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Pediatric Radiology Jan 2020Pediatric radiology is an immensely rewarding career choice. Eight pediatric radiologists, enthusiastic for their profession, were asked six questions about their career... (Review)
Review
Pediatric radiology is an immensely rewarding career choice. Eight pediatric radiologists, enthusiastic for their profession, were asked six questions about their career choice. Their responses illustrate the common virtues of pediatric radiology and also demonstrate the diverse paths and activities that pediatric radiologists take and pursue.
Topics: Attitude of Health Personnel; Career Choice; Humans; Pediatrics; Radiologists; Radiology
PubMed: 31901987
DOI: 10.1007/s00247-019-04569-0 -
Journal of the American College of... Mar 2018Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks,... (Review)
Review
Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data. The ultimate benefit of quantitative radiomics is to (1) yield predictive image-based phenotypes of disease for precision medicine or (2) yield quantitative image-based phenotypes for data mining with other -omics for discovery (ie, imaging genomics). For deep learning in radiology to succeed, note that well-annotated large data sets are needed since deep networks are complex, computer software and hardware are evolving constantly, and subtle differences in disease states are more difficult to perceive than differences in everyday objects. In the future, machine learning in radiology is expected to have a substantial clinical impact with imaging examinations being routinely obtained in clinical practice, providing an opportunity to improve decision support in medical image interpretation. The term of note is decision support, indicating that computers will augment human decision making, making it more effective and efficient. The clinical impact of having computers in the routine clinical practice may allow radiologists to further integrate their knowledge with their clinical colleagues in other medical specialties and allow for precision medicine.
Topics: Data Mining; Decision Support Techniques; Deep Learning; Forecasting; Humans; Machine Learning; Precision Medicine; Radiology; Terminology as Topic
PubMed: 29398494
DOI: 10.1016/j.jacr.2017.12.028 -
Clinical Imaging Nov 2021
Topics: Health Services Accessibility; Humans; Radiology
PubMed: 34385087
DOI: 10.1016/j.clinimag.2021.07.003 -
Neuroimaging Clinics of North America Feb 2020Psychoradiology is an emerging discipline at the intersection between radiology and psychiatry. It holds promise for playing a role in clinical diagnosis, evaluation of... (Review)
Review
Psychoradiology is an emerging discipline at the intersection between radiology and psychiatry. It holds promise for playing a role in clinical diagnosis, evaluation of treatment response and prognosis, and illness risk prediction for patients with psychiatric disorders. Addressing complex issues, such as the biological heterogeneity of psychiatric syndromes and unclear neurobiological mechanisms underpinning radiological abnormalities, is a challenge that needs to be resolved. With the advance of multimodal imaging and more efforts in standardization of image acquisition and analysis, psychoradiology is becoming a promising tool for the future of clinical care for patients with psychiatric disorders.
Topics: Brain; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Mental Disorders; Neuroimaging; Prognosis; Psychiatry; Radiology
PubMed: 31759566
DOI: 10.1016/j.nic.2019.09.001 -
Current Problems in Diagnostic Radiology 2020An audience response system (ARS) is an excellent tool for improving interactive learning in radiology residents. Traditional ARSs have long allowed text-based... (Review)
Review
An audience response system (ARS) is an excellent tool for improving interactive learning in radiology residents. Traditional ARSs have long allowed text-based interactions between teacher and students. However, little attention has been given to techniques which allow students in large groups to interact directly with an image. Fortunately, a growing number of ARSs are beginning to add this ability. However, it is not the technology but the pedagogy that matters the most. The purpose of this article is to review those ARSs, and to present an array of pedagogical techniques that can take advantage of this technology.
Topics: Computer-Assisted Instruction; Humans; Internship and Residency; Radiology; Simulation Training; Students, Medical
PubMed: 31300178
DOI: 10.1067/j.cpradiol.2019.06.003 -
Academic Radiology Jan 2023
Topics: Humans; Bias; Radiology
PubMed: 36192269
DOI: 10.1016/j.acra.2022.09.003 -
Radiologic Technology Jul 2023
Topics: Humans; COVID-19; Radiography; Radiology
PubMed: 37433605
DOI: No ID Found -
Academic Radiology Apr 2019Response Evaluation Criteria in Solid Tumors (RECIST 1.1) is the gold standard for imaging response evaluation in cancer trials. We sought to evaluate consistency of...
PURPOSE
Response Evaluation Criteria in Solid Tumors (RECIST 1.1) is the gold standard for imaging response evaluation in cancer trials. We sought to evaluate consistency of applying RECIST 1.1 between 2 conventionally trained radiologists, designated as A and B; identify reasons for variation; and reconcile these differences for future studies.
METHODS
The study was approved as an institutional quality check exercise. Since no identifiable patient data was collected or used, a waiver of informed consent was granted. Imaging case report forms of a concluded multicentric breast cancer trial were retrospectively reviewed. Cohen's kappa was used to rate interobserver agreement in Response Evaluation Data (target response, nontarget response, new lesions, overall response). Significant variations were reassessed by a senior radiologist to extrapolate reasons for disagreement. Methods to improve agreement were similarly ascertained.
RESULTS
Sixty one cases with total of 82 data-pairs were evaluated (35 data-pairs in visit 5, 47 in visit 9). Both radiologists showed moderate agreement in target response (n = 82; ĸ = 0.477; 95% confidence interval [CI]: 0.314-0.640-), nontarget response (n = 82; ĸ = 0.578; 95% CI: 0.213-0.944) and overall response evaluation in both visits (n = 82; ĸ = 0.510; 95% CI: 0.344-0.676). Further assessment demonstrated "Prevalence effect" of Kappa in some cases which led to underestimation of agreement. Percent agreement of overall response was 74.39% while percent variation was 25.6%. Differences in interpreting RECIST 1.1 and in radiological image interpretation were the primary sources of variation. The commonest overall response was "Partial Response" (Rad A:45/82; Rad B:63/82).
CONCLUSION
Inspite of moderate interobserver agreement, qualitative interpretation differences in some cases increased interobserver variability. Protocols such as Adjudication, to reduce easily avoidable inconsistencies are or should be a part of the Standard Operating Procedure in imaging institutions. Based on our findings, a standard checklist has been developed to help reduce the interpretation error-margin for future studies. Such check-lists may improve interobserver agreement in the preadjudication phase thereby improving quality of results and reducing adjudication per case ratio.
CLINICAL RELEVANCE
Improving data reliability when using RECIST 1.1 will reflect in better cancer clinical trial outcomes. A checklist can be of use to imaging centers to assess and improve their own processes.
Topics: Checklist; Clinical Competence; Female; Humans; Male; Middle Aged; Neoplasms; Observer Variation; Radiology; Reproducibility of Results; Response Evaluation Criteria in Solid Tumors; Retrospective Studies; Treatment Outcome
PubMed: 29934024
DOI: 10.1016/j.acra.2018.05.017 -
Radiography (London, England : 1995) Aug 2022
Musculoskeletal radiography is a highly specialised area within the field of radiography: In response to Rosa et al. (2022) "We should not accept inappropriate radiologic views".
Topics: Humans; Musculoskeletal Diseases; Musculoskeletal System; Radiography; Radiology; Rosa
PubMed: 35459615
DOI: 10.1016/j.radi.2022.04.003 -
RoFo : Fortschritte Auf Dem Gebiete Der... Jun 2021Personalized precision medicine requires highly accurate diagnostics. While radiological research has focused on scanner and sequence technologies in recent decades,... (Review)
Review
Personalized precision medicine requires highly accurate diagnostics. While radiological research has focused on scanner and sequence technologies in recent decades, applications of artificial intelligence are increasingly attracting scientific interest as they could substantially expand the possibility of objective quantification and diagnostic or prognostic use of image information.In this context, the term "radiomics" describes the extraction of quantitative features from imaging data such as those obtained from computed tomography or magnetic resonance imaging examinations. These features are associated with predictive goals such as diagnosis or prognosis using machine learning models. It is believed that the integrative assessment of the feature patterns thus obtained, in combination with clinical, molecular and genetic data, can enable a more accurate characterization of the pathophysiology of diseases and more precise prediction of therapy response and outcome.This review describes the classical radiomics approach and discusses the existing very large variability of approaches. Finally, it outlines the research directions in which the interdisciplinary field of radiology and computer science is moving, characterized by increasingly close collaborations and the need for new educational concepts. The aim is to provide a basis for responsible and comprehensible handling of the data and analytical methods used. KEY POINTS:: · Radiomics is playing an increasingly important role in imaging research.. · Radiomics has great potential to meet the requirements of precision medicine.. · Radiomics analysis is still subject to great variability.. · There is a need for quality-assured application of radiomics in medicine.. CITATION FORMAT: · Attenberger UI, Langs G, . How does Radiomics actually work? - Review. Fortschr Röntgenstr 2021; 193: 652 - 657.
Topics: Artificial Intelligence; Computational Biology; Humans; Magnetic Resonance Imaging; Precision Medicine; Radiology; Tomography, X-Ray Computed
PubMed: 33264805
DOI: 10.1055/a-1293-8953