1. Gillies RJ,
Kinahan PE,
Hricak H.
Radiomics: Images Are More than Pictures,
They Are Data.
Radiology.
2015:151169.
2. Yip SS,
Aerts HJ.
Applications and limitations of radiomics.
Physics in medicine and biology.
2016;61(13):R150-66.
3. Lee G,
Lee HY,
Park H,
Schiebler ML,
van Beek EJ,
Ohno Y,
et al.
Radiomics and its emerging role in lung cancer research,
imaging biomarkers and clinical management: State of the art.
European journal of radiology.
2016.
4. Islam KM,
Jiang X,
Anggondowati T,
Lin G,
Ganti AK.
Comorbidity and Survival in Lung Cancer Patients.
Cancer epidemiology,
biomarkers & prevention: a publication of the American Association for Cancer Research,
cosponsored by the American Society of Preventive Oncology.
2015;24(7):1079-85.
5. Tammemagi CM,
Neslund-Dudas C,
Simoff M,
Kvale P.
Impact of comorbidity on lung cancer survival.
International journal of cancer Journal international du cancer.
2003;103(6):792-802.
6. European Science Foundation.
Personalised Medicine for the European Citizen: towards more precise medicine for the diagnosis,
treatment and prevention of disease (iPM).2012 Accessed: 19.
September 2016.
Available from: http://www.esf.org/fileadmin/Public_documents/Publications/Personalised_Medicine.pdf.
7. OECD (2016),
Computed tomography (CT) exams (indicator).
doi: 10.1787/3c994537-en (Accessed on 11 December 2016).
8. Kumar V,
Gu Y,
Basu S,
Berglund A,
Eschrich SA,
Schabath MB,
et al.
Radiomics: the process and the challenges.
Magnetic resonance imaging.
2012;30(9):1234-48.
9. 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.
European journal of cancer.
2012;48(4):441-6.
10. Ledley RS,
Huang HK,
Rotolo LS.
A texture analysis method in classification of coal workers' pneumoconiosis.
Computers in biology and medicine.
1975;5(1-2):53-67.
11. Mackin D,
Fave X,
Zhang L,
Fried D,
Yang J,
Taylor B,
et al.
Measuring Computed Tomography Scanner Variability of Radiomics Features.
Investigative radiology.
2015;50(11):757-65.
12. Parmar C,
Grossmann P,
Bussink J,
Lambin P,
Aerts HJ.
Machine Learning methods for Quantitative Radiomic Biomarkers.
Scientific reports.
2015;5:13087.
13. Johkoh T,
Muller NL,
Cartier Y,
Kavanagh PV,
Hartman TE,
Akira M,
et al.
Idiopathic interstitial pneumonias: diagnostic accuracy of thin-section CT in 129 patients.
Radiology.
1999;211(2):555-60.
14. Xu Y,
van Beek EJ,
Hwanjo Y,
Guo J,
McLennan G,
Hoffman EA.
Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM).
Academic radiology.
2006;13(8):969-78.
15. 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.
Nature communications.
2014;5:4006.
16. Bayanati H,
R ET,
Souza CA,
Sethi-Virmani V,
Gupta A,
Maziak D,
et al.
Quantitative CT texture and shape analysis: can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer? European radiology.
2015;25(2):480-7.
17. Ostridge K,
Williams N,
Kim V,
Bennett M,
Harden S,
Welch L,
et al.
Relationship between pulmonary matrix metalloproteinases and quantitative CT markers of small airways disease and emphysema in COPD.
Thorax.
2016;71(2):126-32.
18. Jairam PM,
van der Graaf Y,
Lammers JW,
Mali WP,
de Jong PA,
group PS.
Incidental findings on chest CT imaging are associated with increased COPD exacerbations and mortality.
Thorax.
2015;70(8):725-31.
19. Pompe E,
de Jong PA,
de Jong WU,
Takx RA,
Eikendal AL,
Willemink MJ,
et al.
Inter-observer and inter-examination variability of manual vertebral bone attenuation measurements on computed tomography.
European radiology.
2016;26(9):3046-53.
20. Takasu M,
Yamagami T,
Nakamura Y,
Komoto D,
Kaichi Y,
Tani C,
et al.
Multidetector computed tomography-based microstructural analysis reveals reduced bone mineral content and trabecular bone changes in the lumbar spine after transarterial chemoembolization therapy for hepatocellular carcinoma.
PloS one.
2014;9(10):e110106.
21. Zayed N,
Elnemr HA.
Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities.
International journal of biomedical imaging.
2015;2015:267807.
22. Yoon RG,
Seo JB,
Kim N,
Lee HJ,
Lee SM,
Lee YK,
et al.
Quantitative assessment of change in regional disease patterns on serial HRCT of fibrotic interstitial pneumonia with texture-based automated quantification system.
European radiology.
2013;23(3):692-701.
23. Colombi D,
Dinkel J,
Weinheimer O,
Obermayer B,
Buzan T,
Nabers D,
et al.
Visual vs Fully Automatic Histogram-Based Assessment of Idiopathic Pulmonary Fibrosis (IPF) Progression Using Sequential Multidetector Computed Tomography (MDCT).
PloS one.
2015;10(6):e0130653.
24. Chong DY,
Kim HJ,
Lo P,
Young S,
McNitt-Gray MF,
Abtin F,
et al.
Robustness-Driven Feature Selection in Classification of Fibrotic Interstitial Lung Disease Patterns in Computed Tomography Using 3D Texture Features.
IEEE transactions on medical imaging.
2016;35(1):144-57.
25. Daginawala N,
Li B,
Buch K,
Yu H,
Tischler B,
Qureshi MM,
et al.
Using texture analyses of contrast enhanced CT to assess hepatic fibrosis.
European journal of radiology.
2016;85(3):511-7.
26. Zhang X,
Gao X,
Liu BJ,
Ma K,
Yan W,
Liling L,
et al.
Effective staging of fibrosis by the selected texture features of liver: Which one is better,
CT or MR imaging? Computerized medical imaging and graphics: the official journal of the Computerized Medical Imaging Society.
2015;46 Pt 2:227-36.
27. Gill CM,
Torriani M,
Murphy R,
Harris TB,
Miller KK,
Klibanski A,
et al.
Fat Attenuation at CT in Anorexia Nervosa.
Radiology.
2016;279(1):151-7.
28. Lee JJ,
Pedley A,
Hoffmann U,
Massaro JM,
Keaney JF,
Jr.,
Vasan RS,
et al.
Cross-Sectional Associations of Computed Tomography (CT)-Derived Adipose Tissue Density and Adipokines: The Framingham Heart Study.
Journal of the American Heart Association.
2016;5(3):e002545.
29. Shah RV,
Allison MA,
Lima JA,
Abbasi SA,
Eisman A,
Lai C,
et al.
Abdominal fat radiodensity,
quantity and cardiometabolic risk: The Multi-Ethnic Study of Atherosclerosis.
Nutrition,
metabolism,
and cardiovascular diseases: NMCD.
2016;26(2):114-22.
30. European COPD Coalition.
2016; http://www.copdcoalition.eu/about-copd/prevalence.
Accessed 12 Dec,
2016.
31. Mohamed Hoesein FA,
de Jong PA.
Landmark papers in respiratory medicine: Automatic quantification of emphysema and airways disease on computed tomography.
Breathe.
2016;12(1):79-81.
32. Mets OM,
Smit EJ,
Mohamed Hoesein FA,
Gietema HA,
Bokkers RP,
Attrach M,
et al.
Visual versus automated evaluation of chest computed tomography for the presence of chronic obstructive pulmonary disease.
PloS one.
2012;7(7):e42227.
33. Mets OM,
de Jong PA,
van Ginneken B,
Gietema HA,
Lammers JW.
Quantitative computed tomography in COPD: possibilities and limitations.
Lung.
2012;190(2):133-45.
34. O'Neill TW,
Felsenberg D,
Varlow J,
Cooper C,
Kanis JA,
Silman AJ.
The prevalence of vertebral deformity in european men and women: the European Vertebral Osteoporosis Study.
Journal of bone and mineral research: the official journal of the American Society for Bone and Mineral Research.
1996;11(7):1010-8.
35. Cheng XG,
Nicholson PH,
Boonen S,
Lowet G,
Brys P,
Aerssens J,
et al.
Prediction of vertebral strength in vitro by spinal bone densitometry and calcaneal ultrasound.
Journal of bone and mineral research: the official journal of the American Society for Bone and Mineral Research.
1997;12(10):1721-8.
36. Kanis JA,
Johnell O,
Oden A,
De Laet C,
Jonsson B,
Dawson A.
Ten-year risk of osteoporotic fracture and the effect of risk factors on screening strategies.
Bone.
2002;30(1):251-8.
37. NIH Consensus Development Panel on Osteoporosis Prevention,
Diagnosis,
and Therapy,
March 7-29,
2000: highlights of the conference.
Southern medical journal.
2001;94(6):569-73.
38. Hussein AI,
Morgan EF.
The effect of intravertebral heterogeneity in microstructure on vertebral strength and failure patterns.
Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA.
2013;24(3):979-89.
39. Kim YW,
Kim JH,
Yoon SH,
Lee JH,
Lee CH,
Shin CS,
et al.
Vertebral bone attenuation on low-dose chest CT: quantitative volumetric analysis for bone fragility assessment.
Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA.
2016.
40. Oncology society rolls out big-data initiative,
tells why radiology should care,
www.imagingbiz.com/topics/imaging-informatics 2015.
41. Smith BM,
Barr RG.
Establishing normal reference values in quantitative computed tomography of emphysema.
Journal of thoracic imaging.
2013;28(5):280-3.
42. Gallardo-Estrella L,
Lynch DA,
Prokop M,
Stinson D,
Zach J,
Judy PF,
et al.
Normalizing computed tomography data reconstructed with different filter kernels: effect on emphysema quantification.
European radiology.
2016;26(2):478-86.
43. Choo JY,
Goo JM,
Lee CH,
Park CM,
Park SJ,
Shim MS.
Quantitative analysis of emphysema and airway measurements according to iterative reconstruction algorithms: comparison of filtered back projection,
adaptive statistical iterative reconstruction and model-based iterative reconstruction.
European radiology.
2014;24(4):799-806.
44. Kim H,
Park CM,
Park SJ,
Song YS,
Lee JH,
Hwang EJ,
et al.
Temporal Changes of Texture Features Extracted From Pulmonary Nodules on Dynamic Contrast-Enhanced Chest Computed Tomography: How Influential Is the Scan Delay? Investigative radiology.
2016;51(9):569-74.
45. Lo P,
Young S,
Kim HJ,
Brown MS,
McNitt-Gray MF.
Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features.
Medical physics.
2016;43(8):4854.
46. Kim H,
Park CM,
Lee M,
Park SJ,
Song YS,
Lee JH,
et al.
Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability.
PloS one.
2016;11(10):e0164924.
47. Chalkidou A,
O'Doherty MJ,
Marsden PK.
False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review.
PloS one.
2015;10(5):e0124165.
48. Lambin P.
Radiomics Digital Phantom,
https://www.cancerdata.org/resource/doi:10.17195/candat.2016.08.1.
2016.
49. The radiomics quality score,
http://www.radiomics.org/?q=drupalform/form2 (Accessed on 11 December 2016).
50. Parmar C,
Rios Velazquez E,
Leijenaar R,
Jermoumi M,
Carvalho S,
Mak RH,
et al.
Robust Radiomics feature quantification using semiautomatic volumetric segmentation.
PloS one.
2014;9(7):e102107.
51. Parmar C,
Grossmann P,
Rietveld D,
Rietbergen MM,
Lambin P,
Aerts HJ.
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer.
Frontiers in oncology.
2015;5:272.
52. Coroller TP,
Grossmann P,
Hou Y,
Rios Velazquez E,
Leijenaar RT,
Hermann G,
et al.
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.
Radiotherapy and oncology: journal of the European Society for Therapeutic Radiology and Oncology.
2015;114(3):345-50.
53. Huang Y,
Liu Z,
He L,
Chen X,
Pan D,
Ma Z,
et al.
Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.
Radiology.
2016:152234.
54. Patz EF,
Jr.,
Caporaso NE,
Dubinett SM,
Massion PP,
Hirsch FR,
Minna JD,
et al.
National Lung Cancer Screening Trial American College of Radiology Imaging Network Specimen Biorepository originating from the Contemporary Screening for the Detection of Lung Cancer Trial (NLST,
ACRIN 6654): design,
intent,
and availability of specimens for validation of lung cancer biomarkers.
Journal of thoracic oncology: official publication of the International Association for the Study of Lung Cancer.
2010;5(10):1502-6.
55. Armato SG,
3rd,
McLennan G,
Bidaut L,
McNitt-Gray MF,
Meyer CR,
Reeves AP,
et al.
The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.
Medical physics.
2011;38(2):915-31.
56. Depeursinge A,
Vargas A,
Platon A,
Geissbuhler A,
Poletti PA,
Muller H.
Building a reference multimedia database for interstitial lung diseases.
Computerized medical imaging and graphics: the official journal of the Computerized Medical Imaging Society.
2012;36(3):227-38.
57. Ru Zhao Y,
Xie X,
de Koning HJ,
Mali WP,
Vliegenthart R,
Oudkerk M.
NELSON lung cancer screening study.
Cancer imaging: the official publication of the International Cancer Imaging Society.
2011;11 Spec No A:S79-84.
58. Pastorino U,
Rossi M,
Rosato V,
Marchiano A,
Sverzellati N,
Morosi C,
et al.
Annual or biennial CT screening versus observation in heavy smokers: 5-year results of the MILD trial.
European journal of cancer prevention: the official journal of the European Cancer Prevention Organisation.
2012;21(3):308-15.
59. Sanchez-Salcedo P,
Berto J,
de-Torres JP,
Campo A,
Alcaide AB,
Bastarrika G,
et al.
Lung cancer screening: fourteen year experience of the Pamplona early detection program (P-IELCAP).
Archivos de bronconeumologia.
2015;51(4):169-76.
60. Pedersen JH,
Ashraf H,
Dirksen A,
Bach K,
Hansen H,
Toennesen P,
et al.
The Danish randomized lung cancer CT screening trial--overall design and results of the prevalence round.
Journal of thoracic oncology: official publication of the International Association for the Study of Lung Cancer.
2009;4(5):608-14.
61. Bild DE,
Bluemke DA,
Burke GL,
Detrano R,
Diez Roux AV,
Folsom AR,
et al.
Multi-Ethnic Study of Atherosclerosis: objectives and design.
American journal of epidemiology.
2002;156(9):871-81.
62. Framingham Heart Study.
A Project of the National Heart,
Lung,
and Blood Institute and Boston University; https://www.framinghamheartstudy.org/.
Accessed 23 Sep,
2016.
63. Lung Tissue Research Consortium.
A program of the National Heart,
Lung,
and Blood Institute; https://ltrcpublic.com/.
Accessed 10 Dec,
2016.