Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Satisfaction of search (SOS) error and new lesions identification on imaging in central review for clinical trials
0
Zitationen
7
Autoren
2022
Jahr
Abstract
<strong>Purpose:</strong> Medical errors account for a third leading cause of death in the United States. Despite recent interventions, high error rates persist. Satisfaction of search (SOS) is a relatively less harmful type of bias that indicates an individual’s decreased vigilance and/or awareness of additional abnormalities after the first abnormality has been identified. We studied SOS data in clinical practice and tried to correlate its best fit in clinical trial setting reads where diagnosis is typically already known. <br/> <strong>Methods: </strong>SOS data from four different clinical trials including 8036 timepoints with assessments across 1655 subjects were reviewed by board-certified radiologist reviewers using response evaluation criteria in solid tumors (RECIST) 1.1 criteria and analyzed for new lesion identification. <br/> <strong> Results:</strong> We analyzed specific subset of subjects with progressive disease which usually is the critical clinical trial endpoint in oncology. We noticed that once progressive disease was detected by the radiologist reviewer, additional new lesions tend to be not marked or missed out on a statistically significant proportion. This might not be due to the incompetence of the reviewer but due to SOS error where satisfaction was reached on finding progressive disease, the trial endpoint analogous with the first abnormality in clinical practice. <br/> <strong>Conclusions: </strong>With SOS, once an abnormality is detected and recognized, it requires additional attention to look for other possible abnormalities within an image. Additional abnormalities may be missed by the radiologist once the first abnormality is found. Several strategies can be used to mitigate SOS which includes the use of a systematic approach to ensure all relevant findings are identified, through a secondary search once the first finding is reported.
Ähnliche Arbeiten
New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)
2008 · 29.123 Zit.
TNM Classification of Malignant Tumours
1987 · 16.123 Zit.
A survey on deep learning in medical image analysis
2017 · 13.849 Zit.
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
2011 · 10.852 Zit.
The American Joint Committee on Cancer: the 7th Edition of the AJCC Cancer Staging Manual and the Future of TNM
2010 · 9.128 Zit.