Adding attenuation corrected images in myocardial perfusion imaging reduces the need for a rest study.

The American Society of Nuclear Cardiology and the Society of Nuclear Medicine conclude that incorporation of attenuation corrected (AC) images in myocardial perfusion scintigraphy (MPS) will improve diagnostic accuracy. The aim was to investigate the value of adding AC stress-only images for the decision whether a rest study is necessary or not

Referring physicians underestimate the extent of abnormalities in final reports from myocardial perfusion imaging

BACKGROUND: It is important that referring physicians and other treating clinicians properly understand the final reports from diagnostic tests. The aim of the study was to investigate whether referring physicians interpret a final report for a myocardial perfusion scintigraphy (MPS) test in the same way that the reading nuclear medicine physician intended. METHODS: After viewing final reports containing only typical clinical verbiage and images, physicians in nuclear medicine and referring physicians (physicians in cardiology, internal medicine, and general practitioners) independently classified 60 MPS tests for the presence versus absence of ischemia/infarction according to objective grades of 1–5 (1 = No ischemia/infarction, 2 = Probably no ischemia/infarction 3 = Equivocal, 4 = Probable ischemia/infarction, and 5 = Certain ischemia/infarction). When ischemia and/or infarction were thought to be present in the left ventricle, all physicians were also asked to mark the involved segments based on the 17-segment model. RESULTS: There was good diagnostic agreement between physicians in nuclear medicine and referring physicians when assessing the general presence versus absence of both ischemia and infarction (median squared kappa coefficient of 0.92 for both). However, when using the 17-segment model, compared to the physicians in nuclear medicine, 12 of 23 referring physicians underestimated the extent of ischemic area while 6 underestimated and 1 overestimated the extent of infarcted area. CONCLUSIONS: Whereas referring physicians gain a good understanding of the general presence versus absence of ischemia and infarction from MPS test reports, they often underestimate the extent of any ischemic or infarcted areas. This may have adverse clinical consequences and thus the language in final reports from MPS tests might be further improved and standardized

Nuclear medicine technologists are able to accurately determine when a myocardial perfusion rest study is necessary.

ABSTRACT

Asymmetric Cerebral Blood Flow in Patients with Mild Cognitive Impairment: Possible Relationship to Further Cognitive Deterioration

To explore patterns of cerebral blood flow in patients with mild cognitive impairment (MCI), who (1) eventually deteriorate into overt dementia, with no particular focus on the type of dementia, or (2) do not appear to further deteriorate in their cognitive functions

Explaining artificial neural network ensembles: A case study with electrocardiograms from chest pain patients

Artificial neural networks is one of the most commonly used machine learning algorithms in medical applications. However, they are still not used in practice in the clinics partly due to their lack of explanatory capacity. We compare two case-based explanation methods to two trained physicians on analysis of electrocardiogram (ECG) data from patients with a suspected acute coronary syndrome (ACS). The median overlaps of the top 5 selected features between the two physicians, and a given physician and a method, were initially low. Using a correlation analysis of the features the median overlap increased to values typically in the range 3-4. In conclusion, both our case-based methods generate explanations similar to those of trained expert physicians on the problem of diagnosing ACS from ECG data

Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room

Summary Objective Patients with suspicion of acute coronary syndrome (ACS) are difficult to diagnose and they represent a very heterogeneous group. Some require immediate treatment while others, with only minor disorders, may be sent home. Detecting ACS patients using a machine learning approach would be advantageous in many situations. Methods and materials Artificial neural network (ANN) ensembles and logistic regression models were trained on data from 634 patients presenting an emergency department with chest pain. Only data immediately available at patient presentation were used, including electrocardiogram (ECG) data. The models were analyzed using receiver operating characteristics (ROC) curve analysis, calibration assessments, inter- and intra-method variations. Effective odds ratios for the ANN ensembles were compared with the odds ratios obtained from the logistic model. Results The ANN ensemble approach together with ECG data preprocessed using principal component analysis resulted in an area under the ROC curve of 80%. At the sensitivity of 95% the specificity was 41%, corresponding to a negative predictive value of 97%, given the ACS prevalence of 21%. Adding clinical data available at presentation did not improve the ANN ensemble performance. Using the area under the ROC curve and model calibration as measures of performance we found an advantage using the ANN ensemble models compared to the logistic regression models. Conclusion Clinically, a prediction model of the present type, combined with the judgment of trained emergency department personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS

Diagnostic evaluation of three cardiac software packages using a consecutive group of patients

Purpose: The aim of this study was to compare the diagnostic performance of the three software packages 4DMSPECT (4DM), Emory Cardiac Toolbox (ECTb), and Cedars Quantitative Perfusion SPECT (QPS) for quantification of myocardial perfusion scintigram (MPS) using a large group of consecutive patients. Methods: We studied 1,052 consecutive patients who underwent 2-day stress/rest 99mTc-sestamibi MPS studies. The reference/gold-standard classifications for the MPS studies were obtained from three physicians, with more than 25 years each of experience in nuclear cardiology, who re-evaluated all MPS images. Automatic processing was carried out using 4DM, ECTb, and QPS software packages. Total stress defect extent (TDE) and summed stress score (SSS) based on a 17-segment model were obtained from the software packages. Receiver-operating characteristic (ROC) analysis was performed. Results: A total of 734 patients were classified as normal and the remaining 318 were classified as having infarction and/or ischemia. The performance of the software packages calculated as the area under the SSS ROC curve were 0.87 for 4DM, 0.80 for QPS, and 0.76 for ECTb (QPS vs. ECTb p = 0.03; other differences p < 0.0001). The area under the TDE ROC curve were 0.87 for 4DM, 0.82 for QPS, and 0.76 for ECTb (QPS vs. ECTb p = 0.0005; other differences p < 0.0001). Conclusion: There are considerable differences in performance between the three software packages with 4DM showing the best performance and ECTb the worst. These differences in performance should be taken in consideration when software packages are used in clinical routine or in clinical studies

Perfusion vector - a new method to quantify myocardial perfusion scintigraphy images: a simulation study with validation in patients

The interpretation of myocardial perfusion scintigraphy (MPS) largely relies on visual assessment by the physician of the localization and extent of a perfusion defect. The aim of this study was to introduce the concept of the perfusion vector as a new objective quantitative method for further assisting the visual interpretation and to test the concept using simulated MPS images as well as patients