Variability in interpretation of static renal scintigraphy findings

Jaksic, E.; Beatović, S; Žagar, Ivana; Ajdinovic, Boris; Paunković, Nebojša; Stefanovic, Aida; Han, Ruben

doi:10.2298/vsp0503189j

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…The vast majority of the 590 000 renal scans performed annually in the United States are interpreted by general radiologists, who have less than 4 months training and experience in interpretations of renal scans and, thus, have marked variability in their interpretations compared to experienced readers . For instance, a survey shows that different practicing radiologist may disagree on the interpretation of the same scan between 9% and 72% of the time …”

Section: Renal Studymentioning

confidence: 99%

“…where h(·) = exp(·) 1+exp(·) . Then, given a Type I error rate of and tolerable RMSPD d k , we accept that k raters produce reasonably homogeneous ratings on a given subject if (19) is greater than 0 .…”

Section: Inferencementioning

confidence: 99%

“…12,13 For instance, a survey shows that different practicing radiologist may disagree on the interpretation of the same scan between 9% and 72% of the time. 19 Given the background, a pilot study was conducted at Emory University with the goal of gaining better insight into the nature of diagnostic variability present in a real-world clinical practice using renal scans. In this respect, the goal of the study is to quantify the interobserver variability in the population of practicing radiologists.…”

Section: Renal Studymentioning

confidence: 99%

“…12,13 For instance, a survey shows that different practicing radiologist may disagree on the interpretation of the same scan between 9% and 72% of the time. 19…”

Section: Renal Studymentioning

confidence: 99%

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Overall indices for assessing agreement among multiple raters

Jang

Manatunga

Taylor

et al. 2018

Statistics in Medicine

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The need to assess agreement exists in various clinical studies where quantifying inter-rater reliability is of great importance. Use of unscaled agreement indices, such as total deviation index and coverage probability (CP), is recommended for two main reasons: (i) they are intuitive in a sense that interpretations are tied to the original measurement unit; (ii) practitioners can readily determine whether the agreement is satisfactory by directly comparing the value of the index to a prespecified tolerable CP or absolute difference. However, the unscaled indices were only defined in the context of comparing two raters or multiple raters that assume homogeneity of variances across raters. In this paper, we introduce a set of overall indices based on the root mean square of pairwise differences that are unscaled and can be used to evaluate agreement among multiple raters that often exhibit heterogeneous measurement processes in practice. Furthermore, we propose another overall agreement index based on the root mean square of pairwise differences that is scaled and extends the concept of the recently proposed relative area under CP curve in the presence of multiple raters. We present the definitions of overall indices and propose inference procedures in which bootstrap methods are used for the estimation of standard errors. We assess the performance of the proposed approach and demonstrate its superiority over the existing methods when raters exhibit heterogeneous measurement processes using simulation studies. Finally, we demonstrate the application of our methods using a renal study.

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Section: Renal Studymentioning

confidence: 99%

Section: Inferencementioning

confidence: 99%

Section: Renal Studymentioning

confidence: 99%

“…12,13 For instance, a survey shows that different practicing radiologist may disagree on the interpretation of the same scan between 9% and 72% of the time. 19…”

Section: Renal Studymentioning

confidence: 99%

See 2 more Smart Citations

Overall indices for assessing agreement among multiple raters

Jang

Manatunga

Taylor

et al. 2018

Statistics in Medicine

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“…Different specialists may disagree on the interpretation of the same study between 9% and 72% of the time (2). In a recent study, Hunsche showed that 25% of the diuretic renal scan interpretations (obstructed, equivocal, not obstructed) depended on which experienced physician interpreted the study (3).…”

Section: Introductionmentioning

confidence: 99%

Computer-Assisted Diagnosis in Renal Nuclear Medicine: Rationale, Methodology, and Interpretative Criteria for Diuretic Renography

Taylor

Garcia

2014

Seminars in Nuclear Medicine

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The goal of artificial intelligence, expert systems, decision support systems and computer assisted diagnosis (CAD) in imaging is the development and implementation of software to assist in the detection and evaluation of abnormalities, to alert physicians to cognitive biases, to reduce intra and inter-observer variability and to facilitate the interpretation of studies at a faster rate and with a higher level of accuracy. These developments are needed to meet the challenges resulting from a rapid increase in the volume of diagnostic imaging studies coupled with a concurrent increase in the number and complexity of images in each patient data. The convergence of an expanding knowledge base and escalating time constraints increases the likelihood of physician errors. Errors are even more likely when physicians interpret low volume studies such as 99mTc-MAG3 diuretic scans where imagers may have had limited training or experience. Decision support systems include neural networks, case-based reasoning, expert systems and statistical systems. iRENEX (renal expert) is an expert system for diuretic renography that uses a set of rules obtained from human experts to analyze a knowledge base of both clinical parameters and quantitative parameters derived from the renogram. Initial studies have shown that the interpretations provided by iRENEX are comparable to the interpretations of a panel of experts. iRENEX provides immediate patient specific feedback at the time of scan interpretation, can be queried to provide the reasons for its conclusions and can be used as an educational tool to teach trainees to better interpret renal scans. iRENEX also has the capacity to populate a structured reporting module and generate a clear and concise impression based on the elements contained in the report; adherence to the procedural and data entry components of the structured reporting module assures and documents procedural competency. Finally, although the focus is CAD applied to diuretic renography, this review offers a window into the rationale, methodology and broader applications of computer assisted diagnosis in medical imaging.

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