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  1. Liu TT, Achrol AS, Mitchell LA, Rodriguez SA, Feroze A, Iv M, Kim C, Chaudhary N, Gevaert O, Stuart JM, Harsh GR, Chang SD, Rubin DL. Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment. Neuro-Oncology. 2016:1-11. doi: 10.1093/neuonc/now270

  2. Schrock M, Batar B, Lee J, Druck T, Ferguson B, Cho J, Akakpo K, Hagrass H, Heerema N, Xia F. Wwox–Brca1 interaction: role in DNA repair pathway choice. Oncogene. 2016:1-13. DOI: 10.1038/onc.2016.389.

  3. Song SE, Bae MS, Chang JM, Cho N, Ryu HS, Moon WK. MR and mammographic imaging features of HER2-positive breast cancers according to hormone receptor status: a retrospective comparative study. Acta Radiologica. 2016:0284185116673119.

  4. McCann SM, Jiang Y, Fan X, Wang J, et al. Quantitative Multiparametric MRI Features and PTEN Expression of Peripheral Zone Prostate Cancer: A Pilot Study. AJR Am J Roentgenol (2016). 206(3):559-565 (link)

  5. Katrib A, Hsu W, Bui A, Xing Y. “Radiotranscriptomics”: A synergy of imaging and transcriptomics in clinical assessment. Quantitative Biology. 2016:1-12. (link)  

  6. Bai HX, Lee AM, Yang L, Zhang P, Davatzikos C, Maris JM, Diskin SJ. Imaging genomics in cancer research: limitations and promises. The British Journal of Radiology. 2016:20151030. doi:10.1259/bjr.20151030
  7. Zhu, Y., H. Li, et al. (2015). TU-CD-BRB-06: Deciphering Genomic Underpinnings of Quantitative MRI-Based Radiomic Phenotypes of Invasive Breast Carcinoma. Medical physics 42(6): 3603-3603.

  8. Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn). 2015;19(1A):A68-A77.

  9. Shinegare AB, Vikram R, Jaffe C, et al. Radiogenomics of clear renal cell carcinoma: Preliminary Findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group. Abdominal Imaging (2015). 40(6)1684-1692. (link)
  10. Pope WB. Genomics of Brain Tumor Imaging. Neuroimaging Clinics of North America. 2015;25(1):105-19.

  11. Gutman, D. A., W. D. Dunn Jr, et al. (2015). Somatic mutations associated with MRI-derived volumetric features in glioblastoma. Neuroradiology: 1-11.
  12. Feldman, M., M. G. Piazza, et al. (2015). 137 Somatostatin Receptor Expression on VHL-Associated Hemangioblastomas Offers Novel Therapeutic Target. Neurosurgery 62: 209-210.

  13. Colen R, Foster I, Gatenby R, Giger ME, Gillies R, Gutman D, Heller M, Jain R, Madabhushi A, Madhavan S, Napel S, Rao A, Saltz J, Tatum J, Verhaak R, Whitman G. NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures. Translational Oncology. 2014;7(5):556-69. doi: 10.1016/j.tranon.2014.07.007.
  14. Rao A. Exploring relationships between multivariate radiological phenotypes and genetic features: A case-study in Glioblastoma using the Cancer Genome Atlas, Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE.
  15. Gevaert O, Xu J, Hoang CD, Leung AN, Xu Y, Quon A, Rubin DL, Napel S, Plevritis SK. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. Radiology. 2012;264(2):387-96. Epub 2012/06/23.  (link)

Radiomics

  1. D Paredes, A Saha, MA Mazurowski. Deep learning for segmentation of brain tumors: can we train with images from different institutions? Proc. SPIE Medical Imaging: Computer-Aided Diagnosis (2017).International Society for Optics and Photonics. doi: 10.1117/12.2255696
  2. Shijin Kumar PS, Dharun VS. Combination of fuzzy c-means clustering and texture pattern matrix for brain MRI segmentation. Biomedical Research 2017;28(5) (link)
  3. Nabizadeh N, Kubat M. Automatic Tumor Segmentation in Single-Spectral MRI Using A Texture-Based and Contour-Based Algorithm.doi: 10.1016/j.eswa.2017.01.036
  4. Kaur T, Saini BS, Gupta S. A joint intensity and edge magnitude-based multilevel thresholding algorithm for the automatic segmentation of pathological MR brain images. Neural Computing and Applications. 2016:1-24. doi: 10.1007/s00521-016-2751-4

  5. Song J, Liu Z, Zhong W, Huang Y, Ma Z, Dong D, Liang C, Tian J. Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis. Scientific reports. 2016;6:38282:1-9. doi: 10.1038/srep38282

  6. Crawford L, Monod A, Chen AX, Mukherjee S, Rabadán R. Topological Summaries of Tumor Images Improve Prediction of Disease Free Survival in Glioblastoma Multiforme. arXiv preprint arXiv:161106818. 2016:1-29.

  7. Korfiatis P, Kline TL, Erickson BJ. Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning. J Tomography, 2016, 2:4(334-340) DOI: 10.18383/j.tom.2016.00166
  8. Zheng C, Wang X, Feng D, editors. Topology guided demons registration with local rigidity preservation. Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference; 2016: IEEE.

  9. Kotrotsou A, Zinn PO, Colen RR. Radiomics in Brain Tumors: An Emerging Technique for Characterization of Tumor Environment. Magnetic Resonance Imaging Clinics of North America. 2016;24(4):719-29.

  10. Zhao B, Tan Y, Tsai WY, Qi J et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep. 2016 Mar 24;6:23428. (link)
  11. Li H, Zhu Y, Burnside ES, Huang E, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. npj Breast Cancer (2016). (link)
  12. Grossmann P, Gutman DA, et al. Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma. BMC Cancer (2016). (link)
  13. Zhu Y, Li H, Guo W, Drukker K, et al. Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma. Sci Rep (2015). (link) 
  14. Rajakumar K, Muttan S, Deepa G, Revathy S, Priya BS. Intelligent texture feature extraction and indexing for MRI image retrieval using curvelet and PCA with HTF. Advances in Natural and Applied Sciences. 2015 Jun 1;9(6 SE):506-13. (link)
  15. Parmar, C., R. T. Leijenaar, et al. (2015). "Radiomic feature clusters and Prognostic Signatures specific for Lung and Head &Neck cancer." Sci Rep 5: 11044.

  16.  Parmar, C., P. Grossmann, et al. (2015). "Machine Learning methods for Quantitative Radiomic Biomarkers." Sci Rep 5: 13087.
  17. Tanougast C, Chaddad A. High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. Adv Bioinformatics (2015). (link)
  18. Chaddad A. Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models.  International Journal of Biomedical Imaging, 2015. (link)
  19. Dhara AK, Mukhopadhyay S, Khandelwal N. 3d texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images. SPIE 2013. (link)
  20. Dhara AK, Mukhopadhyay S, Alam N, Khandelwal N. Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images. Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700K. (link)

...

  1. Aerts HJ, Velazquez ER, et al. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. TCIA. Saint Louis, MO. (link)
  2. Armato SG and Drukker K, et al. (2015). SPIE-AAPM-NCI Lung Nodule Classification Challenge Dataset. TCIA. Saint Louis, MO. (link)
  3. Bloch N, Rusu M, et al. (2015) NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures. TCIA. St. Louis, MO. (link)
  4. Colen RR, Wang J, et al. (2014). Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death. TCIA. Saint Louis, MO. (link)
  5. Gevaert O, Mitchell LA, et al. (2014). Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. TCIA. Saint Louis, MO. (link)

  6. Gevaert O, Xu J, et al. (2014). Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. TCIA. Saint Louis, MO. (link)
  7. Grove O, Berglund AE, et al. (2015). Data from: Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. TCIA. Saint Louis. MO. (link)
  8. Gutman DA, Cooper LA, et al. (2014). MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set. TCIA. Saint Louis, MO. (link)

  9. Huang W, Li X, et al. (2014). Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. TCIA. Saint Louis, MO. (link)

  10. Jain R, Poisson LM, et al. (2014). Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor. TCIA. Saint Louis, MO. (link)

  11. Kalpathy-Cramer J, Napel S, et al. (2015). QIN multi-site collection of Lung CT data with Nodule Segmentations. TCIA. Saint Louis, MO. (link)

  12. Lee J, Narang S, et al. (2015). Spatial Habitat Features derived from Multiparametric Magnetic Resonance Imaging data from Glioblastoma Multiforme cases. TCIA. Saint Louis, MO. (link)
  13. Liu F,  Hernandez-Cabronero M, et al. (2016). Image Data Used in the Simulations of "The Role of Image Compression Standards in Medical Imaging: Current Status and Future Trends". TCIA. Saint Louis, MO. (link 
  14. Mazurowski MA, Zhang J, et al. (2014). Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging. TCIA. Saint Louis, MO. (link)
  15. Messay T, Hardie RC, et al. (2014). Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset. TCIA. Saint Louis, MO. (link)

  16. Morris E, Burnside M, et al. (2014). TCGA Breast Phenotype Research Group Data sets. TCIA. Saint Louis, MO (link)
  17. Roth H, Lu L, et al. (2015). A new 2.5D representation for lymph node detection in CT. TCIA. Saint Louis, MO. (link)

  18. Shinagare AB, Vikram R, et al. (2015). Radiogenomics of Clear Cell Renal Cell Carcinoma: Preliminary Findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Research Group. TCIA. Saint Louis, MO. (link)

  19. Vallières M, Freeman CR, et al. (2015). Data from: A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. TCIA. Saint Louis, MO. (link)

QIN

  1. Farahani K, Kalpathy-Cramer J, Chenevert TL, Rubin DL, Sunderland JJ, Nordstrom RJ, Buatti J, Hylton N. Computational Challenges and Collaborative Projects Semmineh NB, Stokes AM, Bell LC, Boxerman JL, Quarles CC. A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. TOMOGRAPHY, 2017; 3(1)41-9. doi: 10.18383/j.tom.2016.00286
  2. Farahani K, Kalpathy-Cramer J, Chenevert TL, et al. Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network. Tomography: a journal for imaging research. , 2016;2(4):242-9. (link)

  3. Kalpathy-Cramer J, Mamomov A, Zhao B, Lu L, Cherezov D, Napel S, Echegaray S, Rubin D, McNitt-Gray M, Lo Pet al.. Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features. Tomography: a journal for
    imaging research. ,2016;2(4):430-7. doi: http: //dx.doi.org/ 10.18383/j.tom.2016.00235.

  4. ClarkeHuang, L. PW., RX. J. NordstromLi, et al. (2014). "The Quantitative Imaging Network: NCI's Historical Perspective and Planned Goals." Translational Oncology 7(1): 1-4. (link)Huang, W., X. Li, et al. (2014). "Variations of Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge." Transl Oncol 7(1): 153-166. (link)

  5. Kalpathy-Cramer , J., J. B. FreymannFreymann JB, Kirby JS, et al. (2014). " Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Algorithm Validation Leveraging The Cancer Imaging Archive." Translational oncology  Translational Oncology. 2014 Feb;7(1):147-152.Levy, M. A., J. B. Freymann, et al. (2012). "52. doi: 10.1593/tlo.13862. (link)

  6. Clarke LP, Nordstrom RJ, Zhang H, Tandon P, et al. The Quantitative Imaging Network: NCI’s Historical Perspective and Planned Goals Translational Oncology. 2014 Feb;7(1):1-4. doi: 10.1593/tlo.13832. (link)
  7. Levy MA, Freymann JB, Kirby JS, et al. Informatics methods to enable sharing of quantitative imaging research data." Magnetic  Magnetic Resonance Imaging. 2012 Nov;30(9):1249-56. doi: 10.1016/j.mri.2012.04.007. Epub 2012 Jul 6. (link)

Publications relating to specific data collections:

...

  1. Peskin AP, Dima AA, Saiprasad G. An Automated Method for Locating Phantom modules in Anthropomorphic Thoracic Phantom CT Studies. The 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition. 2012.(link)
  2. Gavrielides MA, Kinnard LM, Myers KJ ,Peregoy J, Pritchard WF, Zeng R, Esparza J, Karanian J, Petrick N, A resource for the assessment of lung nodule size estimation methods: database of thoracic CT scans of an anthropomorphic phantom, Optics Express , vol. 18, n.14, pp. 15244-15255, 2010. (link)

Collection: Quantitative Imaging Network (QIN)

  1. Kalpathy-Cramer J, Freymann JB, Kirby JS, et al. Quantitative Imaging Network: Data Sharing and Competitive Algorithm Validation Leveraging The Cancer Imaging Archive Translational Oncology. 2014 Feb;7(1):147-52. doi: 10.1593/tlo.13862. (link)
  2. Huang W, Li X, Chen Y, Li X, Chang MC, Oborski MJ, Malyarenko DI, Muzi M, Jajamovich GH, Fedorov A, Tudorica A, Gupta SN, Laymon CM, Marro KI, Dyvorne HA, Miller JV, Barbodiak DP, Chenevert TL, Yankeelov TE, Mountz JM, Kinahan PE, Kikinis R, Taouli B, Fennessy F, Kalpahthy-Cramer J. Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. Translational Oncology. 2014 Feb;7(1):153-66. (link)
  3. Clarke LP, Nordstrom RJ, Zhang H, Tandon P, et al. The Quantitative Imaging Network: NCI’s Historical Perspective and Planned Goals Translational Oncology. 2014 Feb;7(1):1-4. doi: http://dx.doi.org/10.1593/tlo.13832. (link)
  4. Levy MA, Freymann JB, Kirby JS, Fedorov A, Fennessy FM, Eschrich SA, Berglund AE, Fenstermacher DA, Tan Y, Guo X, Casavant TL, Brown BJ, Braun TA, Dekker A, Roelofs E, Mountz JM, Boada F, Laymon C, Oborski M, Rubin DL. Informatics methods to enable sharing of quantitative imaging research data. Magnetic Resonance Imaging. 2012 Nov;30(9):1249-56. doi: 10.1016/j.mri.2012.04.007. Epub 2012 Jul 6, n.14, pp. 15244-15255, 2010. (link)

Collection: QIN Breast

  1. Mohammed Ammar, Saïd Mahmoudi, Drisis Stylianos. Breast Cancer Response Prediction in Neoadjuvant Chemotherapy Treatment Based on Texture Analysis. Procedia Computer Science, Volume 100, 2016, Pages 812-817, ISSN 1877-0509, doi: 10.1016/j.procs.2016.09.229
  2. Li X, Abramson RG, Arlinghaus LR, Kang H, Chakravarthy AB, Abramson VG, Farley J, Mayer IA, Kelley MC, Meszoely IM, Means-Powell J, Grau AM, Sanders M, Yankeelov TE. Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer. Investigative Radiology, 2015 Apr;50(4):195-204. PMCID: PMC4471951 doi: 10.1097/RLI.0000000000000100.
  3. Weis JA, Miga MI, Arlinghaus LR, Li X, Abramson V, Chakravarthy AB, Pendyala P, Yankeelov TE. Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model. Cancer Res. 2015 Nov 15;75(22):4697-707. doi: 10.1158/0008-5472.CAN-14-2945.

  4. Atuegwu NC, Arlinghaus L, Li X, Welch EB, Chakravarthy AB, Gore JC, Yankeelov TE. Integration of diffusion weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy. Magnetic Resonance in Medicine 2011; 66:1689-96. PMCID: PMC3218213
  5. Li, X, Welch EB, Chakravarthy B, Mayer I, Meszeoly I, Kelley M, Means-Powell J, Gore JC, Yankeelov TE. Statistical comparison of dynamic contrast-enhanced MRI pharmacokinetic models in human breast cancer. Magnetic Resonance in Medicine, 2012; 68:261-71. PMCID: PMC3291742
  6. Smith DS, Gambrell JV, Li X, Arlinghaus LA, Quarles CC, Yankeelov TE, Welch EB. Robustness of Quantitative Compressive Sensing MRI: The Effect of Random Acquisitions on Derived Parameters for DCE and DSC-MRI. IEEE Transactions in Medical Imaging, 2012; 31:504-11. PMCID: PMC3289060
  7. Smith DS, Gore JC, Yankeelov TE, Welch EB. Real-time Compressive Sensing MRI Reconstruction using GPU Computing and Split Bregman Methods. International Journal of Biomedical Imaging, 2012; 2012:864827. PMCID: PMC3296267
  8. Dula AN, Arlinghaus LR, Dortch RD, Dewey BE, Whisenant JE, Ayers GD, Yankeelov TE, Smith SE. Amide Proton Transfer Imaging of the Breast at 3 T: Establishing reproducibility and possible feasibility for assessing chemotherapy response. Magnetic Resonance in Medicine, 2013; 70: 216-24. PMCID: PMC3505231
  9. Yankeelov TE, Peterson TE, Abramson RG, Garcia-Izquierdo D, Arlinghaus LR, Li X, Atuegwu NC, Catana C, Manning HC, Fayad ZA, Gore JC. Simultaneous PET-MRI in Oncology: A Solution Looking for a Problem? Magnetic Resonance Imaging, 2012; 30:1342-56. Selected as a Top 25 paper in Magnetic Resonance Imaging, 2012. PMCID: PMC3466373
  10. Abramson RG, Arlinghaus LR, Weis JA, Li X, Dula AN, Chekmenev EY, Smith SA, Miga MI, Abramson VG, Yankeelov TE. Current and emerging quantitative magnetic resonance imaging methods for assessing and predicting the response of breast cancer to neoadjuvant therapy. Breast Cancer: Targets and Therapies, 2012; 4: 139-154. PMCID: PMC3496377
  11. Li X, Abramson RG, Arlinghaus LR, Chakravarthy AB, Abramson V, Mayer I, Farley J, Delbeke D, Yankeelov TE. An Algorithm for Longitudinal Registration of PET/CT Images Acquired During Neoadjuvant Chemotherapy in Breast Cancer: Preliminary Results. European Journal of Nuclear Medicine and Molecular Imaging Research, 2012; 16:62. PMCID: PMC3520720
  12. Fluckiger U, Loveless ME, Barnes SL, Lepage M, Yankeelov TE. A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations, and experimental results. Physics in Medicine and Biology, 2013; 58:1983-98. PMCID: PMC3646091
  13. Yankeelov TE. Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer. ISRN Biomathematics, 2012; Article ID 287394. PMCID: PMC3729405
  14. Atuegwu NC, Arlinghaus LR, Li X, Chakravarthy AB, Abramson VG, Sanders ME, Yankeelov TE. Parameterizing the Logistic Model of Tumor Growth by DW-MRI and DCE-MRI Data to Predict Treatment Response and Changes in Breast Cancer Cellularity During Neoadjuvant Chemotherapy. Translational Oncology, 2013; 6:253-64. PMCID: PMC3660793
  15. Klomp DWJ, Dula AN, Arlinghaus LR, Italiaander M, Dortch RD, Zu Z, Williams JM, Gochberg DF, Luijten PR, Gore JC, Yankeelov TE, Smith SA. Amide Proton Transfer Imaging of the Human Breast at 7 Tesla: Development and Reproducibility. NMR in Biomedicine, 2013; 26:1271-7. PMCID: PMC3726578
  16. Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy AB, Abramson V, Bhave SR, Levy MA, Xu H, Yankeelov TE. Machine Learning for Predicting the Response of Breast Cancer to Neoadjuvant Chemotherapy. Journal of the American Medical Informatics Association, 2013; 20:688-95. PMCID: PMC3721158
  17. Li X, Arlinghaus LR, Ayers GD, Chakravarthy AB, Abramson RG, Abramson VG, Atuegwu N, Farley J, Mayer IA, Kelley MC, Meszoely IM, Means-Powell J, Grau AM, Sanders M, Bhave SR, Yankeelov TE. DCE-MRI Analysis Methods for Predicting the Response of Breast Cancer to Neoadjuvant Chemotherapy: Pilot Study Findings. Magnetic Resonance in Medicine, 2014; 71(4):1592-602. PMCID: PMC3742614
  18. Yankeelov TE, Atuegwu N, Hormuth D, Weis JA, Barnes SL, Miga MI, Rericha EC, Quaranta V. Clinically relevant modeling of tumor growth and treatment response. Science Translational Medicine 2013; 5:187ps9. PMCID: PMC3938952
  19. Abramson RG, Hoyt TL, Wilson KJ, Li X, Arlinghaus LR, Su P-F, Abramson VG, Chakravarthy AB, Yankeelov TE. Early Assessment of Breast Cancer Response to Neoadjuvant Chemotherapy by Semi- Quantitative Analysis of High Temporal Resolution DCE-MRI: Preliminary Results. Magnetic Resonance Imaging, 2013 ; 31:1457-64. PMCID: PMC3807825
  20. Weis JA, Miga MI, Arlinghaus LA, Li X, Chakravarthy AB, Abramson VG, Farley J, Yankeelov TE. A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy. Physics of Medicine and Biology, 2013; 58:5851-66. PMCID: PMC3791925
  21. Smith DA, Yankeelov TE, Welch EB. Potential of Compressed Sensing in Quantitative MR Imaging of Cancer. Cancer Imaging, 2013; 13:633-44. PMCID: PMC3893904
  22. Fluckiger JU, Li X, Whisenant JG, Peterson TE, Gore JC, Yankeelov TE. Using dynamic contrast enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations. International Journal of Biomedical Imaging, 2013; 2013:576470. PMCID: PMC3814089
  23. Fedorov A, Fluckiger J, Ayers GD, Li X, Gupta SN, Mulkern R, Yankeelov TE, Fennessy FM. A Comparison of Two Methods for Estimating DCE-MRI Parameters via Individual and Cohort Based AIFs in Prostate Cancer: A Step Towards Practical Implementation. Magnetic Resonance Imaging, 2014; 32:321-9. PMCID: PMC3965600
  24. Li X, Kang H, Arlinghaus LR, Abramson RG, Chakravarthy AB, Abramson VG, Farley J, Sanders M, Yankeelov TE. Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Translational Oncology, 2014; 7:14-22. PMCID: PMC3998687
  25. Chenevert TL, Malyarenko DI, Newitt D, Hylton N, Huang W, Li X, Tudorica A, Fedorov A, Fennessy F, Kikinis R, Arlinghaus L, Li X, Yankeelov TE, Muzi M, Marro KI, Kinahan PE, Jajamovich GH, Dyvorne HA, Taouli B, Kalpathy-Cramer J, Oborski MJ, Laymon CM, Mountz JM, Ross BD. Error in Quantitative Image Analysis Due to Platform-Dependent Image Scaling. Translational Oncology, 2014; 7:65-71. PMCID: PMC3998685
  26. Huang W, Li X, Chen Y, Li X, Chang M-C, Oborski MJ, Malyarenko DI, Muzi M, Jajamovich GH, Federov A, Tudorica A, Gupta S, Laymon CM, Marro KI, Dyvorne HA, Miller JV, Chenevert TL, Yankeelov TE, Mountz JM, Kinahan PE, Kikinis R, Taouli B, Fennessy F, Kalpathy-Cramer J. Variations of Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Evaluation of Breast Cancer Therapy Response: A Multicenter Data Analysis Challenge. Translational Oncology, 2014; 7:153-66. PMCID: PMC3998693
  27. Atuegwu NC, Li X, Arlinghaus LR, Abramson RG, Williams JM, Chakravarthy AB, Abramson V, Yankeelov TE. Longitudinal, Inter-modality Registration of Quantitative Breast PET and MRI Data Acquired Before and During Neoadjuvant Chemotherapy: Preliminary Results. Medical Physics, 2014; 41:052302. PMCID: PMC4000383

...

  1. Angela Giardino, Supriya Gupta, Emmi Olson, Karla Sepulveda, Leon Lenchik, Jana Ivanidze, Rebecca Rakow-Penner, Midhir J. Patel, Rathan M. Subramaniam, Dhakshinamoorthy Ganeshan. Role of Imaging in the Era of Precision Medicine. Academic Radiology, Available online 25 January 2017 doi: 10.1016/j.acra.2016.11.021
  2. Albiol, Alberto; Corbi, Alberto; Albiol, Francisco. Automatic intensity windowing of mammographic images based on a perceptual metric. Medical Physics, 2473-4209.10.1002/mp.12144 
  3. Wu, J; Sun, X; Wang, J; Cui, Y;  Kato, F; Shirato, H; Ikeda, DM.; Li, R. Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation. Journal of Magnetic Resonance Imaging, 2586 doi: 10.1002/jmri.25661
  4. Wu J, Cui Y, Sun X, Cao G, Li B, Ikeda DM, Kurian AW, Li R. Unsupervised clustering of quantitative image phenotypes reveals breast cancer subtypes with distinct prognoses and molecular pathways. Clinical Cancer Research. 2017:clincanres. 2415.016. (link)

  5. Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging. Radiology, 2014. DOI: 10.1148/radiol.14132641 (link)
  6. Lavasani, S. N., A. F. Kazerooni, et al. (2015). Discrimination of Benign and Malignant Suspicious BreastTumors Based on Semi-Quantitative DCE-MRI ParametersEmploying Support Vector Machine. Frontiers in Biomedical Technologies 2(2): 397-403.

  7. Anand, S., V. Vinod, et al. Application of Fuzzy c-means and Neural networks to categorize tumor affected breast MR Images. International Journal of Applied Engineering Research 10(64): 2015.

  8. Guo, W., H. Li, et al. (2015). Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. Journal of Medical Imaging 2(4): 041007-041007.

Collection: TCGA-GBM

  1. Lee, J.K., Wang, J., Sa, J.K., et al. Spatiotemporal genomic architecture informs precision oncology in glioblastoma. Nature Genetics.(2017) DOI: 10.1038/ng.3806
  2. Cui Y, Ren S, Tha KK, Wu J, Shirato H, Li R. Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma. European Radiology. 2017:1-10. (link)

  3. Kanas VG, Zacharaki EI, Thomas GA, Zinn PO, Megalooikonomou V, Colen RR. Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma. Computer Methods and Programs in Biomedicine. 2017;140:249-57.(link)

  4. Czarnek N, Clark K, Peters KB, Mazurowski MA. Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study. Journal of Neuro-Oncology. 2017:1-8. (link)

  5. Chaddad A, Desrosiers C, Toews M, editors. Radiomic analysis of multi-contrast brain MRI for the prediction of survival in patients with glioblastoma multiforme. Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference; 2016.

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Collection: 4D-Lung 

  1. Woodruff, H. C., Shieh, C.-C., Hegi-Johnson, F., Keall, P. J. and Kipritidis, J. (2017), Quantifying the reproducibility of lung ventilation images between 4-Dimensional Cone Beam CT and 4-Dimensional CT. Med. Phys. DOI: 10.1002/mp.12199
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