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:warning: Note - This page is no longer being main= tained. Click here to access the new TCI= A Publications page.
When data is submitted to TCIA it undergoes an extensive curation proces= s to assure completeness, proper formatting to facilitate discovery and dat= a reuse and removal of all protected health information. Once data is= released on the public TCIA repository it is Published to= the world. This publication is associated with the creation of a Dig= ital Object Identifier that allows direct access to the data.
In addition to data publication via TCIA we strongly urge researchers wh= o submit data to TCIA to also submit a Data Descriptor publication to a jou= rnal such as Nature Scientific Data. In this type of publi= cation the authors will describe the data acquisition process, the experime= nt that drove this data collection and value of the data for future researc= h (see each journal for specific content requirements). A Data Descri= ptor is a scientific paper that includes the DOI to the data previously pub= lished on TCIA and helps to call the attention of the scientific community = to the data you have submitted. The details provided in a Data Descri= ptor publication greatly enhance the value of your contribution.
A Data Descriptor is different from a scholarly paper in which you descr= ibe your experiment and present the results of your analysis. Many jo= urnals do not provide sufficient space for details of data acquisition.&nbs= p; So today you can provide those details and the data you collected by mak= ing full use of TCIA and journals that support data publication. In s= ummary we urge you to:
Please remember in all of your publications based on TCIA data to includ= e appropriate references to TCI= A so we can identify your publications, reference them, and make them e= asily available to other researchers from the TCIA web site. These citation= s are critical for providing continued justification of funding from the ag= encies that support TCIA, and are what allow us to provide this data to you= free of charge. Guidelines for how to cite TCIA can be found on our <= a href=3D"https://wiki.cancerimagingarchive.net/x/fgMy" rel=3D"nofollow" st= yle=3D"text-decoration: none;">Citation Guidelines wiki page. In = addition we would like to list these publications here on our web site. If = you have utilized TCIA in your research please contact us at help@cancerimagingarchive.net so that we can include your p= ublications in the list below. The publication list below includes referenc= es to the original data collection as well as publications that specificall= y used data from TCIA.
A listing of published an= alysis results data sets based upon TCIA-hosted data is provided here.<= /p>
Download citation list (Endnote XML format)
For convenience you can also obtain the public= ations specifically based on TCIA in Endnote XML format: Pubs_basedon_TCIA_1218.xml. This should be usable as i= nput to your favorite reference management system.
TCIA-Related Publication History
Table of Contents
Vidya, K., & Kurian, M. (2018). Novel framework for brea= st cancer classification for retaining computational efficiency and precise= diagnosis. Communications Applied Electronics, 7(15), 1-= 6. (link)
= li>Brassey, C. A., O'Mahoney, T. G., Chamberlain, A. T., & Sellers,= W. I. (2017). A volumetric technique for fossil body mass estimati= on applied to Australopithecus afarensis. Journal of Human Evo= lution, 115, 47-64. DOI:10.1016/j.jhevol.2017.07= .014
Omotosho, A., Oluwatobi, A. E., Oluwaseun, O. R., Chukwuka, A. E., &= amp; Adekanmi, A. (2018). A neuro-fuzzy based system for the classi= fication of cells as cancerous or non-cancerous. International= Journal of Medical Research & Health Sciences, 7(5), 155-166. Ret= rieved from http://www.ijmrhs.com/medical-= research/a-neurofuzzy-based-system-for-the-classification-of-cells-as-cance= rous-or-noncancerous.pdf
Russell, P., Fountain, K., Wolverton, D., & Ghosh, D. (2018). TCIA pathfinder: An R client for The Cancer Imaging Archive REST API<=
/strong>. Cancer Research. DOI:10.1158/0008-5472.CAN-18-0678
Bennett, W., Smith, K., Jarosz, Q., Nolan, T., & Bosch, W. (2018= ). Reengineering workflow for curation of DICOM datasets. = Journal of Digital Imaging, 1-9. DOI:10.1007/s10278-018-0097-4
=Yassine, A.-A., Kingsford, W., Xu, Y., Cassidy, J., Lilge, L., &= Betz, V. (2018). Automatic interstitial photodynamic therapy plann= ing via convex optimization. Biomedical Optics Express, 9= (2), 898-920. DOI:10.1364/BOE.9.000898
Gueziri, H.-E. (2017). User-centered design and evaluat= ion of interactive segmentation methods for medical images. Montre= al: =C3=89cole de technologie sup=C3=A9rieure du Quebec. Retrieved fr= om http://espace.etsmtl.ca/195= 9/2/GUEZIRI_Houssem-Eddine-web.pdf
Lan, R., Zhong, S., Liu, Z., Shi, Z., & Luo, X. (2017).
Prior, F., Smith, K., Sharma, A., Kirby, J., Tarbox, L., Clark, K., = Bennett, W., Nolan, T., Freymann, J. (2017). The public cancer radi= ology imaging collections of The Cancer Imaging Archive. Nature Scientific Data, 4; 1-7. D= OI:10.1038/sdata.2017.124
Kohli, M., Morrison, J. J., Wawira, J., Morgan, M. B., & Hostett= er, J., Genereaux, B., Hussain, M., Langer S. G. (2017). Creat= ion and curation of the society of imaging informatics in medicine hackatho= n dataset. Journal of Digital Imaging, 1-4. DOI:10.1007/s10278-017-0003-5
Parks, C.L., Monson, K.L. (2016). Automated Facial Reco= gnition of Computed Tomography-Derived Facial Images: Patient Privacy Impli= cations. Journal of Digital Imaging. 1-11. DOI:10.1007/s10278-016-9932-7
Huang, B.E., Mulyasasmita, W., Rajagopal, G. (2016). Th= e Path from Big Data to Precision Medicine.Expert Review of Pr= ecision Medicine and Drug Development,1(2):129-143. (link)
Freymann, J.B., Kirby, J.S., Perry, J.H., Clunie, D.A., Jaffe, C.C. = (2012). Image data sharing for biomedical research=E2=80=94mee= ting HIPAA requirements for de-identification. Journal of= Digital Imaging, 25(1). 14-24. (= PMC3264712)
Li, Z.-C., Bai, H., Sun, Q., Zhao, Y., Lv, Y., Zhou, J., Liang, C., = Chen, Y., Liang, D., Zheng, H. (2018). Multiregional radiomics prof= iling from multiparametric MRI: Identifying an imaging predictor of IDH1 mu= tation status in glioblastoma. Cancer Medicine. DOI: 10.1002/cam4.1863
Jansen, R. W., van Amstel, P., Martens, R. M., Kooi, I. E., Wesselin= g, P., de Langen, A. J., Menke-Van der Houven van Oordt, C. W., Jansen, B. = H. E., Moll, A. C., Dorsman, J., Castelijns, J., de Graff, P., de Jong, M. = C. (2018). Non-invasive tumor genotyping using radiogenomic biomark= ers, a systematic review and oncology-wide pathway analysis. O= ncotarget, 9(28), 20134-20155. DOI: 10.1863= 2/oncotarget.24893
Smits, M., & van den Bent, M. J. (2017). Imaging correla= tes of adult glioma genotypes. Radiology, 284(2). DOI:&nb= sp;10.1148/radiol.2017151930
Lehrer, M., Bhadra, A., Ravikumar, V., Chen, J. Y., Wintermark, M., = Hwang, S. N., Holder, C. A., Huang, E. P., Fevrier-Sullivan, B., Freymann, = J. B., Rao, A., & TCGA Glioma Phenotype Research Group. (2017). Multiple-response regression analysis links magnetic resonance imaging fea= tures to de-regulated protein expression and pathway activity in lower grad= e glioma. Oncoscience, 4, 57-66. doi:10= .18632/oncoscience.353
Liu, T.T., Achrol, A.S., Mitchell, L.A., Rodriguez, S.A., Feroze, A.= , Iv, M., Kim, C., Chaudhary, N., Gevaert, O., Stuart, J.M., Harsh, G.R., C= hang, S.D., Rubin, D.L. (2016). Magnetic resonance perfusion i= mage features uncover an angiogenic subgroup of glioblastoma patients with = poor survival and better response to antiangiogenic treatment. Neuro-Oncology, 1-11. DOI:10.1093/neuonc/now270= a>
Schrock, M., Batar, B., Lee, J., Druck, T., Ferguson, B., Cho, J., A= kakpo, K., Hagrass, H., Heerema, N., Xia, F. (2016). Wwox=E2=80=93B= rca1 interaction: role in DNA repair pathway choice. Oncogene,= 1-13. DOI:10.1038/onc.2016.389.
Song, S.E., Bae, M.S., Chang, J.M., Cho, N., Ryu, H.S., Moon, W.K. (= 2016). MR and mammographic imaging features of HER2-positive b= reast cancers according to hormone receptor status: a retrospective compara= tive study. Acta Radiologica. 58(7), 792-799. DOI:<= a class=3D"external-link" href=3D"http://dx.doi.org/10.1177/028418511667311= 9" rel=3D"nofollow">10.1177/0284185116673119
McCann, S.M., Jiang, Y., Fan, X., Wang, J. Antic, T., Prior, F., Van=
derWeele, D., Oto, A. Quantitative Multiparametric MRI Feature=
s and PTEN Expression of Peripheral Zone Prostate Cancer: A
Katrib, A., Hsu, W., Bui, A., Xing, Y. (2016). =E2=80= =9CRadiotranscriptomics=E2=80=9D: A synergy of imaging and transcriptomics = in clinical assessment.Quantitative Biology. 1-12. DOI:10.1007/s40484-016-0061-6
Zhu, Y., H. Li, et al. (2015). TU-CD-BRB-06: Deciphering Gen= omic Underpinnings of Quantitative MRI-Based Radiomic Phenotypes of Invasiv= e Breast Carcinoma.Medical physics 42(6): 3603-3603. DOI:= 10.1118/1.4925591
Tomczak, K., Czerwi=C5=84ska, P., Wiznerowicz, M. (2015).
Pope, W.B. (2015). Genomics of Brain Tumor Imaging. Neuroimaging Clinics of North America. 25(1); 105-19. DOI: <= a class=3D"external-link" href=3D"http://dx.doi.org/10.1016/j.nic.2014.09.0= 06" rel=3D"nofollow">10.1016/j.nic.2014.09.006
Feldman, M., Piazza, M.G., Edwards, N.A., Ray, Chaudhury, A., Maric,= D., Merrill, M.J., Zhuang, Z., Chittiboina, P. (2015). 137 So= matostatin Receptor Expression on VHL-Associated Hemangioblastomas Offers N= ovel Therapeutic Target.Neurosurgery 62. (CN_suppl_1); 20= 9-210. DOI: 10.1227/01.neu.0000467099.84064.= 25
Gevaert, O., Xu, J., Hoang, C.D., Leung, A.N.=
, Xu, Y., Quon, A., Rubin, D.L., Napel, S., Plevritis, S.K. (2012)
Chaddad, A., Sabri, S., Niazi, T., & Abdulkarim, B. (2018).
Drukker, K., Li, H., Antropova, N., Edwards, A., Papaioannou, J., &a= mp; Giger, M. L. (2018). Most-enhancing tumor volume by MRI radiomi= cs predicts recurrence-free survival "early on" in neoadjuvant treatment of= breast cancer. Cancer Imaging, 18(1). DOI:10.1186/s40644-018-0145-9
Reeves, A. P., Xie, Y., & Liu, S. (2018). Automated imag= e quality assessment for chest CT scans. Medical Physics, 45= em>(2), 561-578. DOI: 10.1002/mp.12729
AlBadawy, E. A., Saha, A., & Mazurowski, M. A. (2018). D= eep learning for segmentation of brain tumors: Impact of cross-institutiona= l training and testing. Medical Physics. DOI: 10.1002/mp.12752
Larue, R. T. H. M., Van De Voorde, L., van Timmeren, J. E., Leijenaa= r, Ralph T. H., Berbee, M., Sosef, M. N., Schreurs, W. M. J., van Elmpt, W.= , Lambin, P. (2017). 4DCT imaging to assess radiomics feature = stability: An investigation for thoracic cancers. Radioth= erapy and Oncology. DOI: 10.1016/j.radon= c.2017.07.023
Sutton, E. J., Huang, E. P., Drukker, K., Burnside, E. S., Li, H., N= et, J. M., Rao, A., Whitman, G. J., Zuley, M., Ganott, M., Bonaccio, E., Gi= ger, M. L., Morris, E. A. (2017). Breast MRI radiomics: Compar= ison of computer- and human-extracted imaging phenotypes. European Radiology Experimental. DOI: 10.1186/s= 41747-017-0025-2
Vani, N., Swomya, A., & Jayamma, N. (2017). MRI Bra= in tumor classification using support vector machine. Int= ernational Research Journal of Engineering and Technology, 1724-1729. = DOI: 10.1109/SCEECS.2014.6804439
Kaur, T., Saini, B.S., Gupta, S. (2016). A joint intens= ity and edge magnitude-based multilevel thresholding algorithm for the auto= matic segmentation of pathological MR brain images. Neural Com= puting and Applications. 1-24. DOI: 10.1007/s00521-= 016-2751-4
Song, J., Liu, Z., Zhong, W., Huang, Y., Ma, Z., Dong, D., Liang, C.= , Tian, J. (2016). Non-small cell lung cancer: quantitative ph= enotypic analysis of CT images as a potential marker of prognosis.= Scientific Reports. 6:38282:1-9. DOI: 10.1038/srep3828= 2
Crawford, L., Monod, A., Chen, A.X., Mukherjee, S., Rabad=C3=A1n, R.= (2016). Topological Summaries of Tumor Images Improve Predict= ion of Disease Free Survival in Glioblastoma Multiforme. arXiv pre= print arXiv:161106818.
Zheng, C., Wang, X., Feng, D. (Eds.). (2016). Topology = guided demons registration with local rigidity preservation. 2= 016 IEEE 38th Annual International Conference Engineering in = Medicine and Biology Society (EMBC). IEEE. DOI: 10.1109/EMBC.2016.7590913
Kotrotsou, A., Zinn, P.O., Colen, R.R. (2016). Radiomic= s in Brain Tumors: An Emerging Technique for Characterization of Tumor Envi= ronment. Magnetic Resonance Imaging Clinics of North America. = 24(4); 719-29. DOI: 10.1016/j.mric.2016.06.006<= /a>
Parmar, C., Leijenaar, R.T.H., Grossmann, P., Valazquez, E.R., Bussi= nk, J., Rietveld, D., Rietbergen, M.M., Haibe-Kains, B., Lambin, P., Aerts,= H.J.W.L. (2015). Radiomic feature clusters and Prognostic Signatur= es specific for Lung and Head &Neck cancer.Scientific Repo= rts. 5(11044) DOI: 10.1038/srep11044
Dhara, A.K., Mukhopadhyay, S., Alam, N., Khandelwal, N. (2013). =
;Measurement of spiculation index in 3D for solitary pulmonary nodu=
les in volumetric lung CT images. Medical Imaging 2013: C=
omputer-Aided Diagnosis, 8670. DOI: 10.1117/12=
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Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, Samaras D, Shroy= er KR, Zhao T, Batiste R, Van Arnam J, Cancer Genome Atlas Research N, Shmu= levich I, Rao AUK, Lazar AJ, Sharma A, Thorsson V. Spatial Organization and= Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learnin= g on Pathology Images. Cell Rep.
2018;23(1):181-93 e7. DOI: http://doi.org/10.1016/j.celrep.2018.03.086Men, K., Geng, H., Cheng, C., Zhong, H., Huang, M., Fan, Y., Plastar= as, J. P., Lin, A., Xiao, Y. (2018). More accurate and efficient se= gmentation of organs-at-risk in radiotherapy with Convolutional Neural Netw= orks Cascades. Medical Physics. DOI: 10.1002/mp= .13296
Edalati-rad, A., & Mosleh, M. (2018). Improving brain tu= mor diagnosis using MRI segmentation based on collaboration of beta mixture= model and learning automata. Arabian Journal for Science and = Engineering, 1-13. DOI:10.1007/s13369-018-3320-1
Taghanaki, S. A., Duggan, M., Ma, H., Hou, X., Celler, A., Benard, F= ., Hamarneh, G. (2017). Segmentation-free direct tumor volume and m= etabolic activity estimation from PET scans. Computerized Medi= cal Imaging and Graphics, 63, 53-56. DOI: 1= 0.1016/j.compmedimag.2017.12.004
Y Ren, J Ma, J Xiong, Y Chen, L Lu, J Zhao (2018) Improved F= alse Positive Reduction by Novel Morphological Features for Computer-Aided = Polyp Detection in CT Colonography. IEEE Journal of Biomedical and= Health Informatics. DOI: 10.1109/JBH= I.2018.2808199
Hostetter, J. M., Morrison, J. J., Morris, M., Jeudy, J., Wang, K. C= ., & Siegel, E. (2017). Personalizing lung cancer risk predicti= on and imaging follow-up recommendations using the National Lung Screening = Trial dataset. Journal of the American Medical Informatics Ass= ociation, 24(6), 1046-1051. DOI:10.1093/jamia/ocx012
Hsieh KL-C, Tsai R-J, Teng Y-C, Lo C-M. Effect of a computer= -aided diagnosis system on radiologists' performance in grading gliomas wit= h MRI. PloS one. 2017;12(2):e0171342 (lin= k)
Hsieh KL-C, Lo C-M, Hsiao C-J. Computer-aided grading of gli= omas based on local and global MRI features. Computer Methods = and Programs in Biomedicine. 2017;139:31-8. DOI: 10.1016/j.cmpb.2016.10.021
Yang H, Liu F, Wang Z, Tang H, Sun S, Sun S. Research on the= Content-Based Classification of Medical Image. Journal of Med= ical Imaging and Health Informatics. 2017;7(1):129-36. (link)
Rezaie AA, Habiboghli A. Detection of Lung Nodules on Medica= l Images by the Use of Fractal Segmentation. International Jou= rnal of Interactive Multimedia and Artificial Inteligence. 2017;4(Spec= ial Issue on 3D Medicine and Artificial Intelligence):15-9. (link)
Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, Wang G. Low-= dose CT via convolutional neural network. Biomedical Optics Expres= s. 2017;8(2):679-94.(link)
Roth HR, Lu L, Seff A, Cherry KM, Hoffman J, Wang S, Liu J, Turkbey = E, Summers RM. A new 2.5 D representation for lymph node detection = using random sets of deep convolutional neural network observations. Medical Image Computing and Computer-Assisted Intervention=E2=80= =93MICCAI 2014: Springer; 2014. p. 520-7.
Seff A, Lu L, Cherry KM, Roth HR, Liu J, Wang S, Hoffman J, Turkbey = EB, Summers RM. 2d view aggregation for lymph node detection using = a shallow hierarchy of linear classifiers. Medical Image Com= puting and Computer-Assisted Intervention=E2=80=93MICCAI 2014: Springer; 20= 14. p. 544-52.
Agostinelli F, Anderson MR, Lee H, editors. Robust Image Den= oising with Multi-Column Deep Neural Networks. Advances in Neural = Information Processing Systems; 2013.
Kumar, D., A. Wong, et al. (2015). Lung Nodule Classificatio= n Using Deep Features in CT Images. Computer and Robot Vision (CRV= ), 2015 12th Conference on, IEEE.
Kanas, V. G., E. I. Zacharaki, et al. (2015). "A low cost ap= proach for brain tumor segmentation based on intensity modeling and 3D Rand= om Walker." Biomedical Signal Processing and Control 22: 19-30.
Magdy, E., N. Zayed, et al. (2015). "Automatic Classificatio= n of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features." International Journal of Biomedical Imaging 2015.
Zayed, N. and H. A. Elnemr (2015). "Statistical Analysis of = Haralick Texture Features to Discriminate Lung Abnormalities." Int= ernational Journal of Biomedical Imaging 2015.
Jaffray D, Chung C, Coolens C, Foltz W, Keller H, Menard C, Milosevi= c M, Publicover J, Yeung I, editors. Quantitative imaging in radiat= ion oncology: An emerging science and clinical service. Seminars i= n Radiation Oncology; 2015: Elsevier.
Jonathan Hugh Mason.(2018) Quantitative Cone-Beam Computed Tomography Reconstruction for Radioth= erapy Planning . University of Edinburgh. (link to thesis)
Golan, R. (2018). DeepCADe: A deep learning architecture for= the detection of lung nodules in CT scans. (li= nk to thesis)
Gro=C3=9Fmann, P. B. H. J. (2018) Defining the biologic= al and clinical basis of radiomics: towards clinical imaging biomarkers. = strong>Datawyse / Universitaire Pers Maastricht. DOI: 10.2= 6481/dis.20180308pg (lin= k to thesis)
Androutsou, T. Clinical Decision Support System for Lung Can= cer Diagnosis by analysis of thoracic CT images. Carrier NTU= A, Department of Electrical and Computer Engineering 2017. (link to thesis)
=Albalooshi FA. Self-organizing Approach to Learn a Level-set= Function for Object Segmentation in Complex Background Environments. University of Dayton; 2015. (link to thesis)
Camlica Z. Image Area Reduction for Efficient Medical Image = Retrieval. Waterloo, Ontario, Canada,: University of Waterloo; 201= 5. (link to thesis)
Karnayana PM. Radiogenomic correlation for prognosis in pati= ents with glioblastoma multiformae. San Diego State University; 20= 13. (link t= o thesis)
Nabizadeh, N. Automated Brain Lesion Detection and Segmentat= ion Using Magnetic Resonance Images. Electrical and Computer Engin= eering. Miami, FL, University of Miami. PhD., 2015. (link to thesis)
Wieser, H.-P. Supervised Machine Learning Approach Uti= lizing Artificial Neural Networks for Automated Prostate Zone Segmentation = in Abdominal MR images. Klagenfurt, Austria, Fachhochschule K=C3= =A4rnten/Carinthia University of Applied Sciences; 2013.(link to thesis)
Fedorov A, Schwier M, Clunie D, Herz C, Pieper S, Kikinis R, Tempany= C, Fennessy F. (2018) An annotated test-retest collection of prostate multiparametric = MRI Scientific Data 5:180281.( link&n= bsp;)
Gevaert O, Mitchell LA, et al. (2014). Glioblastoma multiforme: exploratory radiog= enomic analysis by using quantitative image features (TCGA-GBM-QI-Radiogeno= mics). TCIA. Saint Louis, MO. (link)
Gutman DA, Cooper LA, et al. (2014). MR Imaging Predictors of Molecular Profile an= d Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set (TC= GA-GBM-Radiogenomics). TCIA. Saint Louis, MO. (link)
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 Loui= s, MO.
Jain R, Poisson LM, et al. (2014). Outcome Prediction in Patients with Glioblastom= a by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhan= cing Component of the Tumor (GBM-MR-NER-Outcomes). TCIA. Saint= Louis, MO. (link)
Kalpathy-Cramer J, Napel S, et al. (2015). QIN multi-site collection of Lung = CT data with Nodule Segmentations (QIN-LungCT-Seg). TCIA. Sain= t Louis, MO. (link)
Messay T, Hardie RC, et al. (2014). Segmentation of Pulmonar= y Nodules in Computed Tomography Using a Regression Neural Network Approach= and its Application to the Lung Image Database Consortium and Image Databa= se Resource Initiative Dataset. TCIA. Saint Louis, MO. (link)
Roth H, Lu L, et al. (2015). A new 2.5D representation for l= ymph node detection in CT. TCIA. Saint Louis, MO. (link)
Shinagare AB, Vikram R, et al. (2015). Radiogenomics of Clea= r Cell Renal Cell Carcinoma: Preliminary Findings of The Cancer Genome Atla= s-Renal Cell Carcinoma (TCGA-RCC) Research Group. TCIA. Saint Loui= s, MO. (link)
Valli=C3=A8res M, Freeman CR, et al. (2015). Data from:= A radiomics model from joint FDG-PET and MRI texture features for the pred= iction of lung metastases in soft-tissue sarcomas of the extremities. TCIA. Saint Louis, MO. (link)<= /p>
Farahani K, Kalpathy-Cramer J, Chenevert TL, et al. Computat= ional Challenges and Collaborative Projects in the NCI Quantitative Imaging= Network. Tomography, 2016;2(4):242-9. DOI: = 10.18383/j.tom.2016.00265)
Kalpathy-Cramer J, Mamomov A, Zhao B,et al.. Radiomics of Lu= ng Nodules: A Multi-Institutional Study of Robustness and Agreement of Quan= titative Imaging Features. Tomography,2016;2(4):430-7. doi: 10.18383/j.tom.2016.00235.
Huang, W., X. Li, et al. (2014). "Variations of dynamic cont= rast-enhanced magnetic resonance imaging in evaluation of breast cancer the= rapy response: a multicenter data analysis challenge." Transl Onco= l 7(1): 153-166. (li= nk)
Kalpathy-Cramer J, Freymann JB, Kirby JS, et al. Quanti= tative Imaging Network: Data Sharing and Competitive Algorithm Validation L= everaging The Cancer Imaging Archive Translational Oncology. = 2014 Feb;7(1):147-52. DOI: 10.1593/tlo.13862. (link)
Lin AY, Du P, Dinning PG, Arkwright JW, Kamp JP, Cheng LK, Bissett I= P, O'Grady G. High resolution anatomic correlation of cyclic motor = patterns in the human colon: Evidence of a rectosigmoid brake. American Journal of Physiology-Gastrointestinal and Liver Physiology.= 2017;312(5):G508-G15. DOI: 10.1152/ajpgi.00021.2017= a>.
Gayathri DK, Radhakrishnan R, Rajamani K. Segmentation of co= lon and removal of opacified fluid for virtual colonoscopy. Patter= n Analysis and Applications. 2017:1-15. DOI: 10.1007/= s10044-017-0614-y
Gruselius, H. (2018). Generative models and feature extracti= on on patient images and structure data in radiation therapy. Retr= ieved from htt= p://kth.diva-portal.org/smash/record.jsf?pid=3Ddiva2%3A1215620&dswid=3D= 2429
Scarpelli, M., Eickhoff, J., Cuna, E., Perlman, S., & Jeraj, R. = (2018). Optimal transformations leading to normal distributions of = positron emission tomography standardized uptake values. Physi= cs in Medicine & Biology, 63(3), 035021. DOI: 10= .1088/1361-6560/aaa175
Ryalat MH, Laycock S, Fisher M, editors. Automatic Removal o= f Mechanical Fixations from CT Imagery with Particle Swarm Optimisation. International Conference on Bioinformatics and Biomedical Engineerin= g; 2017: Springer. DOI: 10.1007/978-3-319-56148-6_= 37
graph=E2=80=90cuts incorporating shape prior and motion from= 4D CT. Medical physics. 2018;45(1):297-306. doi: 1= 0.1002/mp.12690.
Agnes, S. A., Anitha, J., & Peter, J. D. (2018). Automat= ic lung segmentation in low-dose chest CT scans using convolutional deep an= d wide network (CDWN). Neural Computing and Applications. DOI:&nbs= p; 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
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These refer to the Mouse-Mammary Colle= ction data, created before submission to TCIA
Please see List o= f NLST Publications at NIH to browse publications from this Data Collec= tion.
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https://urldefense.proofpoint.com/v2/url?u=3Dhttp-3A__scholar.google.com= _scholar-5Furl-3Furl-3Dhttps-3A__www.frontiersin.org_articles_10.3389_fonc.= 2018.00630_abstract-26hl-3Den-26sa-3DX-26d-3D17035574034759440838-26scisig-= 3DAAGBfm1WQblT5q86dJspvDOqRRu8PSVU7Q-26nossl-3D1-26oi-3Dscholaralrt-26hist-= 3DJZvdUd4AAAAJ-3A7503576860592939312-3AAAGBfm0I-5FIWDH8Wn1Tp7HZ80hnE7g7d1KA= &d=3DDwMFaQ&c=3D27AKQ-AFTMvLXtgZ7shZqsfSXu-Fwzpqk4BoASshREk&r= =3DXmScNBe7GWfThvx7zB1VFtrnLEkFz09aDyXm0X5WSKk&m=3DEheQAEDzYBdj1Hi3Mz5Q= J6rjUvgeeGX13fmu6tj4P8I&s=3DI20gao5lOIC7cbYpu_7balkGSnBxbKOecD5UOqTXN44= &e=3D