Biomedical subjects

Jui G Bhagwat

Publications and source records attributed to Jui G Bhagwat.

9 recordsLinked to original sources

PubMed2006

Statistical combination schemes of repeated diagnostic test data.

RATIONALE AND OBJECTIVES: When diagnostic tests are repeated and combined, a number of schemes may be adopted. Guidelines for their interpretations are required. MATERIALS AND METHODS: Three combination schemes, "and" (A), "or" (O), and "majority" (M), are considered. To evaluate these schemes, dependency by specifying kappa values quantifying repeated test agreement was structured. In a pilot study, the combined accuracies of magnetic resonance imaging using six different pulse sequences of medial collateral ligaments of the elbows of 28 cadavers, with eight having lesions artificially created surgically, were examined. Images were evaluated simultaneously by using a five-point ordinal scale. For each pulse sequence, individuals for whom the diagnosis varied from once to three repetitions were considered. RESULTS: Scheme M improves diagnostic accuracy when sensitivity and specificity of a single test exceed 0.5, with maximal improvement at 0.79. Under scheme A, sensitivity decreases to 0.38-0.59. Under scheme O, sensitivity increases to 0.53-0.79. Scheme M yields a small improvement, reaching 0.50-0.71. Under scheme A, specificity increases to 0.95-0.98. Under scheme O, specificity decreases to 0.91-0.98. Scheme M also yields a small improvement, reaching 0.94-0.98. CONCLUSION: Scheme A is recommended for ruling in diagnoses, scheme O is recommended for ruling out diagnoses, and scheme M is neutral. Consequently, different schemes may be used to optimize the target diagnostic accuracy.

Algorithms↗

PubMed2006

Supratentorial low-grade glioma resectability: statistical predictive analysis based on anatomic MR features and tumor characteristics.

PURPOSE: To retrospectively assess the main variables that affect the complete magnetic resonance (MR) imaging-guided resection of supratentorial low-grade gliomas. MATERIALS AND METHODS: Institutional review board approval was obtained for this retrospective HIPAA-compliant study, with the requirement for informed consent waived. Data from 101 patients (61 men, 40 women; mean age, 39 years; age range, 18-72 years) who had nonenhancing supratentorial mass lesions that were histopathologically diagnosed as low-grade (World Health Organization grade II) gliomas and consecutively underwent surgery with intraoperative MR imaging guidance were analyzed. There were 21 low-grade astrocytomas, 64 oligodendrogliomas, and 16 mixed oligoastrocytomas. Initial and residual tumor volumes were measured on intraoperative T2-weighted MR images and three-dimensional spoiled gradient-echo MR images. The anatomic relationships between the tumor and eloquent cortical and/or subcortical regions and the influence of these relationships on the extent of resection were analyzed on the basis of preoperative MR imaging findings. Summary measures, univariate Fisher exact test and t test, and multivariate logistic regression analyses were performed. RESULTS: Tumor volume ranged from 2.7-231.0 mL. Univariate analyses revealed the following tumor characteristics to be significant predictive variables of incomplete tumor resection: diffuse tumor margin on T2-weighted MR images, oligodendroglioma or oligoastrocytoma histopathologic type, and large tumor volume (P < .05 for all). Tumor involvement of the following structures was associated with incomplete resection: corpus callosum, corticospinal tract, insular lobe, middle cerebral artery, motor cortex, optic radiation, visual cortex, and basal ganglia (P < .05 for all). Multivariate analyses revealed that incomplete tumor resection was due to tumor involvement of the corticospinal tract (P < .01), large tumor volume (P < .01), and oligodendroglioma histopathologic type (P = .02). CONCLUSION: The main variables associated with incomplete tumor resection in 101 patients were identified by using statistical predictive analyses.

Adolescent↗

PubMed2006

Multi-detector row CT urography of normal urinary collecting system: furosemide versus saline as adjunct to contrast medium.

PURPOSE: To retrospectively evaluate whether intravenous furosemide, either alone or in addition to intravenous saline, improved depiction of the normal urinary collecting system at multi-detector row computed tomographic (CT) urography. MATERIALS AND METHODS: Institutional review board approval for review of patient images and medical records was obtained; informed consent was not required for this HIPAA-compliant study. Excretory phase images from multi-detector row CT urography in 87 patients (44 women, 43 men; age range, 21-83 years; mean, 53 years) were reviewed. Examinations were performed with, in addition to intravenous contrast medium, 250 mL of intravenous normal saline alone (n = 35), both 250 mL of normal saline and 10 mg of intravenous furosemide (n = 26), or 10 mg of furosemide alone (n = 26). Three readers, blinded to the imaging technique used, individually assigned opacification scores to each of six urinary collecting system segments. Urinary distention was assessed by one reader by measuring transverse widths of the proximal, middle, and distal ureteral segments. Mean opacification scores for each segment and mean ureteral width measurements for each technique were compared by using the Student t test. RESULTS: Mean opacification scores achieved with furosemide were significantly higher than those achieved with saline for the middle (P </= .008) and distal (P < .001) ureteral segments. Similarly, mean ureteral widths were significantly higher with furosemide than with saline for the middle (P </= .04) and distal segments (P = .01). There was no overall benefit of administering both saline and furosemide. CONCLUSION: To optimize opacification and distention of the normal urinary collecting system, contrast material-enhanced multi-detector row CT urography may be supplemented with intravenous furosemide alone.

Adult↗

PubMed2005

Use of productivity and financial indicators for monitoring performance in academic radiology departments: U.S. nationwide survey.

PURPOSE: To determine how productivity- and finance-related indicators are used by radiology departments to evaluate departmental performance. MATERIALS AND METHODS: The study met the criteria to be exempt from institutional review board approval. All subjects were informed of the purpose of the study and that their questionnaire responses would be kept confidential. For the study, a survey was sent to 132 members of the Society of Chairmen of Academic Radiology Departments (SCARD) nationwide. The survey was designed to (a) assess organizational information about hospital and radiology departments, (b) determine the types and mean numbers of productivity and financial indicators used by radiology departments, (c) determine how these indicators are used to influence departmental productivity, and (d) assess the reference-standard goals with which each indicator value was compared. A total of 77 variables were studied. Summary statistics, Spearman rank correlation coefficient, and chi2 analyses were performed. RESULTS: The response rate was 42% (55 of 132 surveyed SCARD members). The mean number of productivity indicators used by radiology departments was 4.55 +/- 2.56 (standard deviation), while the mean number of financial indicators used was 2.89 +/- 1.99. Twenty-two (40%) of the 55 responding departments used productivity indicators to monitor and provide feedback to radiologists, hospital leaders, and technical staff members for improved productivity, but only 11 (20%) departments used these indicators to compare personnel performances against specific productivity standards. The most frequent goal (of seven [13%] responding departments) of using the indicators was to increase the examination volume from the previous year by 5%-10%. CONCLUSION: Academic radiology departments across the United States do not use a standardized set of productivity and financial indicators to measure departmental performance. Examination volume is the most frequently used productivity indicator, whereas general expenses are commonly used as indicators of financial status.

Chi-Square Distribution↗

PubMed2005

The use of receiver operating characteristic curves in biomedical informatics.

Receiver operating characteristic (ROC) curves are frequently used in biomedical informatics research to evaluate classification and prediction models for decision support, diagnosis, and prognosis. ROC analysis investigates the accuracy of a model's ability to separate positive from negative cases (such as predicting the presence or absence of disease), and the results are independent of the prevalence of positive cases in the study population. It is especially useful in evaluating predictive models or other tests that produce output values over a continuous range, since it captures the trade-off between sensitivity and specificity over that range. There are many ways to conduct an ROC analysis. The best approach depends on the experiment; an inappropriate approach can easily lead to incorrect conclusions. In this article, we review the basic concepts of ROC analysis, illustrate their use with sample calculations, make recommendations drawn from the literature, and list readily available software.

Biomedical Engineering↗

PubMed2005

A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method.

OBJECTIVE: Medical classification accuracy studies often yield continuous data based on predictive models for treatment outcomes. A popular method for evaluating the performance of diagnostic tests is the receiver operating characteristic (ROC) curve analysis. The main objective was to develop a global statistical hypothesis test for assessing the goodness-of-fit (GOF) for parametric ROC curves via the bootstrap. DESIGN: A simple log (or logit) and a more flexible Box-Cox normality transformations were applied to untransformed or transformed data from two clinical studies to predict complications following percutaneous coronary interventions (PCIs) and for image-guided neurosurgical resection results predicted by tumor volume, respectively. We compared a non-parametric with a parametric binormal estimate of the underlying ROC curve. To construct such a GOF test, we used the non-parametric and parametric areas under the curve (AUCs) as the metrics, with a resulting p value reported. RESULTS: In the interventional cardiology example, logit and Box-Cox transformations of the predictive probabilities led to satisfactory AUCs (AUC=0.888; p=0.78, and AUC=0.888; p=0.73, respectively), while in the brain tumor resection example, log and Box-Cox transformations of the tumor size also led to satisfactory AUCs (AUC=0.898; p=0.61, and AUC=0.899; p=0.42, respectively). In contrast, significant departures from GOF were observed without applying any transformation prior to assuming a binormal model (AUC=0.766; p=0.004, and AUC=0.831; p=0.03), respectively. CONCLUSIONS: In both studies the p values suggested that transformations were important to consider before applying any binormal model to estimate the AUC. Our analyses also demonstrated and confirmed the predictive values of different classifiers for determining the interventional complications following PCIs and resection outcomes in image-guided neurosurgery.

Adolescent↗

PubMed2005

Combining classifiers using their receiver operating characteristics and maximum likelihood estimation.

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging.

Artificial Intelligence↗

PubMed2005

Statistical validation of brain tumor shape approximation via spherical harmonics for image-guided neurosurgery.

RATIONALE AND OBJECTIVES: Surgical planning now routinely uses both two-dimensional (2D) and three-dimensional (3D) models that integrate data from multiple imaging modalities, each highlighting one or more aspects of morphology or function. We performed a preliminary evaluation of the use of spherical harmonics (SH) in approximating the 3D shape and estimating the volume of brain tumors of varying characteristics. MATERIALS AND METHODS: Magnetic resonance (MR) images from five patients with brain tumors were selected randomly from our MR-guided neurosurgical practice. Standardized mean square reconstruction errors (SMSRE) by tumor volume were measured. Validation metrics for comparing performances of the SH method against segmented contours (SC) were the dice similarity coefficient (DSC) and standardized Euclidean distance (SED) measure. RESULTS: Tumor volume range was 22,413-85,189 mm3, and range of number of vertices in triangulated models was 3674-6544. At SH approximations with degree of at least 30, SMSRE were within 1.66 x 10(-5) mm(-1). Summary measures yielded a DSC range of 0.89-0.99 (pooled median, 0.97 and significantly >0.7; P < .001) and an SED range of 0.0002-0.0028 (pooled median, 0.0005). CONCLUSION: 3D shapes of tumors may be approximated by using SH for neurosurgical applications.

Adult↗

PubMed2004

Practice management performance indicators in academic radiology departments.

PURPOSE: To determine the management performance indicators most frequently utilized in academic radiology departments in the United States. MATERIALS AND METHODS: This investigation met the criteria for an exemption from institutional review board approval. A cross-sectional study in which a validated national survey was sent to members of the Society of Chairmen of Academic Radiology Departments (SCARD) was conducted. The survey was designed to examine the following six categories of 28 performance indicators: (a) general organization, (b) volume and productivity, (c) radiology reporting, (d) access to examinations, (e) customer satisfaction, and (f) finance. A total of 158 variables were included in the analysis. Summary statistics, the chi(2) test, rank correlation, multiple regression analysis, and analysis of variance were used. RESULTS: A response rate of 42% (55 of 132 SCARD members) was achieved. The mean number of performance indicators used by radiology departments was 16 +/- 6.35 (standard deviation). The most frequently utilized performance indicators were as follows: (a) productivity, in terms of examination volume (78% [43 departments]) and examination volume per modality (78% [43 departments]); (b) reporting, in terms of report turnaround (82% [45 departments]) and transcription time (71% [39 departments]); (c) access, in terms of appointment access to magnetic resonance imaging (80% [44 departments]); (d) satisfaction, in terms of number of patient complaints (84% [46 departments]); and (e) finance, in terms of expenses (67% [37 departments]). Regression analysis revealed that the numbers of performance indicators in each category were statistically significant in predicting the total number of performance indicators used (P < .001 for all). Numbers of productivity and financial indicators were moderately correlated (r = 0.51). However, there were no statistically significant correlations between the numbers of performance indicators used and hospital location, hospital size, or department size (P > .4 for all). CONCLUSION: Assessing departmental performance with a wide range of management indicators is not yet an established and standardized practice in academic radiology departments in the United States. Among all indicators, productivity indicators are the most frequently used.

Academic Medical Centers↗