Diagnostic performance of an expert system for interpretation of 99mTc MAG3 scans in suspected renal obstruction

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.

Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer

This study aimed to establish an artificial neural network (ANN) model based on prostate cancer signature genes (PCaSGs) to predict the patients with prostate cancer (PCa). In the present study, 270 differentially expressed genes (DEGs) were identified between PCa and normal prostate (NP) groups by differential gene expression analysis. Next, we performed Metascape gene annotation, pathway and process enrichment analysis, and PPI enrichment analysis on all 270 DEGs. Then, we identified and screened out 30 PCaSGs based on the random forest analysis and constructed an ANN model based on the gene score matrix consisting of 30 PCaSGs. Lastly, analysis of microarray dataset GSE46602 showed that the accuracy of this model for predicating PCa and NP samples was 88.9 and 78.6%, respectively. Our results suggested that the ANN model based on PCaSGs can be used for effectively predicting the patients with PCa and will be helpful for early PCa diagnosis and treatment.

Association between urinary biomarkers MMP-7/TIMP-2 and reduced renal function in children with ureteropelvic junction obstruction

IMPORTANCE

Extracellular matrix proteins and enzymes involved in degradation have been found to be associated with tissue fibrosis and ureteropelvic junction obstruction (UPJO). In this study we developed a promising urinary biomarker model which can identify reduced renal function in UPJ obstruction patients. This can potentially serve as a non-invasive way to enhance surgical decision making for patients and urologists.

OBJECTIVE

We sought to develop a predictive model to identify UPJO patients at risk for reduced renal function.

DESIGN

Prospective cohort study.

SETTING

Pre-operative urine samples were collected in a prospectively enrolled UPJO biomarker registry at our institution. Urinary MMP-2, MMP-7, TIMP-2, and NGAL were measured as well as clinical characteristics including hydronephrosis grade, differential renal function, t1/2, and UPJO etiology.

PARTICIPANTS

Children who underwent pyeloplasty for UPJO.

MAIN OUTCOME MEASUREMENT

Primary outcome was reduced renal function defined as MAG3 function <40%. Multivariable logistic regression was applied to identify the independent predictive biomarkers in the original Training cohort. Model validation and generalizability were evaluated in a new UPJO Testing cohort.

RESULTS

We included 71 patients with UPJO in the original training cohort and 39 in the validation cohort. Median age was 3.3 years (70% male). By univariate analysis, reduced renal function was associated with higher MMP-2 (p = 0.064), MMP-7 (p = 0.047), NGAL (p = 0.001), and lower TIMP-2 (p = 0.033). Combining MMP-7 with TIMP-2, the multivariable logistic regression model predicted reduced renal function with good performance (AUC = 0.830; 95% CI: 0.722-0.938). The independent testing dataset validated the results with good predictive performance (AUC = 0.738).

CONCLUSIONS AND RELEVANCE

Combination of urinary MMP-7 and TIMP-2 can identify reduced renal function in UPJO patients. With the high sensitivity cutoffs, patients can be categorized into high risk (aggressive management) versus lower risk (observation).

A Bayesian Latent Class Model to Predict Kidney Obstruction in the Absence of Gold Standard

Kidney obstruction, if untreated in a timely manner, can lead to irreversible loss of renal function. A widely used technology for evaluations of kidneys with suspected obstruction is diuresis renography. However, it is generally very challenging for radiologists who typically interpret renography data in practice to build high level of competency due to the low volume of renography studies and insufficient training. Another challenge is that there is currently no gold standard for detection of kidney obstruction. Seeking to develop a computer-aided diagnostic (CAD) tool that can assist practicing radiologists to reduce errors in the interpretation of kidney obstruction, a recent study collected data from diuresis renography, interpretations on the renography data from highly experienced nuclear medicine experts as well as clinical data. To achieve the objective, we develop a statistical model that can be used as a CAD tool for assisting radiologists in kidney interpretation. We use a Bayesian latent class modeling approach for predicting kidney obstruction through the integrative analysis of time-series renogram data, expert ratings, and clinical variables. A nonparametric Bayesian latent factor regression approach is adopted for modeling renogram curves in which the coefficients of the basis functions are parameterized via the factor loadings dependent on the latent disease status and the extended latent factors that can also adjust for clinical variables. A hierarchical probit model is used for expert ratings, allowing for training with rating data from multiple experts while predicting with at most one expert, which makes the proposed model operable in practice. An efficient MCMC algorithm is developed to train the model and predict kidney obstruction with associated uncertainty. We demonstrate the superiority of the proposed method over several existing methods through extensive simulations. Analysis of the renal study also lends support to the usefulness of our model as a CAD tool to assist less experienced radiologists in the field.

99mTc-MAG3 diuresis renography in differentiating renal obstruction: Using statistical parameters as new quantifiable indices

OBJECTIVE

The aim of this study was to research, develop and assess the feasibility of using basic statistical parameters derived from renogram, "mean count value (MeanCV) and "median count value (MedianCV)", as novel indices in the diagnosis of renal obstruction through diuresis renography.

SUBJECTS AND METHODS

First, we re-digitalized and normalized 132 renograms from 74 patients in order to derive the MeanCV and MedianCV. To improve the performance of the parameters, we extrapolated renograms by a two-compartmental modeling. After that, the cutoff points for diagnosis using each modified parameter were set and the sensitivity and specificity were calculated in order to determine the best variants of MeanCV and MedianCV that could differentiate renal obstruction status into 3 distinct classes - i) unobstructed, ii) slightly obstructed, and iii) heavily obstructed.

RESULTS

The modified MeanCV and MedianCV derived from extended renograms predicted the severity of the renal obstruction. The most appropriate variants of MeanCV and MedianCV were found to be the MeanCV₅₀ and the MedianCV₆₀. The cutoff points of MeanCV₅₀ in separating unobstructed and obstructed classes as well as slightly and heavily obstructed classes were 0.50 and 0.72, respectively. The cutoff points of MedianCV₆₀ in separating unobstructed and obstructed classes as well as slightly and heavily obstructed classes were 0.35 and 0.69, respectively. Notably, MeanCV₅₀ and MedianCV₆₀ were not significantly influenced by either age or gender.

CONCLUSIONS

The MeanCV₅₀ and the MedianCV₆₀ derived from a renogram could be incorporated with other quantifiable parameters to form a system that could provide a highly accurate diagnosis of renal obstructions.

Urinary Proteomics Yield Pathological Insights for Ureteropelvic Junction Obstruction

Prenatal hydronephrosis is a common condition that may spontaneously resolve after birth. However, this condition can result in renal damage and requires surgical correction in a number of cases. Preventing renal damage is paramount, but existing diagnostic technology is invasive, exposes infants to radiation, is costly, and is often indeterminate. A better understanding of the pathophysiology of renal obstruction as reflected in the urinary proteome may provide new insights into the disease that could potentially alter the clinical management of hydronephrosis. We performed a quantitative proteomics study of urine that was surgically obtained from eight clinically significant, unilaterally obstructed infants versus eight healthy controls, with the goal of identifying quantitatively varying proteins and the biological networks associated with them. Notably, urine was obtained from both the obstructed kidney and the bladder. Over 1100 proteins were identified, and a total of 76 quantitatively varying proteins were identified. Proteins involved in oxidative stress, inflammation, and renal disease pathways showed the most significant abundance differences. This study gives a deeper understanding of the critical proteomic changes associated with renal obstruction and represents the deepest proteomic profile of renal obstruction to date.

Quantitative means for differentiating renal obstruction by analysing renography by compartmental modelling of renal fluid flow rate

OBJECTIVE

The aim of this study was to investigate the accuracy of using a newly developed index, the ratio of urine outflow to renal pelvis volume U/V2 (1/s), in evaluating renal obstruction and determining the severity of obstruction.

PATIENTS AND METHODS

A total of 42 patients' renograms (80 kidneys) were studied. Compartmental modelling was used to model the behaviour of tracers flowing through the kidney. The derived model led to the formation of the normalized urine flow rate U/V2. An analysis was carried to test the accuracy of the developed index by comparing the developed model and the clinical evaluation of renograms. The Support Vector Machine algorithm was implemented to predict the renal obstruction status.

RESULTS

From the comparison performed between the index and the clinical evaluation from certified experts, it was shown that a higher value of index U/V2 indicated a normal kidney, whereas a lower value indicated an obstructed kidney. The classifier developed could provide a 100% accurate diagnosis of differentiated unobstructed kidneys (42/42) and obstructed kidney (18/18). For further classification of obstructed kidneys, the system grouped the samples into slightly obstructed cases with an accuracy of 100% (9/9) and heavily obstructed cases with an accuracy of 89% (8/9).

CONCLUSION

The use of the single parameter U/V2 could produce the diagnosis of renal obstruction with a high level of accuracy. This method has the potential to be used as a benchmark to distinguish the severity level of the renal obstruction.

Radionuclides in nephrourology, Part 2: pitfalls and diagnostic applications

Radionuclide renal scintigraphy provides important functional data to assist in the diagnosis and management of patients with a variety of suspected genitourinary tract problems, but the procedures are underutilized. Maximizing the utility of the available studies (as well as the perception of utility by referring physicians) requires a clear understanding of the clinical question, attention to quality control, acquisition of the essential elements necessary to produce an informed interpretation, and production of a report that presents a coherent impression based on data contained in the report and that specifically addresses the clinical question. To help achieve these goals, part 1 of this review addressed the available radiopharmaceuticals, quality control, and quantitative indices, including the measurement of absolute and relative renal function. Part 2 assumes familiarity with part 1 and focuses on the common clinical indications of suspected obstruction and renovascular hypertension; part 2 also summarizes the status of radionuclide renal imaging in the evaluation of the transplanted kidney and the detection of infection, discusses potential pitfalls, and concludes with suggestions for future research. The series of SAM questions accompanying parts 1 and 2 has been designed to reinforce and extend points made in the review. Although the primary focus is the adult patient, aspects of the review also apply to the pediatric population.

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.

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

Our objective was to design and implement a clinical history database capable of linking to our database of quantitative results from (99m)Tc-mercaptoacetyltriglycine (MAG3) renal scans and export a data summary for physicians or our software decision support system.

METHODS

For database development, we used a commercial program. Additional software was developed in Interactive Data Language. MAG3 studies were processed using an in-house enhancement of a commercial program. The relational database has 3 parts: a list of all renal scans (the RENAL database), a set of patients with quantitative processing results (the Q2 database), and a subset of patients from Q2 containing clinical data manually transcribed from the hospital information system (the CLINICAL database). To test interobserver variability, a second physician transcriber reviewed 50 randomly selected patients in the hospital information system and tabulated 2 clinical data items: hydronephrosis and presence of a current stent. The CLINICAL database was developed in stages and contains 342 fields comprising demographic information, clinical history, and findings from up to 11 radiologic procedures. A scripted algorithm is used to reliably match records present in both Q2 and CLINICAL. An Interactive Data Language program then combines data from the 2 databases into an XML (extensible markup language) file for use by the decision support system. A text file is constructed and saved for review by physicians.

RESULTS

RENAL contains 2,222 records, Q2 contains 456 records, and CLINICAL contains 152 records. The interobserver variability testing found a 95% match between the 2 observers for presence or absence of ureteral stent (κ = 0.52), a 75% match for hydronephrosis based on narrative summaries of hospitalizations and clinical visits (κ = 0.41), and a 92% match for hydronephrosis based on the imaging report (κ = 0.84).

CONCLUSION

We have developed a relational database system to integrate the quantitative results of MAG3 image processing with clinical records obtained from the hospital information system. We also have developed a methodology for formatting clinical history for review by physicians and export to a decision support system. We identified several pitfalls, including the fact that important textual information extracted from the hospital information system by knowledgeable transcribers can show substantial interobserver variation, particularly when record retrieval is based on the narrative clinical records.

iRENEX: a clinically informed decision support system for the interpretation of ⁹⁹mTc-MAG3 scans to detect renal obstruction

PURPOSE

Decision support systems for imaging analysis and interpretation are rapidly being developed and will have an increasing impact on the practice of medicine. RENEX is a renal expert system to assist physicians evaluate suspected obstruction in patients undergoing mercaptoacetyltriglycine (MAG3) renography. RENEX uses quantitative parameters extracted from the dynamic renal scan data using QuantEM™II and heuristic rules in the form of a knowledge base gleaned from experts to determine if a kidney is obstructed; however, RENEX does not have access to and could not consider the clinical information available to diagnosticians interpreting these studies. We designed and implemented a methodology to incorporate clinical information into RENEX, implemented motion detection and evaluated this new comprehensive system (iRENEX) in a pilot group of 51 renal patients.

METHODS

To reach a conclusion as to whether a kidney is obstructed, 56 new clinical rules were added to the previously reported 60 rules used to interpret quantitative MAG3 parameters. All the clinical rules were implemented after iRENEX reached a conclusion on obstruction based on the quantitative MAG3 parameters, and the evidence of obstruction was then modified by the new clinical rules. iRENEX consisted of a library to translate parameter values to certainty factors, a knowledge base with 116 heuristic interpretation rules, a forward chaining inference engine to determine obstruction and a justification engine. A clinical database was developed containing patient histories and imaging report data obtained from the hospital information system associated with the pertinent MAG3 studies. The system was fine-tuned and tested using a pilot group of 51 patients (21 men, mean age 58.2 ± 17.1 years, 100 kidneys) deemed by an expert panel to have 61 unobstructed and 39 obstructed kidneys.

RESULTS

iRENEX, using only quantitative MAG3 data agreed with the expert panel in 87 % (34/39) of obstructed and 90 % (55/61) of unobstructed kidneys. iRENEX, using both quantitative and clinical data agreed with the expert panel in 95 % (37/39) of obstructed and 92 % (56/61) of unobstructed kidneys. The clinical information significantly (p < 0.001) increased iRENEX certainty in detecting obstruction over using the quantitative data alone.

CONCLUSION

Our renal expert system for detecting renal obstruction has been substantially expanded to incorporate the clinical information available to physicians as well as advanced quality control features and was shown to interpret renal studies in a pilot group at a standardized expert level. These encouraging results warrant a prospective study in a large population of patients with and without renal obstruction to establish the diagnostic performance of iRENEX.

Guidance document for structured reporting of diuresis renography

This Guidance Document for structured reporting of diuresis renography in adults was developed by the International Scientific Committee of Radionuclides in Nephro-urology (ISCORN; http://www.iscorn.org). ISCORN chose diuresis renography for its first structured report Guidance Document because suspected obstruction is the most common reason for referral, most radionuclide renal studies are conducted at institutions that perform fewer than 3 studies per week, and a large percentage of studies are interpreted by physicians with limited training in nuclear medicine. Ten panelists were asked to categorize specific reporting elements as essential, recommended, optional (without sufficient data to support a higher ranking), and unnecessary (does not contribute to scan interpretation or quality assurance). The final document was developed through an iterative series of comments and questionnaires with a majority vote required to place an element in a specific category. The Guidance Document recommends a reporting structure organized into indications, clinical history, study procedure, findings and impression and specifies the elements considered essential or recommended in each category. The Guidance Document is not intended to be restrictive but, rather, to provide a basic structure and rationale so that the diuresis renography report will: (1) communicate the results to the referring physician in a clear and concise manner designed to optimize patient care; (2) contain the essential elements required to evaluate and interpret the study; (3) clearly document the technical components of the study necessary for accountability, quality assurance and reimbursement; and (4) encourage clinical research by facilitating better comparison and extrapolation of results between institutions.

Key variables for interpreting 99mTc-mercaptoacetyltriglycine diuretic scans: development and validation of a predictive model

OBJECTIVE

The purpose of this study was to facilitate interpretation of (99m)Tc-mercaptoacetyltriglycine (MAG3) diuretic scans by identifying key interpretative variables and developing a predictive model for computer-assisted diagnosis.

MATERIALS AND METHODS

Ninety-seven studies were randomly selected from an archived database of MAG3 baseline and furosemide acquisitions and scan interpretations (obstruction, equivocal finding, or no obstruction) derived from a consensus of three experts. Sixty-one studies (120 kidneys) were randomly chosen to build a predictive model for diagnosing or excluding obstruction. The other 36 studies (71 kidneys) composed the validation group. The probability of normal drainage (no obstruction) at the baseline acquisition and the probability of no obstruction, equivocal finding, or obstruction after furosemide administration were determined by logistic regression analysis and proportional odds modeling of MAG3 renographic data.

RESULTS

The single most important baseline variable for excluding obstruction was the ratio of postvoid counts to maximum counts. Renal counts in the last minute of furosemide acquisition divided by the maximum baseline acquisition renal counts and time to half-maximum counts after furosemide administration in a pelvic region of interest were the critical variables for determining obstruction. The area under the receiver operating characteristic curve (AUC) for predicting normal drainage in the validation sample was 0.93 (standard error, 0.02); sensitivity, 85%; specificity, 93%. The AUC for the diagnosis of obstruction after furosemide administration was 0.84 (standard error, 0.06); sensitivity, 82%; specificity, 83%.

CONCLUSION

A predictive system has been developed that provides a promising computer-assisted diagnosis approach to the interpretation of MAG3 diuretic renal scans; this system has also identified the key variables required for scan interpretation.

Computer-aided diagnosis of renal obstruction: utility of log-linear modeling versus standard ROC and kappa analysis

Background

The accuracy of computer-aided diagnosis (CAD) software is best evaluated by comparison to a gold standard which represents the true status of disease. In many settings, however, knowledge of the true status of disease is not possible and accuracy is evaluated against the interpretations of an expert panel. Common statistical approaches to evaluate accuracy include receiver operating characteristic (ROC) and kappa analysis but both of these methods have significant limitations and cannot answer the question of equivalence: Is the CAD performance equivalent to that of an expert? The goal of this study is to show the strength of log-linear analysis over standard ROC and kappa statistics in evaluating the accuracy of computer-aided diagnosis of renal obstruction compared to the diagnosis provided by expert readers.

Methods

Log-linear modeling was utilized to analyze a previously published database that used ROC and kappa statistics to compare diuresis renography scan interpretations (non-obstructed, equivocal, or obstructed) generated by a renal expert system (RENEX) in 185 kidneys (95 patients) with the independent and consensus scan interpretations of three experts who were blinded to clinical information and prospectively and independently graded each kidney as obstructed, equivocal, or non-obstructed.

Results

Log-linear modeling showed that RENEX and the expert consensus had beyond-chance agreement in both non-obstructed and obstructed readings (both p < 0.0001). Moreover, pairwise agreement between experts and pairwise agreement between each expert and RENEX were not significantly different (p = 0.41, 0.95, 0.81 for the non-obstructed, equivocal, and obstructed categories, respectively). Similarly, the three-way agreement of the three experts and three-way agreement of two experts and RENEX was not significantly different for non-obstructed (p = 0.79) and obstructed (p = 0.49) categories.

Conclusion

Log-linear modeling showed that RENEX was equivalent to any expert in rating kidneys, particularly in the obstructed and non-obstructed categories. This conclusion, which could not be derived from the original ROC and kappa analysis, emphasizes and illustrates the role and importance of log-linear modeling in the absence of a gold standard. The log-linear analysis also provides additional evidence that RENEX has the potential to assist in the interpretation of diuresis renography studies.

Automated patient motion detection and correction in dynamic renal scintigraphy

Kidney motion during dynamic renal scintigraphy can cause errors in calculated renal function parameters. Our goal was to develop and validate algorithms to detect and correct patient motion.

METHODS

We retrospectively collected dynamic images from 86 clinical renal studies (42 women, 44 men), acquired using (99m)Tc-mercaptoacetyltriglycine (80 image frames [128 × 128 pixels; 3.2 mm/pixel]: twenty-four 2-s frames, sixteen 15-s frames, and forty 30-s frames). We simulated 10 types of vertical motion in each patient study, resulting in 860 image sets. Motion consisted of up or down shifts of magnitude 0.25 pixel to 4 pixels per frame and was either a gradual shift additive over multiple frames or an abrupt shift of one or more consecutive frames, with a later return to the start position. Additional horizontal motion was added to test its effect on detection of vertical motion. Original and shifted files were processed using a motion detection algorithm. Corrective shifts were applied, and the corrected and original (unshifted) images were compared pixel by pixel. Motion detected in the shifted data was also tabulated before and after correction of motion detected in the original data. A detected shift was considered correct if it was within 0.25 pixel of the simulated magnitude. Software was developed to facilitate visual review of all images and to summarize kidney motion and motion correction using linograms.

RESULTS

Overall detection of simulated shifts was 99% (3,068/3,096 frames) when the existing motion in the original images was first corrected. When the original motion was not corrected, overall shift detection was 76% (2,345/3,096 frames). For image frames in which no shift was added (and original motion was not corrected), 87% (27,142/31,132 frames) were correctly detected as having no shift. When corrected images were compared with original images, calculated count recovery was 100% for all shifts that were whole-pixel magnitudes. For fractional-pixel shifts, percentage count recovery varied from 52% to 73%. Visual review suggested that some original frames exhibited true patient motion.

CONCLUSION

The algorithm accurately detected motion as small as 0.25 pixel. Whole-pixel motion can be detected and corrected with high accuracy. Fractional-pixel motion can be detected and corrected, but with less accuracy. Importantly, by accurately identifying unshifted frames, the algorithm helps to prevent the introduction of errors during motion correction.

A 7% decrease in the differential renal uptake of MAG3 implies a loss in renal function

OBJECTIVES

To address the fact that a decrease in the relative renal uptake of 99mTc-mercaptoacetyltriglycine (MAG3) on serial MAG3 scans may indicate a loss of function and require a change in management by providing guidance as to what constitutes a meaningful change in serial relative function measurements as well determining the normal variation of other common MAG3 renogram parameters.

METHODS

A prospective study was conducted in 24 male urology patients with stable renal function. The mean age was 66.5 ± 7.9 (SD) years; the mean serum creatinine was 1.38 ± 0.57 (SD) mg/dL, and the MAG3 renal scans were performed a mean of 11 ± 8 days apart. Each MAG3 scan included a measurement of relative function as well as the time to maximum counts and 20 minutes to maximum count ratios for both cortical and whole kidney regions of interest.

RESULTS

The Pearson and intraclass correlations for the baseline and repeat measurements of relative renal function were both 0.98. Bland-Altman plots showed no bias between the baseline and repeat relative uptake measurements. The mean difference between 2 repeated measurements of the relative MAG3 uptake was 0.04 ± 2.88% (SD) for the left kidney and 0.08 ± 3.07% (SD) for the right kidney. Comparable results were obtained for the other renogram parameters.

CONCLUSIONS

Measurements of relative renal uptake of MAG3 and common renogram parameters are highly reproducible; a decrease in relative uptake ≥7% (ie, 50%-43%) implies a loss in renal function.

Amyloid imaging in Alzheimer's disease and other dementias

With the advent of new therapeutic strategies aimed at reducing β-amyloid (Aβ) burden in the brain to potentially prevent or delay functional and irreversible cognitive loss, there is increased interest in developing agents that allow assessment of Aβ burden in vivo. Molecular neuroimaging techniques such as positron emission tomography (PET), in conjunction with related biomarkers in plasma and cerebrospinal fluid, are proving valuable in the early and differential diagnosis of Alzheimer's disease (AD). (11)C-PiB PET has proven useful in the discrimination of dementias, showing significantly higher PiB retention in grey matter of AD patients when compared with healthy controls or patients with frontotemporal dementia. (11)C-PiB PET also appears to be more accurate than FDG for the diagnosis of AD. Despite apparently underestimating the Aβ burden in the brain, (11)C-PiB PET is an optimal method to differentiate healthy controls from AD, matching histopathological reports in aging and dementia and reflecting the true regional density of Aβ plaques in cortical areas. High striatal Aβ deposition seems to be typical for carriers of familial forms of AD, whilst ApoE ε4 carriers, independent of diagnosis or disease severity, present with higher Aβ burden than non- ε4 carriers. Characterization of the binding properties of PiB has shown that despite binding to other misfolded proteins in vitro, PiB is extremely selective for Aβ at the concentrations achieved during a PET scan. Aβ burden as assessed by PET does not correlate with measures of cognition or cognitive decline in AD. Approximately 30% of apparently healthy older people, and 50-60% of people with mild cognitive impairment, present with cortical (11)C-PiB retention. In these groups, Aβ burden does correlate with episodic memory and rate of memory decline. These observations suggest that Aβ deposition is not part of normal ageing, supporting the hypothesis that Αβ deposition occurs well before the onset of symptoms and is likely to represent preclinical AD. Further longitudinal observations, coupled with different disease-specific tracers and biomarkers are required not only to confirm this hypothesis, but also to better elucidate the role of Αβ deposition in the course of Alzheimer's disease.