sion, peritumoral enhancement, and other imaging features have been reported as predictors in HCC for MVI or posthepatectomy recurrence HCC [5][6][7]. However, independent validation of these features has not yet been performed, and these features are not yet applied widely.The Liver Imaging Reporting and Data System (LI-RADS) [8] was developed to standardized terminology, interpretation, and reporting of imaging for HCC diagnosis. The system addresses the full spectrum of liver lesions and pseudolesions with a 5-point scale reflecting the relative likelihood of HCC, from LR-1 (definitely benign) to LR-5 ( definitely HCC). LI-RADS also assigns category LR-M to observations considered probably or definitely malignant but lacking criteria specific for HCC and a separate category (LR-TIV
Background There are few studies about the Liver Imaging Reporting and Data System (LI-RADS), which was developed with the purpose of standardizing the interpretation and reporting of liver imaging examinations in patients at risk for hepatocellular carcinoma (HCC). Purpose To evaluate the diagnostic accuracy of HCC diagnosis using LI-RADS. Material and Methods The computed tomography (CT), magnetic resonance imaging (MRI), and clinical data of 297 lesions in 249 patients between June 2012 and August 2013 were retrospectively analyzed. Using LI-RADS 2014, two radiologists evaluated the lesions and a LI-RADS category was retrospectively assigned to each nodule. Results The final diagnoses of 297 nodules in 249 patients consisted of 191 malignant and 106 benign lesions. Out of 44 LI-RADS category 1 lesions, none were HCCs. However, 2/25 category 2 lesions, 3/35 category 3 lesions, 16/25 category 4 lesions, 151/156 category 5 lesions, and 3/12 category LRM/OM (probable malignancy, not specific for HCC/other malignancy) lesions were HCCs. The Kappa value was 0.44 (95% confidence interval [CI] = 0.39-0.49) between two observers during LI-RADS grading. Conclusion The negative predictive value of LI-RADS category 1 was 100%. In addition, a relevant proportion of lesions categorized as category 2 or 3, or even as other malignancies, were HCCs. LI-RADS category 5 had a high specificity for HCC. LI-RADS was not able to give a differential diagnosis for the false-positive lesions of LI-RADS category 5.
BACKGROUND The accurate classification of focal liver lesions (FLLs) is essential to properly guide treatment options and predict prognosis. Dynamic contrast-enhanced computed tomography (DCE-CT) is still the cornerstone in the exact classification of FLLs due to its noninvasive nature, high scanning speed, and high-density resolution. Since their recent development, convolutional neural network-based deep learning techniques has been recognized to have high potential for image recognition tasks. AIM To develop and evaluate an automated multiphase convolutional dense network (MP-CDN) to classify FLLs on multiphase CT. METHODS A total of 517 FLLs scanned on a 320-detector CT scanner using a four-phase DCE-CT imaging protocol (including precontrast phase, arterial phase, portal venous phase, and delayed phase) from 2012 to 2017 were retrospectively enrolled. FLLs were classified into four categories: Category A, hepatocellular carcinoma (HCC); category B, liver metastases; category C, benign non-inflammatory FLLs including hemangiomas, focal nodular hyperplasias and adenomas; and category D, hepatic abscesses. Each category was split into a training set and test set in an approximate 8:2 ratio. An MP-CDN classifier with a sequential input of the four-phase CT images was developed to automatically classify FLLs. The classification performance of the model was evaluated on the test set; the accuracy and specificity were calculated from the confusion matrix, and the area under the receiver operating characteristic curve (AUC) was calculated from the SoftMax probability outputted from the last layer of the MP-CDN. RESULTS A total of 410 FLLs were used for training and 107 FLLs were used for testing. The mean classification accuracy of the test set was 81.3% (87/107). The accuracy/specificity of distinguishing each category from the others were 0.916/0.964, 0.925/0.905, 0.860/0.918, and 0.925/0.963 for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively. The AUC (95% confidence interval) for differentiating each category from the others was 0.92 (0.837-0.992), 0.99 (0.967-1.00), 0.88 (0.795-0.955) and 0.96 (0.914-0.996) for HCC, metastases, benign non-inflammatory FLLs, and abscesses on the test set, respectively. CONCLUSION MP-CDN accurately classified FLLs detected on four-phase CT as HCC, metastases, benign non-inflammatory FLLs and hepatic abscesses and may assist radiologists in identifying the different types of FLLs.
Objectives: The chest CT findings that can distinguish patients with corona virus disease 2019 (COVID-19) from those with clinically suspected COVID-19 but subsequently found to be COVID-19 negative have not previously been described in detail. The purpose of this study was to determine the distinctions among patients with COVID-19 by comparing the imaging findings of patients with suspected confirmed COVID-19 and those of patients initially suspected to have COVID-19 who were ultimately negative for the disease. Methods: 28 isolated suspected in-patients with COVID-19 were enrolled in this retrospective study from January 22, 2020 to February 6, 2020. 12 patients were confirmed to have positive severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) RNA results, and 16 patients had negative results. The thin-section CT imaging findings and clinical and laboratory data of all the patients were evaluated. Results: There were no significant differences between the 12 confirmed COVID-19 (SARS-Cov-2-positive) patients and 16 SARS-CoV-2-negative patients in epidemiology and most of the clinical features or laboratory data. The CT images showed that the incidence of pure/mixed ground-glass opacities (GGOs) was not different between COVID-19 and SARS-CoV-2-negative patients [9/12 (75.0%) vs 10/16 (62.5%), p = 0.687], but pure/mixed GGOs in the peripheral were more common in patients with COVID-19 [11/12 (91.7%) vs 6/16 (37.5%), p = 0.006]. There were no significant differences in the number of lesions, bilateral lung involvement, large irregular/patchy opacities, rounded opacities, linear opacities, crazy-paving patterns, halo signs, interlobular septal thickening or air bronchograms. Conclusions: Although peripheral pure/mixed GGOs on CT may help distinguish patients with COVID-19 from clinically suspected but negative patients, CT cannot replace RT-PCR testing. Advances in knowledge: Peripheral pure/mixed GGOs on-chest CT findings can be helpful in distinguishing patients with COVID-19 from those with clinically suspected COVID-19 but subsequently found to be COVID-19 negative.
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