Differentiation of Intrahepatic Cholangiocellular Carcinoma from Hepatocellular Carcinoma in the Cirrhotic Liver Using Contrast-enhanced MR Imaging

Diagnostic performance of magnetic resonance imaging and contrast-enhanced ultrasound in differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma: a meta-analysis

PURPOSE

To compare the diagnostic ability between magnetic resonance imaging (MRI) and contrast-enhanced ultrasound (CEUS) in distinguishing intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC).

METHODS

Original studies reporting the diagnostic accuracy of MRI and CEUS in differentiating ICC from HCC were identified in PubMed and EMBASE databases. Histopathological examination was used as the reference standard for tumor diagnosis. Study quality was assessed using QUADAS-2 scale. Data were extracted to calculate the pooled diagnostic sensitivity, specificity, and diagnostic odds ratio (DOR) using a bivariate random-effects model, as well as the area under the curve (AUC). Sensitivity analysis, subgroup analysis, meta-regression, and investigation of publication bias were also performed.

RESULTS

A total of 26 studies with 28 data subsets (18 on MRI, 10 on CEUS) were included, consisting of 4169 patients with 1422 ICC lesions and 2747 HCC lesions. Most MRI studies were performed at 3T with hepatobiliary agents, and most CEUS studies used SonoVue as the contrast agent. In MRI, the pooled sensitivity, specificity, DOR, and AUC in distinguishing ICC from HCC were 0.81 (0.79, 0.84), 0.90 (0.88, 0.91), 41.47 (24.07, 71.44), and 0.93 (0.90, 0.96), respectively. The pooled sensitivity, specificity, DOR, and AUC of CEUS were 0.88 (0.84, 0.90), 0.80 (0.78, 0.83), 42.06 (12.38, 133.23), and 0.93 (0.87, 0.99), respectively. Subgroup analysis and meta-regression analysis demonstrated significant heterogeneity among the studies associated with the type of contrast agent in MRI studies. No publication bias was found.

CONCLUSION

Both MRI and CEUS showed excellent diagnostic performance in differentiating ICC from HCC. CEUS showed higher pooled sensitivity and MRI showed higher pooled specificity.

Extracellular volume fraction using contrast-enhanced CT is useful in differentiating intrahepatic cholangiocellular carcinoma from hepatocellular carcinoma

Objectives

To evaluate whether tumor extracellular volume fraction (fECV) on contrast-enhanced computed tomography (CT) aids in the differentiation between intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC).

Methods

In this retrospective study, 113 patients with pathologically confirmed ICC (n = 39) or HCC (n = 74) who had undergone preoperative contrast-enhanced CT were enrolled. Enhancement values of the tumor (Etumor) and aorta (Eaorta) were obtained in the precontrast and equilibrium phase CT images. fECV was calculated using the following equation: fECV [%] = Etumor/Eaorta × (100 - hematocrit [%]). fECV values were compared between the ICC and HCC groups using Welch's t-test. The diagnostic performance of fECV for differentiating ICC and HCC was assessed using receiver-operating characteristic (ROC) analysis. fECV and the CT imaging features of tumors were evaluated by two radiologists. Multivariate logistic regression analysis was performed to identify factors predicting a diagnosis of ICC.

Results

Mean fECV was significantly higher in ICCs (43.8% ± 13.2%) than that in HCCs (31.6% ± 9.0%, p < 0.001). The area under the curve for differentiating ICC from HCC was 0.763 when the cutoff value of fECV was 41.5%. The multivariate analysis identified fECV (unit OR: 1.10; 95% CI: 1.01-1.21; p < 0.05), peripheral rim enhancement during the arterial phase (OR: 17.0; 95% CI: 1.29-225; p < 0.05), and absence of washout pattern (OR: 235; 95% CI: 14.03-3933; p < 0.001) as independent CT features for differentiating between the two tumor types.

Conclusions

A high value of fECV, peripheral rim enhancement during the arterial phase, and absence of washout pattern were independent factors in the differentiation of ICC from HCC.

Value of Intravoxel Incoherent Motion (IVIM) Imaging for Differentiation between Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma

Efficient noninvasive imaging techniques in the differentiation of intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) are very important because of their different management and prognosis. Our purpose was to evaluate the difference of parameters extracted from intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) between the two groups and their performance for the differentiation, as well as the significance of perfusion information. IVIM studies (9 b-values) in 41 patients with either ICC or HCC were reviewed retrospectively by two observers. Diffusion coefficient (D), pseudodiffusion coefficient (D∗), perfusion fraction (f), ADC, and the mean percentage of parenchymal enhancement (MPPE) at 30 s after contrast-enhancement were calculated and compared between ICC and HCC. The relationship between D∗, f values, and MPPE was evaluated by Spearman's correlation test. The diagnostic efficacy of all parameters was analyzed by the receiver operating characteristic (ROC) curve. Interobserver and intraobserver agreements were analyzed. The parameters (D and ADC) of ICC were distinctly higher than those of HCC; whereas the parameters (f and MPPE of arterial phase) were distinctly lower (all false discovery rate [FDR]-corrected P < 0.05). The metric D∗ value of ICC was slightly higher than that of HCC (71.44 vs 69.41) with FDR-corrected P > 0.05. Moreover, the value of parameter D was significantly lower than that of ADC (FDR-corrected P < 0.05). The parameters (D and f values) extracted from IVIM showed excellent diagnostic efficiency in the identification, and the diagnostic efficiency of D value was significantly higher than that of the ADC. There were positive correlations between perfusion-related parameters (D∗, f values) and MPPE. Interobserver and intraobserver agreements were excellent or perfect in measurements of all parameters. Parameters derived from IVIM were valuable for distinguishing ICC and HCC. Moreover, the D value showed better diagnostic efficiency for the differential diagnosis than monoexponential fitting-derived ADC value. Meanwhile, the significant correlation between perfusion-related parameters and MPPE demonstrates that specific IVIM metrics may serve as a noninvasive indicator for the vascular perfusion information of ICC and HCC.

Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI

With modern management of primary liver cancer shifting towards non-invasive diagnostics, accurate tumor classification on medical imaging is increasingly critical for disease surveillance and appropriate targeting of therapy. Recent advancements in machine learning raise the possibility of automated tools that can accelerate workflow, enhance performance, and increase the accessibility of artificial intelligence to clinical researchers. We explore the use of an automated Tree-Based Optimization Tool that leverages a genetic programming algorithm for differentiation of the two common primary liver cancers on multiphasic MRI. Manual and automated analyses were performed to select an optimal machine learning model, with an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 70–75% (95% CI 0.48–0.89), and specificity of 71–79% (95% CI 0.52–0.90) on manual optimization, and an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 65–75% (95% CI 0.43–0.89) and specificity of 75–79% (95% CI 0.56–0.90) for automated machine learning. We found that automated machine learning performance was similar to that of manual optimization, and it could classify hepatocellular carcinoma and intrahepatic cholangiocarcinoma with an sensitivity and specificity comparable to that of radiologists. However, automated machine learning performance was poor on a subset of scans that met LI-RADS criteria for LR-M. Exploration of additional feature selection and classifier methods with automated machine learning to improve performance on LR-M cases as well as prospective validation in the clinical setting are needed prior to implementation.

Differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma and combined hepatocellular-cholangiocarcinoma in high-risk patients matched to MR field strength: diagnostic performance of LI-RADS version 2018

PURPOSE

To eliminate the effects of field strength in determining the diagnostic performance of the LI-RADS version 2018 (LI-RADS v2018) in differentiating hepatocellular carcinoma (HCC) from non-HCC primary liver malignancy in high-risk patients.

METHODS

Patients who were pathologically confirmed intrahepatic cholangiocarcinoma (iCCA) or combined hepatocellular-cholangiocarcinoma (cHCC-CCA) were retrospectively reviewed. Patients with HCC were matched to the iCCA or cHCC-CCA patients on age, tumor size, MR scanner, and number of tumors. Two readers independently evaluated the lesions according to LI-RADS v2018. Diagnostic performance of LI-RADS v2018 in differentiating HCC from non-HCC primary liver malignancy were analyzed.

RESULTS

A total of 198 patients with 204 lesions (102 HCCs, 78 iCCAs, and 24 cHCC-CCAs) were enrolled. The sensitivity and specificity of LR-5 or LR-TIV (definitely due to HCC) in diagnosing HCC were 68.63% and 85.29%, respectively. LR-M or LR-TIV (may be due to non-HCC malignancy) had a sensitivity of 72.55% and a specificity of 86.27% in diagnosing non-HCC malignancy. The sensitivity of LR-M or LR-TIV (may be due to non-HCC malignancy) for iCCA and cHCC-CCA was 82.05% and 41.67%, respectively. Nearly half (11/24, 45.83%) of cHCC-CCAs were categorized as LR-5. Three tesla MR showed higher sensitivity than 1.5 T in diagnosing HCC (80.00% vs 57.69%, P = 0.015).

CONCLUSION

When the effect of field strength was eliminated, LI-RADS v2018 demonstrated high specificity but suboptimal sensitivity in distinguishing HCC from non-HCC primary liver carcinomas. Most iCCAs were categorized as LR-M or LR-TIV (may be due to non-HCC malignancy). However, nearly half of cHCC-CCAs were assigned as LR-5.

Liver Imaging and Data System (LI-RADS) Version 2018 and Other Imaging Features in Intrahepatic Cholangiocarcinoma in Chinese Adults with vs. without Chronic Hepatitis B Viral Infection

PURPOSE

To describe liver imaging reporting and data system (LI-RADS) version 2018 and other MRI imaging features in intrahepatic mass-forming cholangiocarcinoma (iCCA) in Chinese adults with vs. without chronic hepatitis B viral (HBV) infection.

METHODS

We retrospectively enrolled 89 patients with pathologically proven iCCA after multiphase imaging performed between 2004 and 2017 at a tertiary medical center in southern China. Based on whether patients had chronic HBV, iCCA was divided into two subgroups: HBV-positive (n = 50 patients, including 9 with cirrhosis) vs. HBV-negative (n = 39 patients, including 14 with hepatolithiasis and 25 with no identifiable risk factor for iCCA; none had cirrhosis). Two independent abdominal radiologists in consensus reviewed the largest mass in each patient to assign LI-RADS v2018 features; they also scored each observation's shape and location. Imaging features were compared using chi-square or Fisher's exact tests.

RESULTS

Most iCCAs in HBV-positive (88% (44/50)) and HBV-negative (97% (38/39)) patients had at least one LR-M feature. Compared to iCCAs in HBV-negative patients, iCCAs in HBV-positive patients were more likely to have at least one major feature of HCC (46% (23/50) vs. 8% (3/39), P < 0.001) and more likely to be smooth (42% (21/50) vs. 10% (4/39), P = 0.001). Six of 50 (12%) iCCAs in HBV-positive patients and 1/39 (3%) iCCAs in HBV-negative patients had at least one major feature of HCC without any LR-M feature.

CONCLUSIONS

In this retrospective single-center study in Chinese adults, iCCAs in HBV-positive patients were more likely to resemble HCCs than iCCAs in HBV-negative patients.

Comparison of magnetic resonance imaging and 18-fludeoxyglucose positron emission tomography/computed tomography in the diagnostic accuracy of staging in patients with cholangiocarcinoma: A meta-analysis

BACKGROUND

Accurate clinical staging of patients with cholangiocarcinoma (CCA) has a significant impact on treatment decisions. In this study, we aimed to compare the diagnostic value of magnetic resonance imaging (MRI) and 18-fludeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) for staging of CCA.

METHODS

We performed comprehensive systematic search in Web of Science (including MEDLINE) and Excerpta Medica Database for relevant diagnostic studies in accordance with the preferred reporting items for systematic reviews and meta-analysis statement. Based on data extracted from patient-based analysis, we calculated the pooled sensitivity and specificity with the 95% confidence intervals (CIs). In addition, the publication bias was assessed by Deek funnel plot of the asymmetry test. The potential heterogeneity was explored by threshold effect analysis and subgroup analyses.

RESULTS

Thirty-two studies with 1626 patients were included in present analysis. In T stage, the pooled sensitivity and specificity of MRI were 0.90 (95% CI 0.86-0.93), 0.84 (95% CI 0.73-0.91) respectively. The pooled sensitivity and specificity of F-FDG PET/CT were 0.91 (95% CI 0.83-0.95) and 0.85 (0.64-0.95) respectively. In N stage, the pooled sensitivity and specificity of MRI were 0.64 (95% CI 0.52-0.74) and 0.69 (95% CI 0.51-0.87) respectively. The pooled sensitivity and specificity of PET/CT were 0.52 (95% CI 0.37-0.66) and 0.92 (95% CI 0.79-0.97) respectively. In M stage, the pooled sensitivity and specificity of F-FDG PET/CT were 0.56 (95% CI, 0.42-0.69) and 0.95 (95% CI, 0.91-0.97) respectively. The Deek test revealed no significant publication bias. No threshold effect was identified. The subgroup analyses showed that pathological type (extrahepatic cholangiocarcinoma vs hilar cholangiocarcinoma/intrahepatic cholangiocarcinoma), country (Asia vs non-Asia) and type of MRI (1.5T vs. 3.0T) were potential causes for the heterogeneity of MRI studies and country (Asia vs non-Asia) was a potential source for F-FDG PET/CT studies.

CONCLUSION

The analysis suggested that both modalities provide reasonable diagnostic accuracy in T stage without significant differences between them. We recommend that both modalities be considered based on local availability and practice for the diagnosis of primary CCA tumors. In N stage, the diagnosis of lymph node metastasis (N) of CCA is still limited by MRI and F-FDG PET/CT, due to unsatisfactory diagnostic accuracy of both. Nevertheless, F-FDG PET/CT can be used to confirm lymph node metastasis while a negative result may not rule out metastasis. Furthermore, F-FDG PET/CT have a low sensitivity and a high specificity for detection of distant metastasis.

Diagnosis of Liver Neoplasms by Computational and Statistical Image Analysis

Background

Distinguishing well-differentiated hepatocellular carcinoma (WD-HCC), hepatocellular adenoma (HA) and non-neoplastic liver tissue (NNLT) solely on morphology is often challenging. The purpose of this study was to evaluate the use of computational image analysis to distinguish WD-HCC, HA and NNLT.

Methods

Seventy-seven cases comprising of WD-HCC (n = 26), HA (n = 23) and NNLT (n = 28) were retrieved and reviewed. A total of 485 hematoxylin and eosin (H&E) photomicrographs (× 400, 0.09 µm²) of WD-HCC (n = 183), HA (n = 173), NNLT (n = 129) and nine whole-slide scans (three of each diagnosis) were obtained, color deconvoluted and digitally transformed. Quantitative data including nuclear density, nuclear sphericity, nuclear perimeter, and nuclear eccentricity from each image were acquired. The data were analyzed by one-way analysis of variance (ANOVA) with Tukey post hoc test, followed by unsupervised and supervised (Chi-square automatic interaction detection (CHAID)) cluster analysis.

Results

Unsupervised cluster analysis identified three well defined clusters of WD-HCC, HA and NNLT. Employing the four most discriminating nuclear features, supervised analysis was performed on a training set of 383 images, and validated on the remaining 102 test images. The analysis identified WD-HCC (sensitivity 100%, specificity 98%), HA (sensitivity 71%, specificity 85%) and NNLT (sensitivity 70%, specificity 86%). An analysis of whole-slide images identified WD-HCC with sensitivity and specificity of 100%.

Conclusions

We have successfully demonstrated that computational image analysis of nuclear features can differentiate WD-HCC from non-malignant liver with high accuracy, and can be used to assist in the histopathological diagnosis of hepatocellular carcinoma.

Successful Resection in a Case of Hepatocellular Carcinoma Masquerading as Hilar Cholangiocarcinoma: A Case Report

Obstructive jaundice in the setting of a hilar mass raises strong suspicion of a cholangiocarcinoma, with diagnosis usually made on imaging. Jaundice, when present in patients with hepatocellular carcinoma, is mostly related to underlying liver disease (i.e. cirrhosis) and only rarely to diffuse tumor infiltration of liver parenchyma or hilar invasion. We report here the good outcome of a 67 year-old patient who presented with obstructive jaundice, underwent surgery and was given a diagnosis of hepatocellular carcinoma, which was made only at histopathological examination of resected specimen.