Medical imaging in personalised medicine: a white paper of the research committee of the European Society of Radiology (ESR)

,

doi:10.1007/s13244-011-0125-0

Cited by 39 publications

(20 citation statements)

References 24 publications

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“…Reporting policies have been addressed from the European Society of Radiology [19] with specific statements on reporting of IF; in the current clinical setting, in fact, the introduction of structured reports and formatted templates could have a profound impact on increasing the detection rate of IF. Guidelines for the advice of further actions to be suggested in the presence of potentially clinically relevant IF may be more difficult to set up; the adoption of multidisciplinary clinico-radiological approaches could enhance the clinical efficacy with a patient-centred and personalised management [20]. …”

Section: Discussionmentioning

confidence: 99%

Extra-spinal incidental findings at lumbar spine MRI in the general population: a large cohort study

et al. 2013

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ObjectiveTo determine the prevalence of clinically and non-clinically relevant extra-spinal incidental findings (IF) in patients undergoing magnetic resonance imaging (MRI) of the lumbar spine and to evaluate the rate of undetected findings in archived radiological reports.MethodsA retrospective search of patients undergoing lumbar spine MRI from January 2006 to December 2010 was conducted. By means of randomisation, we retrospectively reviewed 3,000 lumbar spine MRI examinations. Extra-spinal abnormalities were classified according to a modified CT Colonography Reporting and Data System (C-RADS). We retrospectively compared our structured approach with the archived MRI reports as it regarded the detection of extra-spinal IF to estimate non-detection rates.ResultsBy means of the structured approach used, extra-spinal findings were detected in 2,060 (68.6 %) of the 3,000 lumbar spine MRI examinations; 362 (17.6 %) patients had indeterminate or clinically important findings (E3 and E4) requiring clinical correlation or further evaluation. After review of the original archived radiological reports, potentially important C-RADS E3 and E4 extra-spinal IF were respectively reported in 47 of the 265 (17.7 %) and in 8 of 74 (10.8 %) patients.ConclusionsOur study shows that incidental extra-spinal findings at conventional lumbar spine MRI are common but underestimated in radiological reports.

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Section: Discussionmentioning

confidence: 99%

Extra-spinal incidental findings at lumbar spine MRI in the general population: a large cohort study

et al. 2013

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“…There are two reasons for this: on the one hand the lack of technology to use heterogeneous and partly unstructured routine data for machine learning, and on the other hand the stumbling blocks of data management. Machine learning on this scale makes the integration of partially unstructured clinical information from patient records and imaging data necessary [4, 14], but to date technology for this is lacking. Both challenges are currently being tackled, and we can expect new kinds of evidence and robustness of prediction models once we are able to perform machine learning on this body of observations.…”

Section: The Role Of Routine Data In Machine Learningmentioning

confidence: 99%

Machine learning: from radiomics to discovery and routine

Langs

Röhrich²,

Hofmanninger³

et al. 2018

Radiologe

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Machine learning is rapidly gaining importance in radiology. It allows for the exploitation of patterns in imaging data and in patient records for a more accurate and precise quantification, diagnosis, and prognosis. Here, we outline the basics of machine learning relevant for radiology, and review the current state of the art, the limitations, and the challenges faced as these techniques become an important building block of precision medicine. Furthermore, we discuss the roles machine learning can play in clinical routine and research and predict how it might change the field of radiology.

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“…Several methods and probes are currently under investigation with questions still remaining regarding the choice of relevant imaging target and the timing of assessment [1, 158, 191]. …”

Section: Imaging Biomarkers For the “Hallmarks Of Cancer”mentioning

confidence: 99%

Biomarkers in preclinical cancer imaging

Bernsen

Kooiman

Segbers

et al. 2015

Eur J Nucl Med Mol Imaging

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In view of the trend towards personalized treatment strategies for (cancer) patients, there is an increasing need to noninvasively determine individual patient characteristics. Such information enables physicians to administer to patients accurate therapy with appropriate timing. For the noninvasive visualization of disease-related features, imaging biomarkers are expected to play a crucial role. Next to the chemical development of imaging probes, this requires preclinical studies in animal tumour models. These studies provide proof-of-concept of imaging biomarkers and help determine the pharmacokinetics and target specificity of relevant imaging probes, features that provide the fundamentals for translation to the clinic. In this review we describe biological processes derived from the “hallmarks of cancer” that may serve as imaging biomarkers for diagnostic, prognostic and treatment response monitoring that are currently being studied in the preclinical setting. A number of these biomarkers are also being used for the initial preclinical assessment of new intervention strategies. Uniquely, noninvasive imaging approaches allow longitudinal assessment of changes in biological processes, providing information on the safety, pharmacokinetic profiles and target specificity of new drugs, and on the antitumour effectiveness of therapeutic interventions. Preclinical biomarker imaging can help guide translation to optimize clinical biomarker imaging and personalize (combination) therapies.

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Medical imaging in personalised medicine: a white paper of the research committee of the European Society of Radiology (ESR)

Abstract: The future of medicine lies in early

Cited by 39 publications

References 24 publications

Extra-spinal incidental findings at lumbar spine MRI in the general population: a large cohort study

Extra-spinal incidental findings at lumbar spine MRI in the general population: a large cohort study

Machine learning: from radiomics to discovery and routine

Biomarkers in preclinical cancer imaging

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