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Yearb Med Inform 2017; 26(01): 110-119
DOI: 10.15265/IY-2017-041
Section 4: Sensor, Signal and Imaging Informatics
Survey
Georg Thieme Verlag KG Stuttgart

An Assessment of Imaging Informatics for Precision Medicine in Cancer

C. Chennubhotla
1   Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
,
L. P. Clarke
2   Cancer Imaging Program, NCI, NIH, Bethesda, MD, USA
,
A. Fedorov
3   Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
,
D. Foran
4   Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, New Brunswick, NJ, USA
,
G. Harris
5   Harvard Medical School, Boston, MA, USA
,
E. Helton
6   Center for Biomedical Informatics and Information Technology, NCI, NIH, Bethesda, MD, USA
,
R. Nordstrom
2   Cancer Imaging Program, NCI, NIH, Bethesda, MD, USA
,
F. Prior
7   Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
,
D. Rubin
8   Department of Radiology, Stanford University, Palo Alto, CA, USA
,
J. H. Saltz
9   Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
,
E. Shalley
2   Cancer Imaging Program, NCI, NIH, Bethesda, MD, USA
,
A. Sharma
10   Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
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Publikationsverlauf

Publikationsdatum:
11. September 2017 (online)

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Summary

Objectives: Precision medicine requires the measurement, quantification, and cataloging of medical characteristics to identify the most effective medical intervention. However, the amount of available data exceeds our current capacity to extract meaningful information. We examine the informatics needs to achieve precision medicine from the perspective of quantitative imaging and oncology.

Methods: The National Cancer Institute (NCI) organized several workshops on the topic of medical imaging and precision medicine. The observations and recommendations are summarized herein.

Results: Recommendations include: use of standards in data collection and clinical correlates to promote interoperability; data sharing and validation of imaging tools; clinician’s feedback in all phases of research and development; use of open-source architecture to encourage reproducibility and reusability; use of challenges which simulate real-world situations to incentivize innovation; partnership with industry to facilitate commercialization; and education in academic communities regarding the challenges involved with translation of technology from the research domain to clinical utility and the benefits of doing so.

Conclusions: This article provides a survey of the role and priorities for imaging informatics to help advance quantitative imaging in the era of precision medicine. While these recommendations were drawn from oncology, they are relevant and applicable to other clinical domains where imaging aids precision medicine.

Keywords

Quantitative imaging - precision medicine - Big Data - information dissemination/methods - diagnostic imaging/trends
 

  • References

  • 1 Ashley EA. The precision medicine initiative: a new national effort. JAMA 2015; 313 (21) 2119-20.
    Suche in Google Scholar
  • 2 National Research Council. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. Washington (DC): 2011
    Suche in Google Scholar
  • 3 Hsu W, Markey MK, Wang MD. Biomedical imaging informatics in the era of precision medicine: progress, challenges, and opportunities. J Am Med Inform Assoc 2013; 20 (06) 1010-3.
    Suche in Google Scholar
  • 4 Yankeelov TE, Mankoff DA, Schwartz LH, Lieberman FS, Buatti JM, Mountz JM. et al. (2016) Quantitative Imaging in Cancer Clinical Trials. Clin Cancer Res 2016; 22 (02) 284-90.
    Suche in Google Scholar
  • 5 NRC. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. National Academies Press; (US): 2011
    Suche in Google Scholar
  • 6 Thrall JH. Moreton Lecture: Imaging in the Age of Precision Medicine. J Am Coll Radiol 2015; 12 (10) 1106-11.
    Suche in Google Scholar
  • 7 Herold CJ, Lewin JS, Wibmer AG, Thrall JH, Krestin GP, Dixon AK. et al. Imaging in the Age of Precision Medicine: Summary of the Proceedings of the 10th Biannual Symposium of the International Society for Strategic Studies in Radiology. Radiology 2016; 279 (01) 226-38.
    Suche in Google Scholar
  • 8 Thrall JH. Personalized medicine. Radiology 2004; 231 (03) 613-6.
    Suche in Google Scholar
  • 9 Sutton EJ, Oh JH, Dashevsky BZ, Veeraraghavan H, Apte AP, Thakur SB. et al. Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay. J Magn Reson Imaging 2015; 42 (05) 1398-406.
    Suche in Google Scholar
  • 10 Cooper LA, Kong J, Gutman DA, Wang F, Gao J, Appin C, et al. Integrated morphologic analysis for the identification and characterization of disease subtypes. J Am Med Inform Assoc 2012; 19 (02) 317-23.
    Suche in Google Scholar
  • 11 de la Fuente MI, Young RJ, Rubel J, Rosenblum M, Tisnado J, Briggs S, et al. Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-mutant glioma. Neuro Oncol 2016; 18 (02) 283-90.
    Suche in Google Scholar
  • 12 Siddiqui MM, Truong H, Rais-Bahrami S, Stamatakis L, Logan J, Walton-Diaz A. et al. Clinical implications of a multiparametric magnetic resonance imaging based nomogram applied to prostate cancer active surveillance. J Urol 2015; 193 (06) 1943-9.
    Suche in Google Scholar
  • 13 Giardino A, Gupta S, Olson E, Sepulveda K, Lenchik L, Ivanidze J. et al. Role of Imaging in the Era of Precision Medicine. Acad Radiol 2017; 24 (05) 639-49.
    Suche in Google Scholar
  • 14 Sullivan DC, Obuchowski NA, Kessler LG, Raunig DL, Gatsonis C, Huang EP, Group R-QMW. et al. Metrology Standards for Quantitative Imaging Biomarkers. Radiology 2015; 277 (03) 813-25.
    Suche in Google Scholar
  • 15 Raunig DL, McShane LM, Pennello G, Gatsonis C, Carson PL, Voyvodic JT, Group QTPW. et al. Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment. Stat Methods Med Res 2015; 24 (01) 27-67.
    Suche in Google Scholar
  • 16 Nordstrom RJ. The Quantitative Imaging Network in Precision Medicine. Tomography 2016; 02 (04) 239-41.
    Suche in Google Scholar
  • 17 Mangla R, Singh G, Ziegelitz D, Milano MT, Korones DN, Zhong J. et al. Changes in Relative Cerebral Blood Volume 1 Month after Radiation-Temozolomide Therapy Can Help Predict Overall Survival in Patients with Glioblastoma. Radiology 2010; 256 (02) 575-84.
    Suche in Google Scholar
  • 18 Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 05: 4006.
    Suche in Google Scholar
  • 19 Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB. et al. Radiomics: the process and the challenges. Magn Reson Imaging 2012; 30 (09) 1234-48.
    Suche in Google Scholar
  • 20 Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48 (04) 441-6.
    Suche in Google Scholar
  • 21 Buckler AJ, Bresolin L, Dunnick NR, Sullivan DC. Group Ft. Quantitative Imaging Test Approval and Biomarker Qualification: Interrelated but Distinct Activities. Radiology 2011; 259 (03) 875-84.
    Suche in Google Scholar
  • 22 Clarke LP, Croft BS, Nordstrom R, Zhang H, Kelloff G, Tatum J. Quantitative imaging for evaluation of response to cancer therapy. Transl Oncol 2009; 02 (04) 195.
    Suche in Google Scholar
  • 23 Clarke LP, Sriram RD, Schilling LB. Imaging as a biomarker: standards for change measurements in therapy workshop summary. Acad Radiol 2008; 15 (04) 501-30.
    Suche in Google Scholar
  • 24 Eliceiri KW, Berthold MR, Goldberg IG, Ibanez L, Manjunath BS, Martone ME. et al. Biological imaging software tools. Nat Meth 2012; 09 (07) 697-710.
    Suche in Google Scholar
  • 25 Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S. et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012; 30 (09) 1323-41.
    Suche in Google Scholar
  • 26 Gatenby RA, Grove O, Gillies RJ. Quantitative Imaging in Cancer Evolution and Ecology. Radiology 2013; 269 (01) 8-14.
    Suche in Google Scholar
  • 27 Gillies R. Radiomics: informing cancer heterogeneity. J Nucl Med 2013; 54: 31.
    Suche in Google Scholar
  • 28 Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage 2009; 48 (01) 63.
    Suche in Google Scholar
  • 29 Hunter L. Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction. MS: University of Texas Graduate School of Biomedical Sciences at Houston; 2013
    Suche in Google Scholar
  • 30 Kalpathy-Cramer J, Freymann JB, Kirby JS, Kinahan PE, Prior FW. Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive. Transl Oncol 2014; 07 (01) 147-52.
    Suche in Google Scholar
  • 31 Kuo MD, Gollub J, Sirlin CB, Ooi C, Chen X. Radiogenomic Analysis to Identify Imaging Phenotypes Associated with Drug Response Gene Expression Programs in Hepatocellular Carcinoma. J Vasc Interv Radiol 2007; 18 (07) 821-30.
    Suche in Google Scholar
  • 32 Rutman AM, Kuo MD. Radiogenomics: Creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol 2009; 70 (02) 232-41.
    Suche in Google Scholar
  • 33 Sivakumar S, Chandrasekar C. Lung Nodule Detection Using Fuzzy Clustering and Support Vector Machines. International Journal of Engineering and Technology 2013; 05: 179-85.
    Suche in Google Scholar
  • 34 Veiga C, McClelland J, Moinuddin S, Laurenco A, Ricketts K, Modat M. et al. Adaptive radiotherapy for head and neck patients: evaluation of a deformable registration-based “dose of the day” calculation. International Journal of Radiation: Oncology-Biology-Physics. 2013
    Suche in Google Scholar
  • 35 Waterton JC, Pylkkanen L. Qualification of imaging biomarkers for oncology drug development. E J Cancer 2012; 48 (04) 409-15.
    Suche in Google Scholar
  • 36 Gurcan MN, Pan T, Shimada H, Saltz J. Image analysis for neuroblastoma classification: segmentation of cell nuclei. Conf Proc IEEE Eng Med Biol Soc 2006; 01: 4844-7.
    Suche in Google Scholar
  • 37 Ross JC, Estepar RS, Diaz A, Westin CF, Kikinis R, Silverman EK. et al. Lung extraction, lobe segmentation and hierarchical region assessment for quantitative analysis on high resolution computed tomography images. Med Image Comput Comput Assist Interv 2009; 12 (Pt 2) 690-8.
    Suche in Google Scholar
  • 38 Sertel O, Kong J, Shimada H, Catalyurek UV, Saltz JH, Gurcan MN. Computer-aided Prognosis of Neuroblastoma on Whole-slide Images: Classification of Stromal Development. Pattern Recognit 2009; 02 (06) 1093-103.
    Suche in Google Scholar
  • 39 Gutman DA, Cooper LA, Hwang SN, Holder CA, Gao J, Aurora TD. et al. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 2013; 267 (02) 560-9.
    Suche in Google Scholar
  • 40 Velazquez ER, Parmar C, Jermoumi M, Mak RH, van Baardwijk A, Fennessy FM. et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Scientific Reports 2013; 03: 3529.
    Suche in Google Scholar
  • 41 Colen R, Foster I, Gatenby R, Giger ME, Gillies R, Gutman D. et al. NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures. Transl Oncol 2014; 07 (05) 556-69.
    Suche in Google Scholar
  • 42 Parmar C, Rios EVelazquez, Leijenaar R, Jermoumi M, Carvalho S, Mak RH. et al. Robust Radiomics feature quantification using semiautomatic volumetric segmentation. PloS One 2014; 09 (07) e102107.
    Suche in Google Scholar
  • 43 Coroller TP, Grossmann P, Hou Y, Rios EVelazquez, Leijenaar RT, Hermann G. et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 2015; 114 (03) 345-50.
    Suche in Google Scholar
  • 44 Parmar C, Leijenaar RT, Grossmann P, Rios EVelazquez, Bussink J, Rietveld D. et al. Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer. Sci Rep 2015; 05: 11044.
    Suche in Google Scholar
  • 45 Basavanhally AN, Ganesan S, Agner S, Monaco JP, Feldman MD, Tomaszewski JE. et al. Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology. IEEE Trans Biomed Eng 2010; 57 (03) 642-53.
    Suche in Google Scholar
  • 46 Chen W, Chu V, Hu J, Yang L, Wang F, Kurc T. et al. ImageMiner: a medical image analysis and image management UML data model. APIII: Advancing Practice, Instruction & Innovation Through Informatics; Pittsburgh, PA: 2009
    Suche in Google Scholar
  • 47 Cholleti S, Cooper L, Kong J, Chisolm C, Brat D, Gutman D. et al. Classification of brain tumor regions. Boston, MA: Pathology Informatics; 2010
    Suche in Google Scholar
  • 48 Cooper L, Kong J, Gutman D, Wang F, Cholleti S, Pan T. et al. An Integrative Approach for In Silico Glioma Research. IEEE Trans Biomed Eng 2010; 57 (10) 2617-21.
    Suche in Google Scholar
  • 49 Cooper L, Kong J, Gutman D, Wang F, Cholleti S, Pan T. et al. Integrative Analysis of Image and Molecular Data for Study of Brain Tumors. The NCI-NCRI Informatics Initiative Joint Conference: Biomedical Informatics without Borders: Implementing Interoperability; Bethesda, MD: 2010
    Suche in Google Scholar
  • 50 Cooper LAD, Carter AB, Farris AB, Wang F, Kong J, Gutman DA. et al. Digital Pathology: Data Intensive Frontier in Medical Imaging. Proc IEEE Inst Electr Electron Eng 2012; 100 (04) 991-1003.
    Suche in Google Scholar
  • 51 Foran DJ, Yang L, Chen W, Hu J, Goodell LA, Reiss M. et al. ImageMiner: a software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology. J Am Med Inform Assoc 2011; 18 (04) 403-15.
    Suche in Google Scholar
  • 52 Fuchs TJ, Buhmann JM. Computational pathology: Challenges and promises for tissue analysis. Comput Med Imaging Grap 2011; 35 (07) 515-30.
    Suche in Google Scholar
  • 53 Gao Y, Tannenbaum A. Combining Atlas and Active Contour for Automatic 3d Medical Image Segmentation. Proc IEEE Int Symp Biomed Imaging. 2011: 1401-4.
    Suche in Google Scholar
  • 54 Gao Y, Tannenbaum A, Chen H, Torres M, Yoshida E, Yang XF. et al. Automated Skin Segmentation in Ultrasonic Evaluation of Skin Toxicity in Breast Cancer Radiotherapy. Ultrasound Med Biol 2013; 39 (11) 2166-75.
    Suche in Google Scholar
  • 55 Gurcan MN, Boucheron L, Can A, Madabhushi A, Rajpoot N, Yener B. Histopathological Image Analysis: A Review. IEEE Rev Biomed Eng 2009; 02: 147-71.
    Suche in Google Scholar
  • 56 Huang K, Mosaliganti K, Cooper LAD, Machiraju R. Quantitative phenotyping using microscopic images. In: Rittscher J, Machiraju R, Wong STC. (eds) Microscopic Image Analysis for Life Science Applications. Boston, MA, USA: Artech House; 2008
    Suche in Google Scholar
  • 57 Huang PW, Lee CH. Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans Med Imaging 2009; 28 (07) 1037-50.
    Suche in Google Scholar
  • 58 Kong J, Sertel O, Shimada H, Boyer K, Saltz J, Gurcan M. Computer-aided grading of neuroblastic differentiation: Multi-resolution and multi-classifier approach. IEEE International Conference on Image Processing. 2007 05. V-525
    Suche in Google Scholar
  • 59 Lu C, Mahmood M, Jha N, Mandal M. Automated Segmentation of the Melanocytes in Skin Histopathological Images. IEEE J Biomed Health Inform 2013; 17 (02) 284-96.
    Suche in Google Scholar
  • 60 Lu C, Mandal M. Automated Segmentation and Analysis of the Epidermis Area in Skin Histopathological Images. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2012: 5355-9.
    Suche in Google Scholar
  • 61 Martone ME, Zhang S, Gupta A, Qian X, He H, Price DL. et al. The cell-centered database: a database for multiscale structural and protein localization data from light and electron microscopy. Neuroinformatics 2003; 01 (04) 379-95.
    Suche in Google Scholar
  • 62 McKeen-Polizzotti L, Henderson KM, Oztan B, Bilgin CC, Yener B, Plopper GE. Quantitative metric profiles capture three-dimensional temporospatial architecture to discriminate cellular functional states. BMC Medical Imaging 2011; 11 (01) 11.
    Suche in Google Scholar
  • 63 Michailovitch O, Rathi Y, Tannenbaum A. Image segmentation using active contours driven by the Bhattacharyya gradient flow. IEEE Trans Image Processing 2007; 16: 2787-801.
    Suche in Google Scholar
  • 64 Monaco J, Tomaszewski J, Feldman M, Moradi M, Mousavi P, Boag A. et al. Detection of prostate cancer from whole-mount histology images using Markov random fields. Workshop on Microscopic Image Analysis with Applications in Biology (in conjunction with MICCAI). 2008
    Suche in Google Scholar
  • 65 Oztan B, Kong H, Gürcan MN, Yener B. Follicular lymphoma grading using cell-graphs and multi-scale feature analysis. Proc SPIE 2012; 8315: 831516-831511.
    Suche in Google Scholar
  • 66 Phan J, Quo C, Cheng C, Wang M. Multi-Scale Integration of-omic, Imaging, and Clinical Data in Biomedical Informatics. IEEE Rev Biomed Eng 2012; 05: 74-87.
    Suche in Google Scholar
  • 67 Pieper S, Lorensen B, Schroeder W, Kikinis R. The NA-MIC Kit: ITK, VTK, pipelines, grids and 3D slicer as an open platform for the medical image computing community. 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro. 2016: 698-701.
    Suche in Google Scholar
  • 68 Qi X, Kim H, Xing F, Parashar M, Foran DJ, Yang L. The analysis of image feature robustness using CometCloud. J Pathol Inform. 2012: 3-33.
    Suche in Google Scholar
  • 69 Song Y, Treanor D, Bulpitt AJ, Magee DR. 3D reconstruction of multiple stained histology images. J Pathol Inform 2013; 04 (Suppl) S7.
    Suche in Google Scholar
  • 70 Tay CH. Algorithms for Tissue Image Analysis using Multifractal Techniques. Master of Science Thesis. University of Canterbury. Computer Science and Software Engineering. 2012
    Suche in Google Scholar
  • 71 Viros A, Fridlyand J, Bauer J, Lasithiotakis K, Garbe C, Pinkel D. et al. Improving melanoma classification by integrating genetic and morphologic features. PLoS Medicine 2008; 05 (06) e120.
    Suche in Google Scholar
  • 72 Yang L, Tuzel O, Chen W, Meer P, Salaru G, Goodell LA. et al. PathMiner: a web-based tool for computer-assisted diganostics in pathology. IEEE Trans Inf Technol Biomed 2009; 13 (03) 291-99.
    Suche in Google Scholar
  • 73 Zhu LJ, Gao Y, Appia V, Yezzi A, Arepalli C, Faber T. et al. Automatic Delineation of the Myocardial Wall From CT Images Via Shape Segmentation and Variational Region Growing. IEEE Trans Biomed Eng 2013; 60 (10) 2887-95.
    Suche in Google Scholar
  • 74 Grove O, Berglund AE, Schabath MB, Aerts HJ, Dekker A, Wang H. et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PloS One 2015; 10 (03) e0118261.
    Suche in Google Scholar
  • 75 Ghaznavi F, Evans A, Madabhushi A, Feldman M. Digital imaging in pathology: whole-slide imaging and beyond. Annu Rev Pathol 2013; 08: 331-59.
    Suche in Google Scholar
  • 76 Gurcan MN, Madabhushi A. Digital Pathology. Proc SPIE 2013; 8676: 867601-867601.
    Suche in Google Scholar
  • 77 Wang C, Pécot T, Zynger DL, Machiraju R, Shapiro CL, Huang K. Identifying survival associated morphological features of triple negative breast cancer using multiple datasets. J Am Med Inform Assoc 2013; 20 (04) 680-7.
    Suche in Google Scholar
  • 78 Hamilton PW, Bankhead P, Wang Y, Hutchinson R, Kieran D, McArt DG. et al. Digital pathology and image analysis in tissue biomarker research. Methods 2014; 70 (01) 59-73.
    Suche in Google Scholar
  • 79 Gurcan MN, Madabhushi A. Medical Imaging 2015: Digital Pathology. Proc SPIE 2015; 9420: 942001-942001.
    Suche in Google Scholar
  • 80 Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ. et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med 2011; 03 (108) 108ra113.
    Suche in Google Scholar
  • 81 Dong F, Irshad H, Oh EY, Lerwill MF, Brachtel EF, Jones NC. et al. Computational pathology to discriminate benign from malignant intraductal proliferations of the breast. PloS One 2014; 09 (12) e114885.
    Suche in Google Scholar
  • 82 Qi X, Wang D, Rodero I, Diaz-Montes J, Gensure RH, Xing F. et al. Content-based histopathology image retrieval using CometCloud. BMC Bioinformatics 2014; 15: 287.
    Suche in Google Scholar
  • 83 Hai S, Yong S, Fuyong X, Xin Q, Hirshfeld KM, Lin Y. et al. Robust automatic breast cancer staging using a combination of functional genomics and imageomics. Conf Proc IEEE Eng Med Biol Soc. 2015: 7226-9.
    Suche in Google Scholar
  • 84 Cheng J, Hipp J, Monaco J, Lucas DR, Madabhushi A, Balis UJ. Automated vector selection of SIVQ and parallel computing integration MATLAB: Innovations supporting large-scale and high-throughput image analysis studies. J Pathol Inform 2011; 02: 37.
    Suche in Google Scholar
  • 85 Hipp J, Smith SC, Cheng J, Tomlins SA, Monaco J, Madabhushi A. et al. Optimization of complex cancer morphology detection using the SIVQ pattern recognition algorithm. Anal Cell Pathol (Amsterdam) 2012; 35 (01) 41-50.
    Suche in Google Scholar
  • 86 Gevaert O, Xu J, Hoang CD, Leung AN, Xu Y, Quon A. et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. Radiology 2012; 264 (02) 387-96.
    Suche in Google Scholar
  • 87 Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ. Machine Learning methods for Quantitative Radiomic Biomarkers. Sci Rep 2015; 05: 13087.
    Suche in Google Scholar
  • 88 Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B. et al. DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ 2016; 04: e2057.
    Suche in Google Scholar
  • 89 Boettiger C. An introduction to Docker for reproducible research. ACM SIGOPS Operating Systems Review 2015; 49 (01) 71-9.
    Suche in Google Scholar
  • 90 NCI Genomic Cloud Pilots.
  • 91 Jacobsen DM, Canon RS. Contain this, unleashing docker for hpc. Proceedings of the Cray User Group. 2015
    Suche in Google Scholar
  • 92 Roelofs E, Dekker A, Meldolesi E, van Stiphout RGPM, Valentini V, Lambin P. International data-sharing for radiotherapy research: An open-source based infrastructure for multicentric clinical data mining. Radiother Oncol 2014; 110 (02) 370-4.
    Suche in Google Scholar
  • 93 Goode A, Gilbert B, Harkes J, Jukic D, Satyanarayanan M. OpenSlide: A vendor-neutral software foundation for digital pathology. J Pathol Inform 2013; 04: 27.
    Suche in Google Scholar
  • 94 Toga AW. 2012; The clinical value of large neuroimaging data sets in Alzheimer’s disease. Neuroimaging clinics of North America 22 (01) 107.
    Suche in Google Scholar
  • 95 Grethe JS, Baru C, Gupta A, James M, Ludaescher B, Martone ME. et al. Biomedical informatics research network: building a national collaboratory to hasten the derivation of new understanding and treatment of disease. Stud Health Technol Inform 2005; 112: 100-10.
    Suche in Google Scholar
  • 96 Marcus D, Harwell J, Olsen T, Hodge M, Glasser M, Prior F. et al. Informatics and Data Mining Tools and Strategies for the Human Connectome Project. Front Neuroinform 2011; 05 (04) 1-12.
    Suche in Google Scholar
  • 97 Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL. Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults. J Cogn Neurosci 2007; 19 (09) 1498-507.
    Suche in Google Scholar
  • 98 Hall D, Huerta MF, McAuliffe MJ, Farber GK. Sharing heterogeneous data: the national database for autism research. Neuroinformatics 2012; 10 (04) 331-9.
    Suche in Google Scholar
  • 99 Korfiatis PD, Kline TL, Blezek DJ, Langer SG, Ryan WJ, Erickson BJ. MIRMAID: A Content Management System for Medical Image Analysis Research. Radiographics 2015; 35 (05) 1461-8.
    Suche in Google Scholar
  • 100 Moore SM, Maffitt DR, Smith KE, Kirby JS, Clark KW, Freymann JB. et al. De-identification of Medical Images with Retention of Scientific Research Value. Radiographics 2015; 35 (03) 727-35.
    Suche in Google Scholar
  • 101 Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S. et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012; 30 (09) 1323-41.
    Suche in Google Scholar
  • 102 Grand Challenges in Biomedical Image Analysis. https://grand-challenge.org/ Accessed 2017-07-01.
  • 103 Cooper LA, Kong J, Gutman DA, Wang F, Cholleti SR, Pan TC. et al. An integrative approach for in silico glioma research. IEEE Trans Biomed Eng 2010; 57 (10) 2617-21.
    Suche in Google Scholar
  • 104 Wang C, Pecot T, Zynger DL, Machiraju R, Shapiro CL, Huang K. Identifying survival associated morphological features of triple negative breast cancer using multiple datasets. J Am Med Inform Assoc 2013; 20 (04) 680-7.
    Suche in Google Scholar
  • 105 Doi K, Giger ML, Nishikawa RM, Hoffmann KR, Macmahon H, Schmidt RA, et al. Digital Radiography. A useful clinical tool for computer-aided diagnosis by quantitative analysis of radiographic images. Acta Radiol 1993; 34 (05) 426-39.
    Suche in Google Scholar
  • 106 Lo S-CB, Lin J-S, Freedman MT, Mun SK. Computer-assisted diagnosis of lung nodule detection using artificial convoultion neural network. Medical Imaging 1993. International Society for Optics and Photonics. 1993: 859-69.
    Suche in Google Scholar
  • 107 Sahiner B, Chan HP, Petrick N, Wei D, Helvie MA, Adler DD. et al. Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging 1996; 15 (05) 598-610.
    Suche in Google Scholar
  • 108 Greenspan H, Ginneken Bv, Summers RM. Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Trans Med Imaging 2016; 35 (05) 1153-9.
    Suche in Google Scholar
  • 109 Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J. et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 2015; 34 (10) 1993-2024.
    Suche in Google Scholar
  • 110 Bhargava R, Madabhushi A. Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology. Annu Rev Biomed Eng 2016; 18: 387-412.
    Suche in Google Scholar
  • 111 Mongkolwat P, Kleper V, Talbot S, Rubin D. The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation model. J Digit Imaging 2014; 27 (06) 692-701.
    Suche in Google Scholar
  • 112 Müller H, Kalpathy-Cramer J, Hanbury A, Farahani K, Sergeev R, Paik JH. et al. Report on the Cloud-Based Evaluation Approaches Workshop 2015. ACM SIGIR Forum 2016; 50: 38-41.
    Suche in Google Scholar
  • 113 Moreira DA, Hage C, Luque EF, Willrett D, Rubin DL. 3D markup of radiological images in ePAD, a web-based image annotation tool. Computer-Based Medical Systems (CBMS), 2015 IEEE 28th International Symposium on. IEEE. 2015: 97-102.
    Suche in Google Scholar
  • 114 Belmann P, Dröge J, Bremges A, McHardy AC, Sczyrba A, Barton MD. Bioboxes: standardised containers for interchangeable bioinformatics software. Gigascience 2015; 04 (01) 47.
    Suche in Google Scholar
  • 115 Rubin DL. The electronic Imaging Physician Annotation Device (ePAD). 2017
    Suche in Google Scholar
  • 116 Keller BM, Chen J, Daye D, Conant EF, Kontos D. Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography. Breast Cancer Res 2015; 17: 117.
    Suche in Google Scholar
  • 117 Toolkit DD. DCMTK. 2010
    Suche in Google Scholar
  • 118 Torvalds L, Hamano J. Git: Fast version control system. 2010 URL http://git-scm.com
    Suche in Google Scholar
  • 119 Merkel D. Docker: lightweight linux containers for consistent development and deployment. Linux Journal 2014; (239) 2.
    Suche in Google Scholar
  • 120 Di Tommaso P, Palumbo E, Chatzou M, Prieto P, Heuer ML, Notredame C. The impact of Docker containers on the performance of genomic pipelines. PeerJ 2015; 03: e1273.
    Suche in Google Scholar
  • 121 Fridsma DB, Evans J, Hastak S, Mead CN. The BRIDG project: a technical report. J Am Med Inform Assoc 2008; 15 (02) 130-137.
    Suche in Google Scholar
  • 122 Singer DS, Jacks T, Jaffee E. 2016; A US “Cancer Moonshot” to accelerate cancer research. Science 353 6304 1105-6.
    Suche in Google Scholar
  • 123 Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA. et al. Toward a shared vision for cancer genomic data. New Engl J Med 2016; 375 (12) 1109-12.
    Suche in Google Scholar
  • 124 caMicroscope - A Web Based Annotation and Visualization Platform for Digitized Whole Slide Images. https://github.com/camicroscope/caMicroscope Accessed 2017-07-01.