Population-based imaging biobanks as source of big data

Gatidis, Sergios; Heber, Sophia D.; Storz, Corinna; Bamberg, Fabian

doi:10.1007/s11547-016-0684-8

Cited by 11 publications

(9 citation statements)

References 35 publications

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“…The wide implementation of SR is critical for providing referring physicians and patients with the best quality of service, and for providing researchers with best quality data in the context of the big data exploitation of available clinical information [ 51 , 52 , 53 , 54 ]. Implementation is complex, requiring mature technology to successfully address pending user-friendliness, organizational and interoperability challenge (with particular regard to the adequate storage of data, and easy and adequate connections with PACS and post-processing software).…”

Section: Discussionmentioning

confidence: 99%

Structured Reporting of Lung Cancer Staging: A Consensus Proposal

et al. 2021

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Background: Structured reporting (SR) in radiology is becoming necessary and has recently been recognized by major scientific societies. This study aimed to build CT-based structured reports for lung cancer during the staging phase, in order to improve communication between radiologists, members of the multidisciplinary team and patients. Materials and Methods: A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi exercise was used to build the structural report and to assess the level of agreement for all the report sections. The Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to perform a quality analysis according to the average inter-item correlation. Results: The final SR version was built by including 16 items in the “Patient Clinical Data” section, 4 items in the “Clinical Evaluation” section, 8 items in the “Exam Technique” section, 22 items in the “Report” section, and 5 items in the “Conclusion” section. Overall, 55 items were included in the final version of the SR. The overall mean of the scores of the experts and the sum of scores for the structured report were 4.5 (range 1–5) and 631 (mean value 67.54, STD 7.53), respectively, in the first round. The items of the structured report with higher accordance in the first round were primary lesion features, lymph nodes, metastasis and conclusions. The overall mean of the scores of the experts and the sum of scores for staging in the structured report were 4.7 (range 4–5) and 807 (mean value 70.11, STD 4.81), respectively, in the second round. The Cronbach’s alpha (Cα) correlation coefficient was 0.89 in the first round and 0.92 in the second round for staging in the structured report. Conclusions: The wide implementation of SR is critical for providing referring physicians and patients with the best quality of service, and for providing researchers with the best quality of data in the context of the big data exploitation of the available clinical data. Implementation is complex, requiring mature technology to successfully address pending user-friendliness, organizational and interoperability challenges.

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Section: Discussionmentioning

confidence: 99%

Structured Reporting of Lung Cancer Staging: A Consensus Proposal

et al. 2021

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“…Body imaging, especially MRI with its benefit of no radiation exposure, has become an integral component in several epidemiological cohort studies including primarily healthy subjects, such as the German National Cohort or the UK Biobank aiming for 30,000 and 100,000 whole-body MR scans, respectively. 25 Thus, MR-based assessment of epicardial fat would potentially provide an important, imaging-based risk marker for cardiovascular disease. We found that manual assessment of the cardiac fat depots in the cine long-axis SSFP sequence (4-chamber view)…”

Section: Discussionmentioning

confidence: 99%

“…However, MRI has rarely been used for the assessment of epicardial fat in study cohort settings, which traditionally employ either ultrasound-based echocardiography 23 or CT. 3,4,24 Nevertheless, MRI-particular whole-body MRI-is more and more frequently applied in epidemiological cohort studies like the German National Cohort or UK Biobank. 25 In this context, the possibility of radiation-free assessment of the cardiac fat depots by MRI becomes feasible.…”

Section: Objectivementioning

confidence: 99%

MRI-based assessment and characterization of epicardial and paracardial fat depots in the context of impaired glucose metabolism and subclinical left-ventricular alterations

Rado¹,

Lorbeer

Gatidis³

et al. 2019

The British Journal of Radiology

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IntroDuctIonSeveral studies have investigated distinct local fat depots, since these show stronger associations with traditional risk factors and/or cardiovascular disease as compared to anthropometric measurements of obesity; 1,2 most promising evidence has been reported for assessing abdominal visceral adipose tissue (VAT) 1 and epicardial fat, 3,4 which is located within the visceral layer of the pericardium.

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“…Clearly enough, IB development is destined to proceed in parallel with the growth of the imaging biobanking field [29][30][31]. Here, diagnostic multimodal images generated by various technologies will be increasingly exploited by high-throughput computing to extract radiomic features, although translational gaps remain to be dealt with before achieving validation in clinical settings.…”

Section: Imaging Biobankingmentioning

confidence: 99%

From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health

Capobianco

Dominietto

2020

JPM

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Treating disease according to precision health requires the individualization of therapeutic solutions as a cardinal step that is part of a process that typically depends on multiple factors. The starting point is the collection and assembly of data over time to assess the patient’s health status and monitor response to therapy. Radiomics is a very important component of this process. Its main goal is implementing a protocol to quantify the image informative contents by first mining and then extracting the most representative features. Further analysis aims to detect potential disease phenotypes through signs and marks of heterogeneity. As multimodal images hinge on various data sources, and these can be integrated with treatment plans and follow-up information, radiomics is naturally centered on dynamically monitoring disease progression and/or the health trajectory of patients. However, radiomics creates critical needs too. A concise list includes: (a) successful harmonization of intra/inter-modality radiomic measurements to facilitate the association with other data domains (genetic, clinical, lifestyle aspects, etc.); (b) ability of data science to revise model strategies and analytics tools to tackle multiple data types and structures (electronic medical records, personal histories, hospitalization data, genomic from various specimens, imaging, etc.) and to offer data-agnostic solutions for patient outcomes prediction; (c) and model validation with independent datasets to ensure generalization of results, clinical value of new risk stratifications, and support to clinical decisions for highly individualized patient management.

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Population-based imaging biobanks as source of big data

Cited by 11 publications

References 35 publications

Structured Reporting of Lung Cancer Staging: A Consensus Proposal

Structured Reporting of Lung Cancer Staging: A Consensus Proposal

MRI-based assessment and characterization of epicardial and paracardial fat depots in the context of impaired glucose metabolism and subclinical left-ventricular alterations

From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health

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