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For convenience you can also obtain the publications specifically based on TCIA in Endnote XML format: Pubs_basedon_TCIA_1218.xml. This should be usable as input to your favorite reference management system.
TCIA-Related Publication History
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- Mackie, T. R., Jackson, E. F., & Giger, M. (2018). Opportunities and challenges to utilization of quantitative imaging: Report of the AAPM practical big data workshop. Medical Physics. DOI: 10.1002/mp.13135
- Sumathipala, Y., Shafiq, M., Bongen, E., Brinton, C., & Paik, D. (2018). Machine learning to predict lung nodule biopsy method using CT image features: A pilot study. Computerized Medical Imaging and Graphics. doi: 10.1016/j.compmedimag.2018.10.006
- Cha J, Farhangi MM, Dunlap N, Amini AA. Segmentation and tracking of lung nodules via
graph‐cuts incorporating shape prior and motion from 4D CT. Medical physics. 2018;45(1):297-306. doi: 10.1002/mp.12690.
Agnes, S. A., Anitha, J., & Peter, J. D. (2018). Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN). Neural Computing and Applications. DOI: 10.1007/s00521-018-3877-3
Kohl, S. A., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K. H., Eslami, S., Rezende, D. J., Ronneberger, O. (2018). A probabilistic U-Net for segmentation of ambiguous images. Retrieved from https://arxiv.org/pdf/1806.05034.pdf
- Kang, G., Liu, K., Hou, B., & Zhang, N. (2017). 3D multi-view convolutional neural networks for lung nodule classification. (Y. Deng, Ed.) PLOS One, 12(11). DOI: 10.1371/journal.pone.0188290
- Liu, F., Feng, J., Su, W., Lv, Z., Xiao, F., & Qiu, S. (2017). Normalized euclidean super-pixels for medical image segmentation. International Conference on Intelligent Computing (pp. 586-597). Springer. 10.1007/978-3-319-63315-2_51
Farag, A. A., Ali, A., Elshazly, S., & Farag, A. A. (2017). Feature fusion for lung nodule classification. International Journal of Computer Assisted Radiology and Surgery, 1-10. DOI:10.1007/s11548-017-1626-1
- MC Hancock, JF Magnan. Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the Lung Image Database Consortium dataset. Proc. SPIE Medical Imaging: Computer-Aided Diagnosis (2017). International Society for Optics and Photonics. DOI: 10.1117/12.2254446
- Wang, D; Fong, S; Wong, RK.; Mohammed, S; Fiaidhi, J; Wong, KKL. Robust High-dimensional Bioinformatics Data Streams Mining by ODR-ioVFDT. Scientific Reports 7, article number 43167 DOI: 10.1038/srep43167
Mhetre RR, Sache RG. Detection of Lung Cancer Nodule on CT scan Images by using Region Growing Method. International Journal of Current Trends in Engineering & Research. 2016;2(7):215-9. (link)
Setio AAA, Traverso A, de Bel T, Berens MS, Bogaard Cvd, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B. Validation, comparison, and combination of algorithms for automaticdetection of pulmonary nodules in computed tomography images: the LUNA16 challenge. arXiv preprint arXiv:161208012. 2016:1-16.
- Firmino M, Angelo G, et al. Computer-aided Detection (CADe) and Diagnosis (CADx) System for Lung Cancer with Likelihood of Malignancy Biomed Eng Online (2016) 15(1):2 (link)
- Deep G, Kaur L, et al. Directional Local Ternary Quantized Extrema Pattern: A new descriptor for Biomedical Image Indexing and Retrieval Eng Sci and Tech, an International Journal (2016) (link)
- Wang W, Luo J, Yang X, Lin H. Data Analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative. Academic Radiology. 2015.
Sivakumar, S. and C. Chandrasekar (2015). "A Novel Noise Removal Method for Lung CT SCAN Images Using Statistical Filtering Techniques." International Journal of Algorithms Design and Analysis 1(1).
- Shen S, Bui AA, Cong J, Hsu W. An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Computers in biology and medicine. 2015;57:139-49.
- Messay T, Hardie RC, Tuinstra TR. 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. Medical Image Analysis. 2015.(paper)
Magdy, E., N. Zayed, et al. Automatic Classification of Normal and Cancer Lung CT Images using Multi-scale AM-FM Features. Intl Journal of Biomedical Imaging, 2015. (link)
Lassen BC, Jacobs C, et al. Robust Semi-automatic Segmentation of Pulmonary Subsolid Nodules in Chest Computed Tomography Scans. Phys Med Biol (2015) 60(3):1307-1323. (link)
Kumar, D., M. J. Shafiee, et al. Discovery Radiomics for Computed Tomography Cancer Detection. arXiv e-print, 2015. (arXiv link)
Demir, Ö. and A. Yılmaz Çamurcu (2015). "Computer-aided detection of lung nodules using outer surface features." Bio-Medical Materials and Engineering 26(s1): 1213-1222.
Kumar, A., F. Nette, et al. (2014). "A Visual Analytics Approach using the Exploration of Multi-Dimensional Feature Spaces for Content-based Medical Image Retrieval IEEE J Biomed Health Inform (2014) 19(5):1734:1746 (pubmed link)
- Sivakumar S and Chandrasekar C, Lung nodule detection using fuzzy clustering and support vector machines. International Journal of Engineering and Technology, 2013. 5(1):179-185.(link)
- Gavrielides MA, Zeng R, Myers KJ, Sahiner B, Petrick N. Benefit of overlapping reconstruction for improving the quantitative assessment of CT lung nodule volume. Academic Radiology, 2013. 20(2):173-180. DOI: 10.1016/j.acra.2012.08.014. (link)
- Aggarwal P, Vig R, and Sardana H Patient-Wise Versus Nodule-Wise Classification of Annotated Pulmonary Nodules using Pathologically Confirmed Cases. Journal of Computers, 2013. 8(9):2245-2255. (link)
- Sivakumar S and Chandrasekar C, Lungs image segmentation through weighted FCM.Recent Advances in Computing and Software Systems (RACSS), 2012 International Conference. 25-27 April 2012 pages 109-113. IEEE. DOI:10.1109/RACSS.2012.6212707 (link)
- Armato S, et al., Collaborative projects. Int J CARS, 2012. 7(1):S111-S115.
- Diciotti S, Lombardo S, Falchini M, Picozzi G, Mascalchi M. Automated segmentation refinement of small lung nodules in CT scans by local shape analysis. Biomedical Engineering, IEEE Transactions. 2011. 58(12):3418-3428. DOI: 10.1109/TBME.2011.2167621. (link)
Raicu DS, Varutbangkul E, Furst JD, Armato SG III: Modeling semantics from image data: Opportunities from LIDC. International Journal of Biomedical Engineering and Technology 3: 83–113, 2010.
Zinovev D, Duo Y, Raicu DS, Furst JD, Armato SG III: Consensus versus disagreement in imaging research: A case study using the LIDC Database. Journal of Digital Imaging 25: 423–436, 2012.
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Arimura, H., Soufi, M., Ninomiya, K., Kamezawa, H., & Yamada, M. (2018). Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis. Radiological Physics and Technology, 48, 27-36. DOI: 10.1007/s12194-018-0486-x
Buch, K., Kuno, H., Qureshi, M. M., Li, B., & Sakai, O. (2018). Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model. Journal of Applied Clinical Medical Physics. DOI: 10.1002/acm2.12482
- Barani R, Sumathi M. A New Adaptive-Weighted Fusion Rule for Wavelet based PET/CT Fusion. International Journal of Signal Processing, Image Processing and Pattern Recognition. 2016;9(11):271-82. DOI: 10.14257/ijsip.2016.9.11.25
- Aerts, H. J. W. L. et al. Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC. Sci. Rep.(2016) 6, 33860 (link)
Oliveira B, O'Halloran M, Conceicao R, Glavin M, Jones E. Development of Clinically-Informed 3D Tumor Models for Microwave Imaging Applications. IEEE Antennas and Wireless Propagation Letters 2016;15:520-3. DOI: 10.1109/LAWP.2015.2456051
- Melouah A. Comparison of Automatic Seed Generation Methods for Breast Tumor Detection Using Region Growing Technique. Computer Science and Its Applications: Springer; 2015. p. 119-28.
- Aerts HJ, Velazquez ER, Leijenaar RTH, Parmar C, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications, 2014. 5(4006). DOI:10.1038/ncomms5006 (link)
- Balagurunathan Y, Kumar V, Gu Y, Kim J, Wang H, Liu Y, Goldgof DB, Hall LO, Korn R, Zhao B. Test–Retest Reproducibility Analysis of Lung CT Image Features. Journal of digital imaging. 2014:1-19.
Melouah, A. (2015). Comparison of Automatic Seed Generation Methods for Breast Tumor Detection Using Region Growing Technique. Computer Science and Its Applications, Springer: 119-128.
Desseroit M-C, Visvikis D, Tixier F, Majdoub M, Perdrisot R, Guillevin R, Le Rest CC, Hatt M. Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III. European journal of nuclear medicine and molecular imaging. 2016:1-9. DOI: 10.1007/s00259-016-3325-5
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Lehrer, M., Bhadra, A., Aithala, S., Ravikumar, V., Zheng, Y., Dogan, B., Bonaccio, E., Burnside, E. S., Morris, E., Sutton, E., Whitman, G. J., Net, J., Brandt, K., Ganott, M., Zuley, M., Rao, A., & TCGA Breast Phenotype Research Group. (2018). High-dimensional regression analysis links magnetic resonance imaging features and protein expression and signaling pathway alterations in breast invasive carcinoma. Oncoscience, 5(1-2), 39-48. (link)
Al-Dabagh MZ, AL-Mukhtar FH. Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine. IJAERS. 2017;4(3):258-63. DOI: 10.22161/ijaers.4.3.41
- 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
- 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
- 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
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)
- 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)
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.
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.
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.
Kim, G. R., Ku, Y. J., Cho, S. G., Kim, S. J., & Min, B. S. (2017). Associations between gene expression profiles of invasive breast cancer and breast imaging reporting and data system MRI lexicon. Annals of Surgical Treatment and Research, 93(1), 18-26. DOI: 10.4174/astr.2017.93.1.18
Collection: TCGA-GBM
Han, L., & Kamdar, M. R. (2018). MRI to MGMT: Predicting methylation status in glioblastoma patients using convolutional recurrent neural networks. Pacific Symposium on Biocomputing, 23, 331-342. (link)
ParthaSarathi, M., & Ansari, M. A. (2017). Multimodal retrieval framework for brain volumes in 3D MR volumes. Journal of Medical and Biological Engineering, 1-12. DOI:10.1007/s40846-017-0287-4
Liu, Y., Xu, X., Yin, L., Zhang, X., Li, L., & Lu, H. (2017). Relationship between glioblastoma heterogeneity and survival time: An MR imaging texture analysis. American Journal of Neuroradiology, 1-7. DOI:10.3174/ajnr.A5279.
Beig N, Patel J, Prasanna P, et al. Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in Glioblastoma. SPIE Medical Imaging; 2017; 10134:1-10. International Society for Optics and Photonics. DOI:10.1117/12.2255694
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
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)
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)
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)
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.
Prasanna, P., Patel, J., Partovi, S. et al. Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings. Eur Radiol (2016) pp 1–10. DOI:10.1007/s00330-016-4637-3
Mulvey M, Muhyadeen S, Sinha U. Classification of Glioblastoma Multiforme Molecular Subtypes Using Three-Dimensional Multi-Modal MR Imaging Features. Med. Phys. 43, 3373 (2016); (link)
- Ren X, Cui Y, Gao H, Li, R. Identifying High-Risk Tumor Volume Based On Multi-Region and Integrated Analysis of Multi-Parametric MR Images for Prognostication of Glioblastoma. Med. Phys. 43, 3751 (2016); (link)
- Dunn WD Jr, Aerts HJWL, et al. Assessing the Effects of Software Platforms on Volumetric Segmentation of Glioblastoma. J Neuroimaging Psychiatry Neurol 2016. 1(2): 64-72.
- Upadhaya T, Morvan Y, et al. Prognosis classification in glioblastoma multiforme using multimodal MRI derived heterogeneity textural features: impact of pre-processing choices. Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850W (March 24, 2016); (link)
Upadhaya T, Morvan Y, et al. Prognostic value of multimodal MRI tumor features in Glioblastoma multiforme using textural features analysis. In Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, pp. 50-54. IEEE, 2015.
Upadhaya T, Morvan Y, et al. A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in Glioblastoma Multiforme. IRBM. 2015 Nov 30;36(6):345-50.
Reza SM, Mays R, Iftekharuddin KM, editors. Multi-fractal detrended texture feature for brain tumor classification. SPIE Medical Imaging; 2015: International Society for Optics and Photonics.
Nabizadeh N, Kubat M. Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. Computers & Electrical Engineering. 2015.
Natteshan N, Jothi JAA. Automatic Classification of Brain MRI Images Using SVM and Neural Network Classifiers. Advances in Intelligent Informatics: Springer; 2015. p. 19-30. (link)
Zhang J, Barboriak DP, Hobbs H, Mazurowski MA. A fully automatic extraction of magnetic resonance image features in Glioblastoma patients. Medical physics. 2014;41(4):042301.
Wangaryattawanich P, Wang J, Thomas GA, Chaddad A, Zinn PO, Colen RR, editors. Survival analysis of pre-operative GBM patients by using quantitative image features. Control, Decision and Information Technologies (CoDIT), 2014 International Conference on; 2014: IEEE.
Colen RR, Wang J, Singh SK, Gutman DA, Zinn PO. Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death. Radiology. 2014.
- Colen RR, Vangel M, Wang J, Gutman DA, Hwang SN, Wintermark M, Rajan J, Jilwan-Nicola M, Chen JY, Raghavan P, Holder CA, Rubin D, Huang E, Kirby J, Freymann J, Jaffee CC, Flanders A, Zinn PO. Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project.BMC Medical Genomics, 2014. 7(1):30. DOI: 10.1186/1755-8794-7-30 (link)
- Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK, Chesier SH, Napel S, Zaharchuk G, Plevritis SK. Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features. Radiology, 2014. DOI: 10.1148/radiol.14131731 (link)
- Mazurowski MA, Zhang J, Peters KB, and Hobbs H. Computer-extracted MR imaging features are associated with survival in glioblastoma patients. Journal of Neuro-Oncology, 2014. 120(3):483–488 DOI: 10.1007/s11060-014-1580-5 (link)
- Jain R, Poisson L, Gutman D, Scarpace L, Hwang SN, Holder C, Wintermark M, Colen RR, Kirby J, Freymann J, Jaffe C, Mikkelsen T, Flanders A. Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor. Radiology. 2014 Aug;272(2):484-93. DOI: 10.1148/radiol.14131691. Epub 2014 Mar 19. 2014 (link)
- Nicolasjilwan M, Hu Y, Yan C, Meerzaman D, Holder CA, Gutman D, et al. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. Journal of Neuroradiology, July 2014. DOI: 10.1016/j.neurad.2014.02.006
- Wassal E, Zinn P, Colen R. Diffusion and conventional MR imaging genomic biomarker signature for EGFR mutation identification in glioblastoma. Neuro-Oncology. 2014;16(suppl 5):v156-v7.
Wassal E, Zinn P, Colen R. Diffusion and conventional and MR imaging genomic biomarker signature predicts IDH-1 mutation in glioblastoma patients. Neuro-Oncology. 2014;16(suppl 5):v157-v.
Kwon D, Shinohara RT, Akbari H, Davatzikos C. Combining Generative Models for Multifocal Glioma Segmentation and Registration. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: Springer; 2014. p. 763-70.
- Amer A, Zinn P, Colen R. Immediate post operative volume of abnormal flair signal predicts patient survival in glioblastoma patients. Neuro-Oncology. 2014;16(suppl 5):v138-v.
- Amer A, Zinn P, Colen R. Immediate post-resection pericavitarian DWI hyperintensity in glioblastoma patients is predictive of patient outcome. Neuro-Oncology. 2014;16(suppl 5):v138-v9.
- Gutman DA, Cooper LAD, Hwang SN, Holder CA, Gao J, Aurora TD, Dunn WD, Scarpace L, Mikkelsen T, Jain R, Wintermark M, Jilwan M, Raghavan P, Huang E, Clifford RJ, Monqkolwat P, Kleper V, Freymann J, Kirby J, Zinn PO, Moreno CS, Jaffe C, Colen R, Rubin DL, Saltz J, Flanders A, Brat DJ. MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set. Radiology. 2013 May:267(2):560-569,DOI:10.1148/radiol.13120118 (link)
- Jain R, Poisson L, Narang J, Gutman D, Scarpace L, Hwang SN, Holder C, Wintermark M, Colen RR, Kirby J, Freymann J, Brat DJ, Jaffe C, Mikkelsen T. Genomic Mapping and Survival Prediction in Glioblastoma: Molecular Subclassification Strengthened by Hemodynamic Imaging Biomarkers. Radiology, 2013 Apr:267(1):212 –220, DOI:10.1148/radiol.12120846 (link)
- Mazurowski MA, Desjardins A, Malof JM. Imaging descriptors improve the predictive power of survival models for glioblastoma patients. Neuro-oncology, 2013. 15(10):1389-1394 (link)
- Zinn PO, Colen RR. Imaging Genomic Mapping in Glioblastoma. Neurosurgery 60:126-130. Aug 2013 (link)
- Jain R, Poisson L, Narang J, Scarpace L, Rosenblum ML, Rempel S, Mikkelson T. Correlation of Perfusion Parameters with Genes Related to Angiogenesis Regulation in Glioblastoma: A Feasibility Study. American Journal of Neuroradiology, 2012. 33(7):1343-1348 [Epub ahead of print] (link)
- Zinn PO, Sathyan P, Mahajan B, Bruyere J, Hegi M, et al. A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature. PLoS ONE, 2012 7(8): e41522. DOI:10.1371/journal.pone.0041522 (link)
- Zinn PO, Majadan B, Sathyan P, Singh SK, Majumder S, et al. Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme. PLoS ONE, 2011 6(10): e25451. DOI:10.1371/journal.pone.0025451 (link)
Wangaryattawanich, P., M. Hatami, et al. "Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival." Neuro-oncology, (2015): nov117 .
Kuo, J. S., K. B. Pointer, et al. (2015). "139 Human Ether-a-Go-Go-Related-1 Gene (hERG) K+ Channel as a Prognostic Marker and Therapeutic Target for Glioblastoma." Neurosurgery 62: 210-211.
Zinn, P. O., M. Hatami, et al. (2015). "138 Diffusion MRI ADC Mapping of Glioblastoma Edema/Tumor Invasion and Associated Gene Signatures." Neurosurgery 62: 210.
Steed, T., J. Treiber, et al. (2015). "Iterative Probabilistic Voxel Labeling: Automated Segmentation for Analysis of The Cancer Imaging Archive Glioblastoma Images." American Journal of Neuroradiology 36(4): 678-685.
Lee, J., S. Narang, et al. (2015). "Associating spatial diversity features of radiologically defined tumor habitats with epidermal growth factor receptor driver status and 12-month survival in glioblastoma: methods and preliminary investigation." Journal of Medical Imaging 2(4): 041006-041006.
Itakura, H., A. S. Achrol, et al. (2015). "Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities." Science Translational Medicine 7(303): 303ra138-303ra138.
Cui, Y., K. K. Tha, et al. (2015). "Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images." Radiology: 150358.
Lee, J., S. Narang, et al. (2015). "Spatial Habitat Features Derived from Multiparametric Magnetic Resonance Imaging Data Are Associated with Molecular Subtype and 12-Month Survival Status in Glioblastoma Multiforme." PloS one 10(9): e0136557.
Rios Velazquez E, Meier R, Dunn WD Jr, Alexander B, Wiest R, Bauer S, Gutman DA, Reyes M, Aerts HJ. "Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features." Sci Rep. 2015 Nov 18;5:16822. DOI: 10.1038/srep16822.
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- 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
- Hugo GD, Weiss E, Sleeman WC, Balik S, Keall PJ, Lu J, Williamson JF. A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer. Med. Phys. (2017) DOI: 10.1002/mp.12059
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1. Halani, S. H., Yousefi, S.; Vega, J. V.; Rossi, M. R.; Zhao, Z.; Amrollahi, F.; Holder, C. A.; Baxter-Stoltzfus, A.; Eschbacher, J.; Griffith, B.; Olson, J. J.; Jiang, T.; Yates, J. R.; Eberhart, C. G.; Poisson, L. M.; Cooper, L. A. D.; Brat, D. J. (2018). Multi-faceted computational assessment of risk and progression in oligodendroglioma implicates NOTCH and PI3K pathways. Precision Oncology.
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