scholarly journals Evaluation of treatment response in hepatocellular carcinoma in the explanted liver with Liver Imaging Reporting and Data System version 2017

2020 ◽  
Vol 30 (1) ◽  
pp. 261-271 ◽  
Author(s):  
Nieun Seo ◽  
Myoung Soo Kim ◽  
Mi-Suk Park ◽  
Jin-Young Choi ◽  
Richard K. G. Do ◽  
...  
2019 ◽  
Vol 26 (2) ◽  
pp. 203-214 ◽  
Author(s):  
Katherine S. Cools ◽  
Andrew M. Moon ◽  
Lauren M. B. Burke ◽  
Katrina A. McGinty ◽  
Paula D. Strassle ◽  
...  

Author(s):  
Krzysztof Bartnik ◽  
Joanna Podgórska ◽  
Grzegorz Rosiak ◽  
Krzysztof Korzeniowski ◽  
Olgierd Rowiński

Abstract Aim To determine inter-reader agreement in categorization of imaging features using the Liver Imaging Reporting and Data System (LI-RADS) treatment response (LR-TR) algorithm in patients with hepatocellular carcinoma (HCC) treated with conventional transarterial chemoembolization (cTACE). Methods Two radiologists used the LR-TR algorithm to assess 112 computed tomography (CT) examinations of 102 patients treated with cTACE. The inter-observer agreement in categorization of LR-TR features was assessed using kappa (κ) statistics. Results There was substantial inter-observer agreement between the two reviewers using the LR-TR algorithm (κ = 0.70; 95% CI 0.58–0.81). The two reviewers categorized tumors as non-viable in 37 (33.0%) and 39 (34.8%) of 112 examinations, viable in 58 (51.8%) and 62 (55.4%) examinations, and equivocal in 18 (16.1%) and 11 (9.8%) examinations, respectively. There was almost perfect inter-observer agreement for the LR-TR non-viable category (κ = 0.80; 95% CI 0.68–0.92), substantial agreement for the viable category (κ = 0.78 95% CI 0.67–0.90), and fair agreement for the equivocal category (κ = 0.25; 95% CI 0.02–0.49). Conclusion The LR-TR algorithm conveys high degrees of inter-observer agreement for the assessment of CT imaging features in the viable and non-viable categories. Further refinement of indeterminate features may be necessary to improve the correct categorization of equivocal lesions. Graphic abstract


Author(s):  
Hang Zhou ◽  
Chao Zhang ◽  
Linyao Du ◽  
Jiapeng Jiang ◽  
Qing Zhao ◽  
...  

Abstract Objectives To determine the diagnostic performance and inter-reader agreement of the contrast-enhanced ultrasound liver imaging reporting and data system (CEUS-LI-RADS) for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. Methods In this prospective study, CEUS-LI-RADS categories (LR-5 for predicting HCC) were assigned by six blinded readers and compared to the definitive HCC diagnosis in patients with liver cirrhosis per the 2017 China Liver Cancer Guidelines (CLCG). CEUS features were recorded in 96 patients with 96 histology-proven lesions. The diagnostic performance of LR-5 was described by the sensitivity, specificity and accuracy. Multi-reader agreement was assessed by using intraclass correlation coefficients (ICC). Results In cirrhotic patients, the specificity of LR-5 (range: 92.7–100.0 %) was statistically higher than that of CLCG for each reader (range: 28.6–64.3 %). However, the sensitivity (range: 38.6–63.6 %) and accuracy (range: 53.4–70.7 %) were statistically lower in CEUS-LIRADS than in CLCG (sensitivity range: 88.6–100.0 %; accuracy range: 77.6–86.2 %). Only fair to moderate inter-reader agreement was achieved for the CEUS-LI-RADS category (ICC = 0.595) and washout appearance (ICC range: 0.338 to 0.555). Neither nodule-in-nodule nor mosaic architecture was observed more often in HCC (all P > 0.05), with poor inter-reader consistency for both (both ICC < 0.20). Conclusion CEUS-LI-RADS category 5 has a high specificity but a low accuracy for identifying HCC in high-risk patients. Inter-reader agreement is not satisfactory concerning CEUS-LIRADS category and washout appearance. Moreover, the clinical value of ancillary features favoring HCC is quite limited.


2019 ◽  
Vol 25 (10) ◽  
pp. 1488-1502 ◽  
Author(s):  
Victoria Chernyak ◽  
Milana Flusberg ◽  
Jesse Berman ◽  
Kate C. Fruitman ◽  
Mariya Kobi ◽  
...  

2020 ◽  
Vol 75 (6) ◽  
pp. 478.e25-478.e35 ◽  
Author(s):  
A.-H. Ren ◽  
J.-B. Du ◽  
D.-W. Yang ◽  
P.-F. Zhao ◽  
Z.-C. Wang ◽  
...  

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