Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
PURPOSE We aimed to characterize the clinical and multiphase computed tomography (CT) features of the distinguishing endophytic clear cell renal cell carcinoma (ECCRCC) from endophytic renal urothelial carcinoma (ERUC). METHODS Data from 44 patients (35 men and 9 women) with ECCRCC and 21 patients (17 men and 4 women) with ERUC were retrospectively assessed. The mean patient age was 55 years (48.25- 59.50 years) and 68 years (63.00-73.00 years), respectively. Univariate and multivariate logistic regression analyses were performed to determine independent predictors for ECCRCC and to construct a predictive model that comprised clinical and CT characteristics for the differential diagnosis of ECCRCC and ERUC. Differential diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS The independent predictors of ECCRCC were heterogeneous enhancement (odds ratio [OR]=0.027, P=.005), hematuria (OR for gross hematuria=53.995, P=.003; OR for microscopic hematuria=31.126, P = .027), and an infiltrative growth pattern (OR=24.301, P = .022). The AUC of the predictive model was 0.938 (P < .001, sensitivity=84.10%, specificity=95.20%), which had a better diagnostic performance than heterogeneous enhancement (AUC=0.766, P=.001, sensitivity=81.82%, specificity=71.43%), hematuria (AUC=0.786, P < .001, sensitivity=81.82%, specificity=66.67%), and infiltrative growth pattern (AUC=0.748, P=.001, sensitivity=90.48%, specificity=59.09%). CONCLUSION The independent predictors, as well as the predictive model of CT and clinical characteristics, may assist in the differential diagnosis of ECCRCC and ERUC and provide useful information for clinical decision-making.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9682587 | PMC |
http://dx.doi.org/10.5152/dir.2022.211248 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!