Population-Based Imaging and Radiomics: Rationale and Perspective of the German National Cohort MRI Study

 

 Permissions and Reprints

Abstract

The MRI study within the German National Cohort, a large-scale, population-based, longitudinal study in Germany, comprises comprehensive characterization and phenotyping of a total of 30 000 participants using 3-Tesla whole-body MR imaging. A multi-centric study design was established together with dedicated core facilities for e. g. managing incidental findings or providing quality assurance. As such, the study represents a unique opportunity to substantially impact imaging-based risk stratification leading to personalized and precision medicine. Supported by the developments in the field of computational science, the newly developing scientific field of radiomics has large potential for the future. In the present article we provide an overview on population-based imaging and Radiomics and conceptualize the rationale and design of the MRI study within the German National Cohort.

Key Points:

• Population-based imaging and Radiomics constitute two emerging fields with great oppertunities and challenges for Radiology.

• As part of the MRI-study of the NAKO approximately 30 000 subjects will undergo 3 Tesla whole-body MRI.

• MR Imaging data is publicly accessable and will provide important insights into the natural history of disease processes and personalized risk profiles of the general population.

Citation Format:

• Schlett CL, Hendel T, Weckbach S et al. Population-Based Imaging and Radiomics: Rationale and Perspective of the German National Cohort MRI Study. Fortschr Röntgenstr 2016; 188: 652 – 661

Zusammenfassung

Die NAKO Studie, eine bundesdeutsche populationsbasierte Längsschnittstudie, umfasst neben anderen Untersuchungen eine umfangreiche Phänotypisierung von insgesamt etwa 30 000 Teilnehmern mittels 3 Tesla Ganzkörper-MRT. Hierzu wurde ein multizentrisches Design in Verbindung mit umfangreichen Elementen zur Qualitätssicherung und Algorithmen zum Umgang mit MRT-Zufallsergebnissen etabliert. Damit stellt die Studie eine einzigartige Möglichkeit dar, wesentliche Erkenntnisse im Bereich der bildbasierten Risikostratifizierung zu liefern, was zu einer personalisierten Medizin beiträgt. In Kombination mit den aktuellen Entwicklungen der Computerwissenschaften ergeben sich in dem sich entwickelnden radiologischen Schwerpunkt Radiomics Chancen für die Zukunft. Der vorliegende Artikel gibt einen Überblick über populationsbasierte Bildgebung und Radiomics im Allgemeinen und ordnet in diesen Kontext die Rationale und das Design der MRT-Studie der NAKO ein.

• References

• 1 [Anonym]. The German National Cohort: aims, study design and organization. Eur J Epidemiol 2014; 29: 371-382
  CrossrefPubMedGoogle Scholar
• 2 Bamberg F, Kauczor HU, Weckbach S et al. Whole-Body MR Imaging in the German National Cohort: Rationale, Design, and Technical Background. Radiology 2015; 277: 206-220
  CrossrefPubMedGoogle Scholar
• 3 Srivastava S, Gopal-Srivastava R. Biomarkers in cancer screening: a public health perspective. J Nutr 2002; 132: 2471S-2475S
  PubMedGoogle Scholar
• 4 Elmore JG, Armstrong K, Lehman CD et al. Screening for breast cancer. JAMA 2005; 293: 1245-1256
  CrossrefPubMedGoogle Scholar
• 5 Kumar V, Gu Y, Basu S et al. Radiomics: the process and the challenges. Magn Reson Imaging 2012; 30: 1234-1248
  CrossrefPubMedGoogle Scholar
• 6 Kwong RY, Farzaneh-Far A. Measuring myocardial scar by CMR. JACC Cardiovasc Imaging 2011; 4: 157-160
  CrossrefPubMedGoogle Scholar
• 7 [Anonym]. Medical imaging in personalised medicine: a white paper of the research committee of the European Society of Radiology (ESR). Insights Imaging 2015; 6: 141-155
  Google Scholar
• 8 [Anonym]. ESR Position Paper on Imaging Biobanks. Insights Imaging 2015; 6: 403-410
  CrossrefPubMedGoogle Scholar
• 9 Ahrens W, Jockel KH. The benefit of large-scale cohort studies for health research: the example of the German National Cohort. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2015; 58: 813-821
  CrossrefPubMedGoogle Scholar
• 10 Lambin P, Rios-Velazquez E, Leijenaar R et al. Radiomics: extracting more information from medical images using advanced feature analysis. European journal of cancer 2012; 48: 441-446
  CrossrefPubMedGoogle Scholar
• 11 Aerts HJ, Velazquez ER, Leijenaar RT et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5: 4006
  PubMedGoogle Scholar
• 12 Chicklore S, Goh V, Siddique M et al. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. European journal of nuclear medicine and molecular imaging 2013; 40: 133-140
  CrossrefPubMedGoogle Scholar
• 13 Khalvati F, Wong A, Haider MA. Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models. BMC medical imaging 2015; 15: 27
  CrossrefPubMedGoogle Scholar
• 14 Parmar C, Rios Velazquez E, Leijenaar R et al. Robust Radiomics feature quantification using semiautomatic volumetric segmentation. PloS one 2014; 9: e102107
  CrossrefPubMedGoogle Scholar
• 15 Velazquez ER, Parmar C, Jermoumi M et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Scientific reports 2013; 3: 3529
  CrossrefPubMedGoogle Scholar
• 16 Blaschke T, Hay GJ, Kelly M et al. Geographic Object-Based Image Analysis – Towards a new paradigm. ISPRS J Photogramm Remote Sens 2014; 87: 180-191
  CrossrefPubMedGoogle Scholar
• 17 Parmar C, Grossmann P, Bussink J et al. Machine Learning methods for Quantitative Radiomic Biomarkers. Scientific reports 2015; 5: 13087
  CrossrefPubMedGoogle Scholar
• 18 Volzke H, Schmidt CO, Hegenscheid K et al. Population imaging as valuable tool for personalized medicine. Clin Pharmacol Ther 2012; 92: 422-424
  CrossrefPubMedGoogle Scholar
• 19 Raiko JR, Magnussen CG, Kahonen M et al. Tracking of noninvasive ultrasound measurements of subclinical atherosclerosis in adulthood: findings from the Cardiovascular Risk in Young Finns Study. Ultrasound Med Biol 2010; 36: 1237-1244
  CrossrefPubMedGoogle Scholar
• 20 Wolff B, Volzke H, Ludemann J et al. Association between high serum ferritin levels and carotid atherosclerosis in the study of health in Pomerania (SHIP). Stroke 2004; 35: 453-457
  CrossrefPubMedGoogle Scholar
• 21 Bauer M, Hoffmann B, Mohlenkamp S et al. Distribution of carotid intima media thickness in men and women with and without coronary heart disease. Cross-sectional data of the Heinz Nixdorf Recall Study. Herz 2013; 38: 501-508
  CrossrefPubMedGoogle Scholar
• 22 Hofman A, van Duijn CM, Franco OH et al. The Rotterdam Study: 2012 objectives and design update. Eur J Epidemiol 2011; 26: 657-686
  CrossrefPubMedGoogle Scholar
• 23 Stuckey DJ, Carr CA, Tyler DJ et al. Cine-MRI versus two-dimensional echocardiography to measure in vivo left ventricular function in rat heart. NMR Biomed 2008; 21: 765-772
  CrossrefPubMedGoogle Scholar
• 24 Peters SA, Grobbee DE, Bots ML. Carotid intima-media thickness: a suitable alternative for cardiovascular risk as outcome?. Eur J Cardiovasc Prev Rehabil 2011; 18: 167-174
  CrossrefPubMedGoogle Scholar
• 25 [Anonym]. Vascular factors and risk of dementia: design of the Three-City Study and baseline characteristics of the study population. Neuroepidemiology 2003; 22: 316-325
  CrossrefPubMedGoogle Scholar
• 26 Ikram MA, van der Lugt A, Niessen WJ et al. The Rotterdam Scan Study: design update 2016 and main findings. Eur J Epidemiol 2015; 30: 1299-1315
  CrossrefPubMedGoogle Scholar
• 27 Bergstrom G, Berglund G, Blomberg A et al. The Swedish CArdioPulmonary BioImage Study: objectives and design. J Intern Med 2015; 278: 645-659
  CrossrefPubMedGoogle Scholar
• 28 Meijs MF, Bots ML, Vonken EJ et al. Rationale and design of the SMART Heart study: A prediction model for left ventricular hypertrophy in hypertension. Neth Heart J 2007; 15: 295-298
  CrossrefPubMedGoogle Scholar
• 29 Hetterich H, Bayerl C, Peters A et al. Feasibility of a three-step magnetic resonance imaging approach for the assessment of hepatic steatosis in an asymptomatic study population. Eur Radiol 2015; DOI: 10.1007/s00330-015-3966-y.
  PubMedGoogle Scholar
• 30 Schlemmer HP, Schafer J, Pfannenberg C et al. Fast whole-body assessment of metastatic disease using a novel magnetic resonance imaging system: initial experiences. Invest Radiol 2005; 40: 64-71
  CrossrefPubMedGoogle Scholar
• 31 Hegenscheid K, Kuhn JP, Volzke H et al. Whole-body magnetic resonance imaging of healthy volunteers: pilot study results from the population-based SHIP study. Fortschr Röntgenstr 2009; 181: 748-759
  Article in Thieme ConnectPubMedGoogle Scholar
• 32 Petersen SE, Matthews PM, Bamberg F et al. Imaging in population science: cardiovascular magnetic resonance in 100000 participants of UK Biobank – rationale, challenges and approaches. Journal of cardiovascular magnetic resonance: official journal of the Society for Cardiovascular Magnetic Resonance 2013; 15: 46
  CrossrefPubMedGoogle Scholar
• 33 Schlett CL, Hendel T, Hirsch J et al. Quantitative, Organ-Specific Interscanner and Intrascanner Variability for 3 T Whole-Body Magnetic Resonance Imaging in a Multicenter, Multivendor Study. Invest Radiol 2016; 51: 255-265
  CrossrefPubMedGoogle Scholar
• 34 Erbel R, Eisele L, Moebus S et al. The Heinz Nixdorf Recall study. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2012; 55: 809-815
  CrossrefPubMedGoogle Scholar
• 35 Harris TB, Launer LJ, Eiriksdottir G et al. Age, Gene/Environment Susceptibility-Reykjavik Study: multidisciplinary applied phenomics. Am J Epidemiol 2007; 165: 1076-1087
  CrossrefPubMedGoogle Scholar
• 36 Becker N, Motsch E, Gross ML et al. Randomized study on early detection of lung cancer with MSCT in Germany: study design and results of the first screening round. J Cancer Res Clin Oncol 2012; 138: 1475-1486
  CrossrefPubMedGoogle Scholar
• 37 Ru Zhao Y, Xie X, de Koning HJ et al. NELSON lung cancer screening study. Cancer Imaging 2011; 11: S79-S84
  CrossrefPubMedGoogle Scholar
• 38 Engelke K, Libanati C, Liu Y et al. Quantitative computed tomography (QCT) of the forearm using general purpose spiral whole-body CT scanners: accuracy, precision and comparison with dual-energy X-ray absorptiometry (DXA). Bone 2009; 45: 110-118
  CrossrefPubMedGoogle Scholar
• 39 Emaus N, Wilsgaard T, Ahmed LA. Impacts of body mass index, physical activity, and smoking on femoral bone loss: the Tromso study. J Bone Miner Res 2014; 29: 2080-2089
  CrossrefPubMedGoogle Scholar
• 40 Rothney MP, Brychta RJ, Schaefer EV et al. Body composition measured by dual-energy X-ray absorptiometry half-body scans in obese adults. Obesity (Silver Spring) 2009; 17: 1281-1286
  PubMedGoogle Scholar
• 41 Harvey NC, Matthews P, Collins R et al. Osteoporosis epidemiology in UK Biobank: a unique opportunity for international researchers. Osteoporos Int 2013; 24: 2903-2905
  CrossrefPubMedGoogle Scholar
• 42 Hegenscheid K, Seipel R, Schmidt CO et al. Potentially relevant incidental findings on research whole-body MRI in the general adult population: frequencies and management. Eur Radiol 2013; 23: 816-826
  CrossrefPubMedGoogle Scholar
• 43 Schmidt CO, Hegenscheid K, Erdmann P et al. Psychosocial consequences and severity of disclosed incidental findings from whole-body MRI in a general population study. Eur Radiol 2013; 23: 1343-1351
  CrossrefPubMedGoogle Scholar
• 44 Flanders AE. Medical image and data sharing: are we there yet?. Radiographics 2009; 29: 1247-1251
  CrossrefPubMedGoogle Scholar

• Supplementary Material