Development of a relational database to capture and merge clinical history with the quantitative results of radionuclide renography

Computer assisted interpretation of Tc-99m mercaptoacetyltriglycine diuretic scintigraphy enhances resident performance

OBJECTIVE

iRENEX is a software module that incorporates scintigraphic and clinical data to interpret 99m Tc- mercaptoacetyltriglycine (MAG3) diuretic studies and provide reasons for their conclusions. Our objectives were to compare iRENEX interpretations with those of expert physicians, use iRENEX to evaluate resident performance and determine if iRENEX could improve the diagnostic accuracy of experienced residents.

METHODS

Baseline and furosemide 99m Tc-MAG3 acquisitions of 50 patients with suspected obstruction (mean age ± SD, 58.7 ± 15.8 years, 60% female) were randomly selected from an archived database and independently interpreted by iRENEX, three expert readers and four nuclear medicine residents with one full year of residency. All raters had access to scintigraphic data and a text file containing clinical information and scored each kidney on a scale from +1.0 to -1.0. Scores ≥0.20 represented obstruction with higher scores indicating greater confidence. Scores +0.19 to -0.19 were indeterminate; scores ≤-0.20 indicated no obstruction. Several months later, residents reinterpreted the studies with access to iRENEX. Receiver operating characteristic (ROC) analysis and concordance correlation coefficient (CCC) quantified agreement.

RESULTS

The CCC among experts was higher than that among residents, 0.84, versus 0.39, respectively, P  < 0.001. When residents reinterpreted the studies with iRENEX, their CCC improved from 0.39 to 0.73, P  < 0.001. ROC analysis showed significant improvement in the ability of residents to distinguish between obstructed and non-obstructed kidneys using iRENEX ( P  = 0.036).

CONCLUSION

iRENEX interpretations were comparable to those of experts. iRENEX reduced interobserver variability among experienced residents and led to better agreement between resident and expert interpretations.

Personalization and Patient Involvement in Decision Support Systems: Current Trends

OBJECTIVES

This survey aims at highlighting the latest trends (2012-2014) on the development, use, and evaluation of Information and Communication Technologies (ICT) based decision support systems (DSSs) in medicine, with a particular focus on patient-centered and personalized care.

METHODS

We considered papers published on scientific journals, by querying PubMed and Web of ScienceTM. Included studies focused on the implementation or evaluation of ICT-based tools used in clinical practice. A separate search was performed on computerized physician order entry systems (CPOEs), since they are increasingly embedding patient-tailored decision support.

RESULTS

We found 73 papers on DSSs (53 on specific ICT tools) and 72 papers on CPOEs. Although decision support through the delivery of recommendations is frequent (28/53 papers), our review highlighted also DSSs only based on efficient information presentation (25/53). Patient participation in making decisions is still limited (9/53), and mostly focused on risk communication. The most represented medical area is cancer (12%). Policy makers are beginning to be included among stakeholders (6/73), but integration with hospital information systems is still low. Concerning knowledge representation/management issues, we identified a trend towards building inference engines on top of standard data models. Most of the tools (57%) underwent a formal assessment study, even if half of them aimed at evaluating usability and not effectiveness.

CONCLUSIONS

Overall, we have noticed interesting evolutions of medical DSSs to improve communication with the patient, consider the economic and organizational impact, and use standard models for knowledge representation. However, systems focusing on patient-centered care still do not seem to be available at large.

99mTc-MAG3: Image Wisely

Purpose To determine if commonly administered doses of technetium 99m (99mTc) mertiatide (MAG3) in the range of 300-370 MBq (approximately 8-10 mCi) contribute to image interpretation and justify the resulting radiation exposure. Materials and Methods The respective institutional review boards approved this HIPAA-compliant study and waived informed consent. Baseline and furosemide 99mTc-MAG3 imaging examinations in 50 patients suspected of having renal obstruction and 48 patients suspected of having renovascular hypertension (RVH) were randomly selected from archived databases and were independently scored by three experienced readers without access to 2-second flow images. Readers were blinded to their original scores, and then they rescored each examination with access to high-activity 2-second flow images. Relative renal function was determined after a low activity (62.9 MBq ± 40.7) baseline acquisition for RVH and a high activity (303.4 MBq ± 48.1) acquisition after administration of enalaprilat. Data were analyzed by using random effects analysis of variance and mean and standard error of the mean for the difference between sets of scores and the difference between relative function measurements. Results There was no significant difference in the scores without flow images compared with blinded scores with high-activity flow images for patients suspected of having obstruction (P = .80) or RVH (P = .24). Moreover, there was no significant difference in the relative uptake measurements after administration of low and high activities (P > .99). Conclusion Administered doses of 99mTc-MAG3 in the range of 300-370 MBq (approximately 8-10 mCi) do not affect the relative function measurements or contribute to interpretation of images in patients suspected of having RVH or obstruction compared with administration of lower doses; unnecessary radiation exposure can be avoided by administering doses in the range of 37-185 MBq as recommended incurrent guidelines. © RSNA, 2017.

Computer-assisted diagnosis in renal nuclear medicine: rationale, methodology, and interpretative criteria for diuretic renography

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 intraobserver and interobserver 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 technetium-99m-mercaptoacetyltriglycine 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. It 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 ensures 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.