, Biology and Medicine.
[28] R. Shah,et al. Current perspectives on the Gleason grading of prostate cancer. , 2009, Archives of pathology & laboratory medicine.
[29] Peter L Choyke,et al. Imaging prostate cancer: a multidisciplinary perspective. , 2007, Radiology.
[30] Anant Madabhushi,et al. Multi-field-of-view strategy for image-based outcome prediction of multi-parametric estrogen receptor-positive breast cancer histopathology: Comparison to Oncotype DX , 2011, Journal of pathology informatics.
[31] Andre Dekker,et al. Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.
[32] Aytekin Oto,et al. Microvessel density is not increased in prostate cancer: digital imaging of routine sections and tissue microarrays. , 2013, Human pathology.
[33] P Tiwari,et al. Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection , 2012, NMR in biomedicine.
[34] Philippe Puech,et al. MRI and surveillance , 2012, Current opinion in urology.
[35] M. Giger,et al. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. , 2006, Medical physics.
[36] A. Oto,et al. Diffusion-weighted and dynamic contrast-enhanced MRI of prostate cancer: correlation of quantitative MR parameters with Gleason score and tumor angiogenesis. , 2011, AJR. American journal of roentgenology.
[37] Olivier Gevaert,et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. , 2012, Radiology.
[38] Howard Y. Chang,et al. Decoding global gene expression programs in liver cancer by noninvasive imaging , 2007, Nature Biotechnology.
[39] L. Klotz,et al. Cancer overdiagnosis and overtreatment , 2012, Current opinion in urology.