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
Aim: Contrast enhanced ultrasound (CEUS) improved the characterization of focal liver lesions (FLLs), but is an operatordependent method. The goal of this paper was to test a computer assisted diagnosis (CAD) prototype and to see its benefit in assisting a beginner in the evaluation of FLLs.
Material And Method: Our cohort included 97 good quality CEUS videos[34% hepatocellular carcinomas (HCC), 12.3% hypervascular metastases (HiperM), 11.3% hypovascular metastases (HipoM), 24.7% hemangiomas (HMG), 17.5% focal nodular hyperplasia (FNH)] that were used to develop a CAD prototype based on an algorithm that tested a binary decision based classifier. Two young medical doctors (1 year CEUS experience), two experts and the CAD prototype, reevaluated 50 FLLs CEUS videos (diagnosis of benign vs. malignant) first blinded to clinical data, in order to evaluate the diagnostic gap beginner vs. expert.
Results: The CAD classifier managed a 75.2% overall (benign vs. malignant) correct classification rate. The overall classification rates for the evaluators, before and after clinical data were: first beginner-78%; 94%; second beginner-82%; 96%; first expert-94%; 100%; second expert-96%; 98%. For both beginners, the malignant vs. benign diagnosis significantly improved after knowing the clinical data (p=0.005; p=0,008). The expert was better than the beginner (p=0.04) and better than the CAD (p=0.001). CAD in addition to the beginner can reach the expert diagnosis.
Conclusions: The most frequent lesions misdiagnosed at CEUS were FNH and HCC. The CAD prototype is a good comparing tool for a beginner operator that can be developed to assist the diagnosis. In order to increase the classification rate, the CAD system for FLL in CEUS must integrate the clinical data.
Download full-text PDF |
Source |
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http://dx.doi.org/10.11152/mu-936 | DOI Listing |
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