Imaging features of combined hepatocellular and cholangiocarcinoma compared with those of hepatocellular carcinoma and intrahepatic cholangiocellular carcinoma in a Chinese population

How to differentiate between combined hepatocellular carcinoma-cholangiocarcinoma and intrahepatic cholangiocarcinoma with rim arterial phase hyperenhancement?

PURPOSE

To analyze and compare the differences in MRI features between combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) and intrahepatic cholangiocarcinoma (iCCA) with arterial phase peripheral enhancement, so as to provide valuable references for preoperative differential diagnosis.

METHODS

Seventy cHCC-CCA patients and 74 iCCA patients confirmed by pathology were included in this study. Their contrast-enhanced MRI showed rim arterial phase hyperenhancement (Rim APHE). The differences of clinicopathological data and MRI features between cHCC-CCA and iCCA were compared. Then, the sensitivity, specificity, and area under curve (AUC) were also analyzed and compared.

RESULTS

Seventy cHCC-CCA patients (mean age, 55.7 ± 10.6 years) and 74 iCCA patients (mean age, 61.1 ± 10.5 years) were evaluated. In this study, univariable and multivariable regression analysis showed that AFP > 20 ng/ml (OR = 5.824, p = 0.006), enhancing capsule (OR = 7.252, p = 0.001), and mosaic architecture (OR = 32.732, p < 0.001) were independent risk factors of cHCC-CCA with Rim APHE. However, only hepatic capsule retraction (OR = 0.091, p < 0.001) was an independent predictor of iCCA. In addition, combining AFP > 20 ng/ml with enhancing capsule (96.7% vs. 79.2%, p < 0.001) and/or mosaic architecture (96.4% vs. 94.7%, p < 0.001) can improve the sensitivity of differentiating cHCC-CCA (vs. iCCA) with Rim APHE.

CONCLUSION

The combination of elevated AFP and MRI features, such as enhancing capsule and mosaic architecture, will help in preoperative differential diagnosis of cHCC-CCA and iCCA with Rim APHE.

Imaged periductal infiltration: Diagnostic and prognostic role in intrahepatic mass-forming cholangiocarcinoma

Purpose

This study examines periductal infiltration in intrahepatic mass-forming cholangiocarcinoma (IMCC), focusing on its importance for differentiating hepatic tumors and its influence on post-surgical survival in IMCC patients.

Methods

Eighty-three consecutive patients with IMCC (n = 43) and liver cancer whose preoperative images showed intrahepatic bile duct dilatation adjacent to the tumor for differential diagnosis from hepatocellular carcinoma (HCC) [n = 21], metastatic liver cancer (MLC) [n = 16] and combined hepatocellular-cholangiocarcinoma (cHCC-CC) [n = 3] were enrolled. CT and MRI findings of simple bile duct compression, imaged periductal infiltration, and imaged intrabiliary growth adjacent to the main tumor were reviewed. Clinicopathological and imaging features were compared in each group. The sensitivity, specificity, and odds ratio were calculated for each imaging finding of IMCC versus the other tumor groups. Overall survival was compared between cases of IMCC with and without imaged periductal infiltration.

Results

Simple bile duct compression and imaged intrabiliary growth were more frequently observed in HCC than in the others (p < 0.0001 and 0.040, respectively). Imaged periductal infiltration was observed more often in histopathologically confirmed large-duct type IMCC than in the small-duct type IMCC (p = 0.034). Multivariable analysis demonstrated that only imaged periductal infiltration (odds ratio, 50.67) was independently correlated with IMCC. Patients with IMCC who had imaged periductal infiltration experienced a poorer prognosis than those without imaged periductal infiltration (p = 0.0034).

Conclusion

Imaged periductal infiltration may serve as a significant marker for differentiating IMCC from other liver cancers. It may also have the potential to predict post-surgical outcomes in patients with IMCC.

A machine-learning model based on dynamic contrast-enhanced MRI for preoperative differentiation between hepatocellular carcinoma and combined hepatocellular-cholangiocarcinoma

AIM

To establish a machine-learning model based on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to differentiate combined hepatocellular-cholangiocarcinoma (cHCC-CC) from hepatocellular carcinoma (HCC) before surgery.

MATERIALS AND METHODS

Clinical and MRI data of 194 patients with histopathologically diagnosed cHCC-CC (n=52) or HCC (n=142) were analysed retrospectively. ITK-SNAP software was used to delineate three-dimensional (3D) lesions and extract high-throughput features. Feature selection was carried out based on Pearson's correlation coefficient and least absolute shrinkage and selection operator (LASSO) regression analysis. A radiomics model (radiomics features), a clinical model (i.e., clinical-image features), and a fusion model (i.e., radiomics features + clinical-image features) were established using six machine-learning classifiers. The performance of each model in distinguishing between cHCC-CC and HCC was evaluated with the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), sensitivity, and specificity.

RESULTS

Significant differences in liver cirrhosis, tumour number, shape, edge, peritumoural enhancement in the arterial phase, and lipid were identified between cHCC-CC and HCC patients (p<0.05). The AUC of the fusion model based on logistic regression was 0.878 (95% CI: 0.766-0.949) in the arterial phase in the test set, and the sensitivity/specificity was 0.844/0.714; however, the AUC of the clinical and radiomics models was 0.759 (95% CI: 0.663-0.861) and 0.838 (95% CI: 0.719-0.921) in the test set, respectively.

CONCLUSION

The fusion model based on DCE-MRI in the arterial phase can significantly improve the diagnostic rate of cHCC-CC and HCC as compared with conventional approaches.

Update on the Diagnosis and Treatment of Combined Hepatocellular Cholangiocarcinoma

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a unique type of liver tumor that contains both hepatocellular carcinoma and cholangiocarcinoma components within a single tumor. The fifth edition of the World Health Organization classification provides a definition and diagnostic criteria for cHCC-CCA. However, the heterogeneous histomorphology and presentation resulting from variation of the proportion of each component poses challenges for clinical diagnosis and treatment. A diagnosis of cHCC-CCA may be suggested by the synchronous elevation of serum tumor markers for hepatocellular carcinoma and cholangiocarcinoma, a mixed enhancement pattern on imaging, and a discrepancy between the elevation of tumor marker and the imaging enhancement pattern. Histopathological examination using hematoxylin and eosin staining is considered the gold standard for diagnosing cHCC-CCA, and comprehensive examination of resection or biopsy specimens is crucial for an accurate diagnosis. Currently, there is no standard treatment for cHCC-CCA, and surgery is the mainstay. Anatomic hepatectomy with lymphadenectomy is among the recommended surgical procedures. The role of liver transplantation in the management of cHCC-CCA is still uncertain. Transarterial chemoembolization may be effective for unresectable cHCC-CCA, particularly for hypervascular tumors. However, the available evidence does not support systemic therapy for advanced cHCC-CCA. The prognosis of cHCC-CCA is generally poor, and there is no established staging system. Further research is needed to better understand the histogenesis and clinical management of cHCC-CCA. This review provides an overview of the current literature on cHCC-CCA with a focus on its clinical characteristics, pathological diagnosis, and management.

The significance of the predominant component in combined hepatocellular-cholangiocarcinoma: MRI manifestation and prognostic value

PURPOSE

To investigate the significance of the predominant component of combined hepatocellular-cholangiocarcinoma (cHCC-CC) in terms of MRI manifestation and its potential prognostic value compared to hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC).

MATERIALS AND METHODS

A total of 300 patients with chronic liver disease from two centers were retrospectively enrolled, including 100 surgically proven cases of cHCC-CC, HCC, and ICC each. Univariate and multivariate regression analyses were performed to identify independent predictors for distinguishing HCC-predominant cHCC-CC and ICC-predominant cHCC-CC from HCC and ICC, respectively. Diagnostic models were constructed based on the independent features. Recurrence-free survival (RFS) was estimated and compared between groups.

RESULTS

The predominant component was an independent predictor for RFS in cHCC-CC (hazard ratio = 1.957, P = 0.044). The presence of targetoid appearance (odds ratio(OR) = 10.907, P = 0.001), lack of enhancing capsule (OR = 0.072, P = 0.001) and arterial peritumoral enhancement (OR = 0.091, P = 0.003) were independent predictors suggestive of HCC-predominant cHCC-CC over HCC; their combination yielded an area under the curve of 0.756. No significant differences were observed in RFS between HCC-predominant cHCC-CC and HCC (P = 0.864). Male gender (OR = 4.049, P = 0.015), higher alpha fetoprotein (OR = 16.789, P < 0.001) and normal carbohydrate antigen 19-9 (OR = 0.343, P = 0.036) levels, presence of enhancing capsule (OR = 7.819, P < 0.001) and hemorrhage (OR = 23.526, P = 0.004), and lack of targetoid appearance (OR = 0.129, P = 0.005) and liver surface retraction (OR = 0.190, P = 0.021) were independent predictors suggestive of ICC-predominant cHCC-CC over ICC; their combination yielded an area under the curve value of 0.898. ICC-predominant cHCC-CC exhibited poorer survival with shorter RFS than ICC (P = 0.009).

CONCLUSION

The predominant histopathological component is closely related to the imaging manifestation of cHCC-CC; and more importantly, it plays a significant prognostic role, which may alter the RFS prognosis of cHCC-CC.

Radiomics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Preoperative Differentiation of Combined Hepatocellular-Cholangiocarcinoma from Hepatocellular Carcinoma: A Multi-Center Study

Purpose

To explore whether texture features based on magnetic resonance can distinguish diseases combined hepatocellular-cholangiocarcinoma (cHCC-CC) from hepatocellular carcinoma (HCC) before operation.

Methods

The clinical baseline data and MRI information of 342 patients with pathologically diagnosed cHCC-CC and HCC in two medical centers were collected. The data were divided into the training set and the test set at a ratio of 7:3. MRI images of tumors were segmented with ITK-SNAP software, and python open-source platform was used for texture analysis. Logistic regression as the base model, mutual information (MI) and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to select the most favorable features. The clinical, radiomics, and clinic-radiomics model were constructed based on logistic regression. The model's effectiveness was comprehensively evaluated by the receiver operating characteristic (ROC) curve, area under the curve (AUC), sensitivity, specificity, and Youden index which is the main, and the model results were exported by SHapley Additive exPlanations (SHAP).

Results

A total of 23 features were included. Among all models, the arterial phase-based clinic-radiomics model showed the best performance in differentiating cHCC-CC from HCC before an operation, with the AUC of the test set being 0.863 (95% CI: 0.782 to 0.923), the specificity and sensitivity being 0.918 (95% CI: 0.819 to 0.973) and 0.738 (95% CI: 0.580 to 0.861), respectively. SHAP value results showed that the RMS was the most important feature affecting the model.

Conclusion

Clinic-radiomics model based on DCE-MRI may be useful to distinguish cHCC-CC from HCC in a preoperative setting, especially in the arterial phase, and RMS has the greatest impact.

Preoperative Differentiation of Combined Hepatocellular-Cholangiocarcinoma From Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Nomogram Based on Ultrasonographic Features and Clinical Indicators

OBJECTIVE

To establish a predictive nomogram to distinguish combined hepatocellular-cholangiocarcinoma (CHC) from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) based on preoperative clinical and ultrasound findings.

METHODS

A total of 261 patients with pathologically confirmed primary liver cancers (PLCs) were enrolled in this retrospective study, comprising 87 CHCs, 87 HCCs, and 87 ICCs matched by propensity score matching. Patients were randomly assigned to a training cohort and a validation one at the ratio of 7:3. A nomogram integrating ultrasound imaging characteristics and clinical features was established based on the independent risk factors selected by least absolute shrinkage and selection operator (LASSO) regression. The performance of the nomogram was evaluated in the training and validation cohorts in terms of discrimination, calibration, and clinical usefulness.

RESULTS

The nomogram, consisting of ultrasound imaging features (shape and margin on B-mode ultrasound, enhanced pattern on contrast-enhanced ultrasound) and clinical information [elevated alpha fetoprotein (AFP) level and serum protein electrophoresis (SPE) α1 level], showed promising performance in differentiating CHC from HCC and ICC, with the concordance index (C-index) of 0.8275 and 0.8530 in the training cohort and the validation cohort, respectively. Hosmer-Lemeshow test and the calibration curves suggested good consistency between predictions and observations. High clinical practicability was confirmed by the decision curve analysis.

CONCLUSIONS

The nomogram based on clinical and ultrasound imaging characteristics showed good performance in the discrimination of CHC from other subtypes of PLC and would be valuable in clinical decision-making.

Update on the association of hepatitis B with intrahepatic cholangiocarcinoma: Is there new evidence?

Intrahepatic cholangiocarcinoma (iCCA) is a subgroup of cholangiocarcinoma that accounts for about 10%-20% of the total cases. Infection with hepatitis B virus (HBV) is one of the most important predisposing factors leading to the formation of iCCA. It has been recently estimated based on abundant epidemiological data that the association between HBV infection and iCCA is strong with an odds ratio of about 4.5. The HBV-associated mechanisms that lead to iCCA are under intense investigation. The diagnosis of iCCA in the context of chronic liver disease is challenging and often requires histological confirmation to distinguish from hepatocellular carcinoma. It is currently unclear whether antiviral treatment for HBV can decrease the incidence of iCCA. In terms of management, surgical resection remains the mainstay of treatment. There is a need for effective treatment modalities beyond resection in both first- and second-line treatment. In this review, we summarize the epidemiological evidence that links the two entities, discuss the pathogenesis of HBV-associated iCCA, and present the available data on the diagnosis and management of this cancer.

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.

Hepatocellular Carcinoma With Atypical Imaging Features: Review of the Morphologic Hepatocellular Carcinoma Subtypes With Radiology-Pathology Correlation

Hepatocellular carcinoma (HCC) is the fastest growing cause of cancer death in the United States with the incidence rate more than doubling in 20 years. HCC is unique since a noninvasive diagnosis can be achieved with imaging alone when specific clinical criteria and imaging characteristics are met, obviating the need for tissue sampling. However, HCC is a highly heterogeneous neoplasm. Atypical HCC subtypes vary significantly in their morphology, which can be attributed to specific histologic and molecular features, and can cause deviations from the classic imaging characteristics. The different morphologic subtypes of HCC frequently present a diagnostic challenge for radiologists and pathologists since their imaging and pathologic features can overlap with those of non-HCC malignancies. Identifying an atypical subtype can have important clinical implications. Liver transplant, albeit a scarce and limited resource, is the optimal treatment for conventional HCC, potentially curing both the tumor and the underlying pre-malignant condition. Some HCC subtypes as well as mimickers are associated with unacceptably high recurrence and poor outcome after transplant, and there remains limited data on the role and prognosis of liver transplantation for treatment of rare HCC subtypes. Other subtypes tend to recur later than classic HCC, potentially requiring a different follow-up scheme. This review will discuss the appearance of different HCC subtypes in relation to their histopathologic features. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

Development and Validation of a Nomogram for Differentiating Combined Hepatocellular Cholangiocarcinoma From Intrahepatic Cholangiocarcinoma

Objectives

To establish a nomogram based on preoperative laboratory study variables using least absolute shrinkage and selection operator (LASSO) regression for differentiating combined hepatocellular cholangiocarcinoma (cHCC) from intrahepatic cholangiocarcinoma (iCCA).

Methods

We performed a retrospective analysis of iCCA and cHCC patients who underwent liver resection. Blood signatures were established using LASSO regression, and then, the clinical risk factors based on the multivariate logistic regression and blood signatures were combined to establish a nomogram for a differential preoperative diagnosis between iCCA and cHCC. The differential accuracy ability of the nomogram was determined by Harrell’s index (C-index) and decision curve analysis, and the results were validated using a validation set. Furthermore, patients were categorized into two groups according to the optimal cut-off values of the nomogram-based scores, and their survival differences were assessed using Kaplan-Meier curves.

Results

A total of 587 patients who underwent curative liver resection for iCCA or cHCC between January 2008 and December 2017 at West China Hospital were enrolled in this study. The cHCC score was based on the personalized levels of the seven laboratory study variables. On multivariate logistic analysis, the independent factors for distinguishing cHCC were age, sex, biliary duct stones, and portal hypertension, all of which were incorporated into the nomogram combined with the cHCC-score. The nomogram had a good discriminating capability, with a C-index of 0.796 (95% CI, 0.752–0.840). The calibration plot for distinguishing cHCC from iCCA showed optimal agreement between the nomogram prediction and actual observation in the training and validation sets. The decision curves indicated significant clinical usefulness.

Conclusion

The nomogram showed good accuracy for the differential diagnosis between iCCA and cHCC preoperatively, and therapeutic decisions would improve if it was applied in clinical practice.

Clinicopathological features and recurrence patterns of combined hepatocellular-cholangiocarcinoma

Background

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a primary liver carcinoma with both hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) components. We examined the clinicopathological characteristics and recurrence patterns of cHCC-CCA. Because of the rarity of cHCC-CCA, its etiology, clinicopathological features, and prognosis in comparison with other primary liver carcinoma remain unknown. Its recurrence pattern and sites in particular also need to be elucidated.

Methods

All patients who underwent hepatectomy for primary liver malignancies between 2005 and 2015 were retrospectively included in this study.

Results

Eight hundred and ninety-four hepatectomies were performed. Nineteen cases of cHCC-CCA (2.1%) in 16 patients were enrolled. Three patients underwent re-hepatectomy. The background of hepatitis viruses and tumor marker patterns of cHCC-CCA were similar to those of HCC and dissimilar to those of intrahepatic CCA (iCCA). Biliary invasion was common in cHCC-CCA and iCCA. The 5-year overall survival values of the cHCC-CCA, HCC, and iCCA patients were 44.7%, 56.6%, and 38.5%, respectively. The 5-year recurrence-free survival values of the cHCC-CCA, HCC, and iCCA patients were 12.2%, 28.7%, and 32.9%, respectively. The liver was the most common recurrence site. Unlike HCC, however, the lymph node was the second-most common recurrence site in both cHCC-CCA and iCCA. Pathological samples of the recurrent lesions were obtained in six patients, and four had cHCC-CCA recurrence pathologically.

Conclusion

cHCC-CCA had a mixture of characteristics of HCC and iCCA. Many cases of cHCC-CCA remained cHCC-CCA pathologically even after recurrence.

Diagnostic performance of the LR-M criteria and spectrum of LI-RADS imaging features among primary hepatic carcinomas

PURPOSE

To evaluate the diagnostic performance of LR-M criteria for differentiating hepatocellular carcinoma, intrahepatic mass-forming cholangiocarcinoma, and combined hepatocellular-cholangiocarcinoma and to compare the imaging features of each type.

METHODS

In this retrospective study, 110 patients were surgically diagnosed with cholangiocarcinoma (n = 67) and combined hepatocellular-cholangiocarcinoma (n = 43) at a single tertiary hospital between 2013 and 2018. Among them, those with risk factors were enrolled (16 cholangiocarcinomas and 33 combined hepatocellular-cholangiocarcinomas). Forty-nine other patients with size-matched hepatocellular carcinoma were selected as a control group. Two independent readers evaluated the imaging findings of the preoperative MRIs based on LI-RADS version 2018 and assigned an LI-RADS category. The diagnostic performance of the LR-M criteria for diagnosing cholangiocarcinoma or combined hepatocellular-cholangiocarcinoma was evaluated, and the imaging features were compared. The imaging findings of the tumors in patients without risk factors (51 cholangiocarcinomas and 10 combined hepatocellular-cholangiocarcinomas) were evaluated for subgroup analysis.

RESULTS

In the non-hepatocellular carcinoma group, 33 patients were categorized into LR-M and 14 patients into LR-5 (67.3% and 28.6%, respectively), while 5 patients with hepatocellular carcinoma were categorized into LR-M and 38 patients into LR-5 (10.2% and 77.6%, respectively). Sensitivity and specificity of the LR-M criteria were 67.3% and 89.8%, respectively. When more than two LR-M features were present, cholangiocarcinoma or combined hepatocellular-cholangiocarcinoma were suggested with a specificity of 95.9%.

CONCLUSION

The diagnostic performance of the LR-M criteria is acceptable with moderate sensitivity and high specificity for both cholangiocarcinoma and combined hepatocellular-cholangiocarcinoma. Imaging findings of primary hepatic carcinomas should be understood as a spectrum.

Ultrasound-Based Radiomics Analysis for Preoperatively Predicting Different Histopathological Subtypes of Primary Liver Cancer

Background

Preoperative identification of hepatocellular carcinoma (HCC), combined hepatocellular–cholangiocarcinoma (cHCC-ICC), and intrahepatic cholangiocarcinoma (ICC) is essential for treatment decision making. We aimed to use ultrasound-based radiomics analysis to non-invasively distinguish histopathological subtypes of primary liver cancer (PLC) before surgery.

Methods

We retrospectively analyzed ultrasound images of 668 PLC patients, comprising 531 HCC patients, 48 cHCC-ICC patients, and 89 ICC patients. The boundary of a tumor was manually determined on the largest imaging slice of the ultrasound medicine image by ITK-SNAP software (version 3.8.0), and then, the high-throughput radiomics features were extracted from the obtained region of interest (ROI) of the tumor. The combination of different dimension-reduction technologies and machine learning approaches was used to identify important features and develop the moderate radiomics model. The comprehensive ability of the radiomics model can be evaluated by the area under the receiver operating characteristic curve (AUC).

Results

After digitally processing tumor ultrasound images, 5,234 high-throughput radiomics features were obtained. We used the Spearman + least absolute shrinkage and selection operator (LASSO) regression method for feature selection and logistics regression for modeling to develop the HCC-vs-non-HCC radiomics model (composed of 16 features). The Spearman + statistical test + random forest methods were used for feature selection, and logistics regression was applied for modeling to develop the ICC-vs-cHCC-ICC radiomics model (composed of 19 features). The overall performance of the radiomics model in identifying different histopathological types of PLC was moderate, with AUC values of 0.854 (training cohort) and 0.775 (test cohort) in the HCC-vs-non-HCC radiomics model and 0.920 (training cohort) and 0.728 (test cohort) in the ICC-vs-cHCC-ICC radiomics model.

Conclusion

Ultrasound-based radiomics models can help distinguish histopathological subtypes of PLC and provide effective clinical decision making for the accurate diagnosis and treatment of PLC.

Integrated nomograms to predict overall survival and recurrence-free survival in patients with combined hepatocellular cholangiocarcinoma (cHCC) after liver resection

The current clinical classification of primary liver cancer is unable to efficiently predict the prognosis of combined hepatocellular cholangiocarcinoma (cHCC). Accurate satellite nodules (SAT) and microvascular invasion (MVI) prediction in cHCC patients is very important for treatment decision making and prognostic evaluation. The aim of this work was to explore important factors affecting the prognosis of cHCC patients after liver resection and to develop preoperative nomograms to predict SAT and MVI in cHCC patients. The nomogram was developed using the data from 148 patients who underwent liver resection for cHCC patients at our hospital between January 2006 and December 2014. Based on the results of the multivariate analysis, a nomogram integrating all significant independent factors affecting overall survival and recurrence-free survival was constructed to predict the prognosis of cHCC. Next, risk factors for SAT and MVI were evaluated with logistic regression. Blood signatures were established using the LASSO regression, and then, we combined the clinical risk factors and blood signatures of the patients to establish predictive models for SAT and MVI. The C-index of the nomogram for predicting survival was 0.685 (95% CI, 0.638 to 0.732), which was significantly higher than the C-index for other liver cancer classification systems.

Combined hepatocellular-cholangiocarcinoma: which preoperative clinical data and conventional MRI characteristics have value for the prediction of microvascular invasion and clinical significance?

Objectives

To explore which preoperative clinical data and conventional MRI findings may indicate microvascular invasion (MVI) of combined hepatocellular-cholangiocarcinoma (cHCC-CCA) and have clinical significance.

Methods

The study enrolled 113 patients with histopathologically confirmed cHCC-CCA (MVI-positive group [n = 56], MVI-negative group [n = 57]). Two radiologists retrospectively assessed the preoperative MRI features (qualitative analysis of morphology and dynamic enhancement features), and each lesion was assigned according to the LI-RADS. Preoperative clinical data were also evaluated. Logistic regression analyses were used to assess the relative value of these parameters as potential predictors of MVI. Recurrence-free survival (RFS) rates after hepatectomy in the two groups were estimated using Kaplan–Meier survival curves and compared using the log-rank test.

Results

The majority of cHCC-CCAs were categorized as LR-M. On multivariate analysis, a higher serum AFP level (OR, 0.523; 95% CI, 0.282–0.971; p = 0.040), intratumoral fat deposition (OR, 14.368; 95% CI, 2.749–75.098; p = 0.002), and irregular arterial peritumoral enhancement (OR, 0.322; 95% CI, 0.164–0.631; p = 0.001) were independent variables associated with the MVI of cHCC-CCA. After hepatectomy, patients with MVI of cHCC-CCA showed earlier recurrence than those without MVI (hazard ratio [HR], 0.402; 95% CI, 0.189–0.854, p = 0.013).

Conclusion

A higher serum AFP level and irregular arterial peritumoral enhancement are potential predictive biomarkers for the MVI of cHCC-CCA, while intratumoral fat detected on MRI suggests a low risk of MVI. Furthermore, cHCC-CCAs with MVI may have worse surgical outcomes with regard to early recurrence than those without MVI.

Key Points

• Higher serum levels of AFP combined with irregular arterial peritumoral enhancement are independent risk factors for the MVI of cHCC-CCA, while fat deposition might be a protective factor.

• cHCC-CCA with MVI may have a higher risk of early recurrence after surgery.

• Most cHCC-CCAs were categorized as LR-M in this study, and no significant difference was found in MVI based on LI-RADS category.

Differentiation combined hepatocellular and cholangiocarcinoma from intrahepatic cholangiocarcinoma based on radiomics machine learning

BACKGROUND

Combined hepatocellular and cholangiocarcinoma (CHC) and intrahepatic cholangiocarcinoma (ICC) are hard to identify in clinical practice preoperatively. This study looked to develop and confirm a radiomics-based model for preoperative differentiation CHC from ICC.

METHODS

The model was developed in 86 patients with ICC and 46 CHC, confirmed in 37 ICC and 20 CHC, and data were collected from January 2014 to December 2018. The radiomics scores (Radscores) were built from radiomics features of contrast-enhanced computed tomography in 12 regions of interest (ROI). The Radscore and clinical-radiologic factors were integrated into the combined model using multivariable logistic regression. The best-combined model constructed the radiomics-based nomogram, and the performance was assessed concerning its calibration, discrimination, and clinical usefulness.

RESULTS

The radiomics features extracted from tumor ROI in the arterial phase (AP) with preprocessing were selected to build Radscore and yielded an area under the curve (AUC) of 0.800 and 0.789 in training and validation cohorts, respectively. The radiomics-based model contained Radscore and 4 clinical-radiologic factors showed the best performance (training cohort, AUC =0.942; validation cohort, AUC =0.942) and good calibration (training cohort, AUC =0.935; validation cohort, AUC =0.931).

CONCLUSIONS

The proposed radiomics-based model may be used conveniently to the preoperatively differentiate CHC from ICC.