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Satisfaction of search (SOS) error and new lesions identification on imaging in central review for clinical trials

2022·0 Zitationen
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7

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2022

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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.

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Radiomics and Machine Learning in Medical ImagingRadiology practices and educationArtificial Intelligence in Healthcare and Education
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