Literature DB >> 27139177

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

C L Schlett1, T Hendel2, S Weckbach1, M Reiser2, H U Kauczor1, K Nikolaou3, M Günther4, M Forsting5, N Hosten6, H Völzke7, F Bamberg3.   

Abstract

UNLABELLED: 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. © Georg Thieme Verlag KG Stuttgart · New York.

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Year:  2016        PMID: 27139177     DOI: 10.1055/s-0042-104510

Source DB:  PubMed          Journal:  Rofo        ISSN: 1438-9010


  12 in total

Review 1.  MRI adipose tissue and muscle composition analysis-a review of automation techniques.

Authors:  Magnus Borga
Journal:  Br J Radiol       Date:  2018-07-24       Impact factor: 3.039

2.  Automated MR-based lung volume segmentation in population-based whole-body MR imaging: correlation with clinical characteristics, pulmonary function testing and obstructive lung disease.

Authors:  Jan Mueller; Stefan Karrasch; Roberto Lorbeer; Tatyana Ivanovska; Andreas Pomschar; Wolfgang G Kunz; Ricarda von Krüchten; Annette Peters; Fabian Bamberg; Holger Schulz; Christopher L Schlett
Journal:  Eur Radiol       Date:  2018-08-27       Impact factor: 5.315

3.  Population-based imaging biobanks as source of big data.

Authors:  Sergios Gatidis; Sophia D Heber; Corinna Storz; Fabian Bamberg
Journal:  Radiol Med       Date:  2016-09-09       Impact factor: 3.469

4.  Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan.

Authors:  Roberta Maggio; Filippo Messina; Benedetta D'Arrigo; Giacomo Maccagno; Pina Lardo; Claudia Palmisano; Maurizio Poggi; Salvatore Monti; Iolanda Matarazzo; Andrea Laghi; Giuseppe Pugliese; Antonio Stigliano
Journal:  Front Endocrinol (Lausanne)       Date:  2022-06-17       Impact factor: 6.055

5.  Population-Based Magnetic Resonance Imaging: Earlier Detection of Hypertensive Cerebral Small Vessel Disease?

Authors:  Malvika Kaul; Israel Rubinstein
Journal:  Hypertension       Date:  2021-07-07       Impact factor: 9.897

Review 6.  Introduction to Radiomics.

Authors:  Marius E Mayerhoefer; Andrzej Materka; Georg Langs; Ida Häggström; Piotr Szczypiński; Peter Gibbs; Gary Cook
Journal:  J Nucl Med       Date:  2020-02-14       Impact factor: 11.082

7.  Paediatric population neuroimaging and the Generation R Study: the second wave.

Authors:  Tonya White; Ryan L Muetzel; Hanan El Marroun; Laura M E Blanken; Philip Jansen; Koen Bolhuis; Desana Kocevska; Sabine E Mous; Rosa Mulder; Vincent W V Jaddoe; Aad van der Lugt; Frank C Verhulst; Henning Tiemeier
Journal:  Eur J Epidemiol       Date:  2017-10-24       Impact factor: 8.082

8.  Body Composition Profiling in the UK Biobank Imaging Study.

Authors:  Jennifer Linge; Magnus Borga; Janne West; Theresa Tuthill; Melissa R Miller; Alexandra Dumitriu; E Louise Thomas; Thobias Romu; Patrik Tunón; Jimmy D Bell; Olof Dahlqvist Leinhard
Journal:  Obesity (Silver Spring)       Date:  2018-05-22       Impact factor: 5.002

9.  Innovative approaches for cancer treatment: current perspectives and new challenges.

Authors:  Carlotta Pucci; Chiara Martinelli; Gianni Ciofani
Journal:  Ecancermedicalscience       Date:  2019

10.  Vertebral Bone Marrow Fat Is independently Associated to VAT but Not to SAT: KORA FF4-Whole-Body MR Imaging in a Population-Based Cohort.

Authors:  Dunja Hasic; Roberto Lorbeer; Robert C Bertheau; Jürgen Machann; Susanne Rospleszcz; Johanna Nattenmüller; Wolfgang Rathmann; Annette Peters; Fabian Bamberg; Christopher L Schlett
Journal:  Nutrients       Date:  2020-05-24       Impact factor: 5.717

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