Tudor Voicu Moga1, Alina Popescu2, Ioan Sporea1, Mirela Danila3, Ciprian David4, Vasile Gui4, Nicoleta Iacob5, Gratian Miclaus5, Roxana Sirli6. 1. University of Medicine and Pharmacy "Victor Babes" Timisoara. Department of Gastroenterology and Hepatology at the County Hospital Timisoara. 2. University of Medicine and Pharmacy "Victor Babes" Timisoara. Department of Gastroenterology and Hepatology at the County Hospital Timisoara. alinamircea.popescu@gmail.com. 3. University of Medicine and Pharmacy "Victor Babes" Timisoara. Department of Gastroenterology and Hepatology at the County Hospital Timisoara.. 4. Electronics and Telecommunications Faculty, "Politehnica" University of Timișoara, Romania. 5. Department of Anatomy and Embryology, "Victor Babes" University of Medicine and Pharmacy, Timișoara, Romania. 6. Department of Gastroenterology and Hepatology, "Victor Babes" University of Medicine and Pharmacy, Timișoara, Romania.
Abstract
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.
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.
Authors: Tudor Voicu Moga; Ciprian David; Alina Popescu; Raluca Lupusoru; Darius Heredea; Ana M Ghiuchici; Camelia Foncea; Adrian Burdan; Roxana Sirli; Mirela Danilă; Iulia Ratiu; Teofana Bizerea-Moga; Ioan Sporea Journal: J Pers Med Date: 2021-12-20