backup_dois.json

[{ "_id" : { "$oid" : "56fcfa8766d8edd2fbb2500c"} , "title" : "First test with files" , "description" : "asdfasdf" , "authors" : [ "asdfasdfasdf"] , "keywords" : "asdfasdf" , "year" : "2001" , "references" : "asdfasdfasdf" , "url" : "http://localhost:3003/doi?doi=10.5072/FK2.2001.wY7nevMU" , "doi" : "http://dx.doi.org/10.5072/FK2.2001.wY7nevMU"},{ "_id" : { "$oid" : "56fcfaeb66d8edd2fbb2500e"} , "title" : "Resources test 2" , "description" : "sdfdsa asd fasdfasdf" , "authors" : [ "asdfsdf"] , "keywords" : "adfasdf" , "year" : "2010" , "references" : "asdfsdfasdf" , "url" : "http://localhost:3003/doi?doi=10.5072/FK2.2010.kN1o8GbU" , "doi" : "http://dx.doi.org/10.5072/FK2.2010.kN1o8GbU"},{ "_id" : { "$oid" : "56fcfb4866d8edd2fbb25010"} , "title" : "asdfa" , "description" : "adsfadf" , "authors" : [ "asdfasf"] , "keywords" : "asdfsadf" , "year" : "2010" , "references" : "asdfadf" , "url" : "http://localhost:3003/doi?doi=10.5072/FK2.2010.25PVeom0" , "doi" : "http://dx.doi.org/10.5072/FK2.2010.25PVeom0"},{ "_id" : { "$oid" : "56fcfb9c66d8edd2fbb25012"} , "title" : "Resource Test 3" , "description" : "adfasdfasdf" , "authors" : [ "asdfasdf"] , "keywords" : "afasdf" , "year" : "2010" , "references" : "afasdfsadfsadf" , "url" : "http://localhost:3003/doi?doi=10.5072/FK2.2010.KhwQQzMm" , "doi" : "http://dx.doi.org/10.5072/FK2.2010.KhwQQzMm"},{ "_id" : { "$oid" : "56fcfbd666d8edd2fbb25014"} , "title" : "Resource Test 4" , "description" : "asdfasdfadf" , "authors" : [ "adfasdf"] , "keywords" : "asdfadsf" , "year" : "2010" , "references" : "sadfsdfdfs" , "url" : "http://localhost:3003/doi?doi=10.5072/FK2.2010.tFimWOay" , "doi" : "http://dx.doi.org/10.5072/FK2.2010.tFimWOay"},{ "_id" : { "$oid" : "56fd6f5a66d8e5b9c02ef4a9"} , "title" : "New 1" , "description" : "sdagss" , "authors" : [ "ganesh" , " test" , " asdfas"] , "keywords" : "sadfasdf" , "year" : "2010" , "references" : "adsfasdfasdf" , "url" : "http://localhost:3003/doi?doi=10.5072/FK2.2010.kCHvMYjg" , "doi" : "http://dx.doi.org/10.5072/FK2.2010.kCHvMYjg"},{ "_id" : { "$oid" : "56f49ad666d8edd2fbb24f52"} , "doi" : "http://dx.doi.org/10.7937/K9/TCIA.2014.X7ONY6B1" , "description" : "This collection contains images from patients with non-small cell lung cancer (NSCLC) imaged prior to surgical excision with both thin-section computed tomography (CT) and whole body positron emissions tomography (PET)/CT scans acquired under Institutional Review Board approval from Stanford University and the Veterans Administration Palo Alto Health Care System. The first installment of 26 cases corresponds to microarray data acquired from the excised samples, which is available on the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus, where Digital Imaging and Communications in Medicine (DICOM) patient names are identical to microarray sample names. More information about this data can be found at: [NSCLC Radiogenomics](https://wiki.cancerimagingarchive.net/display/Public/NSCLC+Radiogenomics)." , "title" : "Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results." , "references" : "**Radiology**. 2012 Aug;264(2):387-96. doi: 10.1148/radiol.12111607. Epub 2012 Jun 21. PMID: 22723499 ; [PMCID: PMC3401348](#)" , "url" : "http://localhost:3003/doi?doi=10.7937/K9/TCIA.2014.X7ONY6B1" , "authors" : [ "Gevaert O" , "Xu J" , "Hoang CD"]},{ "_id" : { "$oid" : "56f49ae466d8edd2fbb24f53"} , "doi" : "http://dx.doi.org/10.7937/K9/TCIA.2015.3BPe5wRq" , "description" : "This dataset pertains to 74 cases from the **GBM dataset** on which spatial pattern analysis was performed. Spatial Habitat Features derived from Multiparametric Magnetic Resonance Imaging data are associated with Molecular Subtype and 12-month Survival Status in Glioblastoma Multiforme" , "title" : "Spatial Habitat Features derived from Multiparametric Magnetic Resonance Imaging data from Glioblastoma Multiforme cases" , "references" : "**Nat Commun.** 2014 Jun 3;5:4006. doi: 10.1038/ncomms5006. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. [PMID:24892406 ; PMCID:PMC4059926](http://www.ncbi.nlm.nih.gov/pubmed/24892406)" , "url" : "http://localhost:3003/doi?doi=10.7937/K9/TCIA.2015.3BPe5wRq" , "authors" : [ "Joonsang Lee" , "Shivali Narang" , "Juan Martinez" , "Ganesh Rao" , "Arvind Rao"]},{ "_id" : { "$oid" : "56f49aed66d8edd2fbb24f54"} , "doi" : "http://dx.doi.org/10.7937/K9/TCIA.2015.1BUVFJR7" , "description" : "This dataset (also known as the “moist run” among QIN sites) contains CT images (41 total scans) of non-small cell lung cancer from: the Reference Image Database to Evaluate Therapy Response (RIDER), the Lung Image Database Consortium (LIDC), patients from Stanford University Medical Center and the Moffitt Cancer Center, and the Columbia University/FDA Phantom. In addition, 3 academic institutions (Columbia, Stanford, Moffitt-USF) each ran their own segmentation algorithm on a total of 52 tumor volumes.  Segmentations were performed 3 different times with different initial conditions, resulting in 9 segmentations formatted as DICOM Segmentation Objects (DSOs) for each tumor volume, for a total of 468 segmentations. This collection may be useful for designing and comparing competing segmentation algorithms, for establishing acceptable ranges of variability in volume and segmentation borders, and for developing algorithms for creating cancer biomarkers from features computed from the segmented tumors and their environments." , "title" : "QIN multi-site collection of Lung CT data with Nodule Segmentations" , "references" : "a" , "url" : "http://localhost:3003/doi?doi=10.7937/K9/TCIA.2015.1BUVFJR7" , "authors" : [ "Jayashree Kalpathy-Cramer" , "Sandy Napel" , "Dmitry Goldgof" , "Binsheng Zhao"]},{ "_id" : { "$oid" : "56f4b1f466d8edd2fbb24f57"} , "title" : "Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging" , "description" : "To investigate associations between breast cancer molecular subtype and semiautomatically extracted magnetic resonance (MR) imaging features. Materials and Methods Imaging and genomic data from the Cancer Genome Atlas and the Cancer Imaging Archive for 48 patients with breast cancer from four institutions in the United States were used in this institutional review board approval-exempt study. Computer vision algorithms were applied to extract 23 imaging features from lesions indicated by a breast radiologist on MR images. Morphologic, textural, and dynamic features were extracted. Molecular subtype was determined on the basis of genomic analysis. Associations between the imaging features and molecular subtype were evaluated by using logistic regression and likelihood ratio tests. The analysis controlled for the age of the patients, their menopausal status, and the orientation of the MR images (sagittal vs axial). Results There is an association (P = .0015) between the luminal B subtype and a dynamic contrast material-enhancement feature that quantifies the relationship between lesion enhancement and background parenchymal enhancement. Cancers with a higher ratio of lesion enhancement rate to background parenchymal enhancement rate are more likely to be luminal B subtype. Conclusion The luminal B subtype of breast cancer is associated with MR imaging features that relate the enhancement dynamics of the tumor and the background parenchyma. (c) RSNA, 2014 Online supplemental material is available for this article.\r\n" , "author[1]" : "Maciej A. Mazurowski, PhD, Jing Zhang, PhD, Lars J. Grimm, MD, MHS, Sora C. Yoon, MD, James I. Silber" , "keywords" : "" , "year" : "" , "references" : "Radiology. 2014 Nov;273(2):365-72. doi: 10.1148/radiol.14132641. Epub 2014 Jul 15. Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging.\r\n" , "url" : "http://localhost:3003/doi?doi=10.5072/FK2tUGnhRDK" , "doi" : "http://dx.doi.org/10.5072/FK2tUGnhRDK"},{ "_id" : { "$oid" : "56f4bc9266d8edd2fbb24f59"} , "title" : "MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set" , "description" : "**PURPOSE:**\r\n\r\nTo conduct a comprehensive analysis of radiologist-made assessments of glioblastoma (GBM) tumor size and composition by using a community-developed controlled terminology of magnetic resonance (MR) imaging visual features as they relate to genetic alterations, gene expression class, and patient survival.\r\n\r\n**MATERIALS AND METHODS:**\r\n\r\nBecause all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a publicly available data set that contains no linkage to patient identifiers and that is HIPAA compliant, no institutional review board approval was required. Presurgical MR images of 75 patients with GBM with genetic data in the TCGA portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set. Interrater agreements were analyzed by using the Krippendorff α statistic and intraclass correlation coefficient. Associations between survival, tumor size, and morphology were determined by using multivariate Cox regression models; associations between imaging features and genomics were studied by using the Fisher exact test.\r\n\r\n\r\n**RESULTS:**\r\n\r\nInterrater analysis showed significant agreement in terms of contrast material enhancement, nonenhancement, necrosis, edema, and size variables. Contrast-enhanced tumor volume and longest axis length of tumor were strongly associated with poor survival (respectively, hazard ratio: 8.84, P = .0253, and hazard ratio: 1.02, P = .00973), even after adjusting for Karnofsky performance score (P = .0208). Proneural class GBM had significantly lower levels of contrast enhancement (P = .02) than other subtypes, while mesenchymal GBM showed lower levels of nonenhanced tumor (P < .01).\r\n\r\n**CONCLUSION:**\r\n\r\nThis analysis demonstrates a method for consistent image feature annotation capable of reproducibly characterizing brain tumors; this study shows that radiologists' estimations of macroscopic imaging features can be combined with genetic alterations and gene expression subtypes to provide deeper insight to the underlying biologic properties of GBM subsets.\r\n" , "authors" : "Gutman DA, Cooper LA, Hwang SN, Holder CA, Gao J, Aurora TD, Dunn WD Jr, Scarpace L, Mikkelsen T, Jain R, Wintermark M, Jilwan M, Raghavan P, Huang E, Clifford RJ, Mongkolwat P, Kleper V, Freymann J, Kirby J, Zinn PO, Moreno CS, Jaffe C, Colen R, Rubin DL, Saltz J, Flanders A, Brat DJ" , "keywords" : "" , "year" : "2014" , "references" : "MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology. 2013 May;267(2):560-9. doi: 10.1148/radiol.13120118. Epub 2013 Feb 7. PubMed PMID: 23392431; PubMed Central PMCID: PMC3632807.\r\n" , "url" : "http://localhost:3003/doi?doi=10.5072/FK22014S1C9Olir" , "doi" : "http://dx.doi.org/10.5072/FK22014S1C9Olir"},{ "_id" : { "$oid" : "56f4bcb466d8edd2fbb24f5b"} , "title" : "MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set" , "description" : "**PURPOSE:**\r\n\r\nTo conduct a comprehensive analysis of radiologist-made assessments of glioblastoma (GBM) tumor size and composition by using a community-developed controlled terminology of magnetic resonance (MR) imaging visual features as they relate to genetic alterations, gene expression class, and patient survival.\r\n\r\n**MATERIALS AND METHODS:**\r\n\r\nBecause all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a publicly available data set that contains no linkage to patient identifiers and that is HIPAA compliant, no institutional review board approval was required. Presurgical MR images of 75 patients with GBM with genetic data in the TCGA portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set. Interrater agreements were analyzed by using the Krippendorff α statistic and intraclass correlation coefficient. Associations between survival, tumor size, and morphology were determined by using multivariate Cox regression models; associations between imaging features and genomics were studied by using the Fisher exact test.\r\n\r\n\r\n**RESULTS:**\r\n\r\nInterrater analysis showed significant agreement in terms of contrast material enhancement, nonenhancement, necrosis, edema, and size variables. Contrast-enhanced tumor volume and longest axis length of tumor were strongly associated with poor survival (respectively, hazard ratio: 8.84, P = .0253, and hazard ratio: 1.02, P = .00973), even after adjusting for Karnofsky performance score (P = .0208). Proneural class GBM had significantly lower levels of contrast enhancement (P = .02) than other subtypes, while mesenchymal GBM showed lower levels of nonenhanced tumor (P < .01).\r\n\r\n**CONCLUSION:**\r\n\r\nThis analysis demonstrates a method for consistent image feature annotation capable of reproducibly characterizing brain tumors; this study shows that radiologists' estimations of macroscopic imaging features can be combined with genetic alterations and gene expression subtypes to provide deeper insight to the underlying biologic properties of GBM subsets.\r\n" , "authors" : [ "Gutman DA" , " Cooper LA" , " Hwang SN" , " Holder CA" , " Gao J" , " Aurora TD" , " Dunn WD Jr" , " Scarpace L" , " Mikkelsen T" , " Jain R" , " Wintermark M" , " Jilwan M" , " Raghavan P" , " Huang E" , " Clifford RJ" , " Mongkolwat P" , " Kleper V" , " Freymann J" , " Kirby J" , " Zinn PO" , " Moreno CS" , " Jaffe C" , " Colen R" , " Rubin DL" , " Saltz J" , " Flanders A" , " Brat DJ"] , "keywords" : "" , "year" : "2014" , "references" : "MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology. 2013 May;267(2):560-9. doi: 10.1148/radiol.13120118. Epub 2013 Feb 7. PubMed PMID: 23392431; PubMed Central PMCID: PMC3632807.\r\n" , "url" : "http://localhost:3003/doi?doi=10.5072/FK22014JdnHkevY" , "doi" : "http://dx.doi.org/10.5072/FK22014JdnHkevY"},{ "_id" : { "$oid" : "56fd632866d8edd2fbb25025"} , "title" : "Test" , "description" : "asdfasdf" , "authors" : [ "asdfas"] , "keywords" : "asdfasdf" , "year" : "2001" , "references" : "asdfasdf" , "url" : "http://localhost:3003/doi?doi=10.5072/FK2.2001.u1OclxnV" , "doi" : "http://dx.doi.org/10.5072/FK2.2001.u1OclxnV"},{ "_id" : { "$oid" : "56fd6ab566d8e5b9c02ef4a7"} , "title" : "Test DOI" , "description" : "afasdfasdf asdfasdfasdf " , "authors" : [ "Ganesh"] , "keywords" : "asdfasdf " , "year" : "2010" , "references" : "dsfasdfadfasdf" , "url" : "http://localhost:3003/doi?doi=10.5072/FK2.2010.Vro9zTHv" , "doi" : "http://dx.doi.org/10.5072/FK2.2010.Vro9zTHv"}]