Nomogram based on clinical and preoperative CT features for predicting the early recurrence of combined hepatocellular-cholangiocarcinoma: a multicenter study.

To establish and validate a multiparameter prediction model for early recurrence after radical resection in patients diagnosed with combined hepatocellular-cholangiocarcinoma (cHCC-CC). This study reviewed the clinical characteristics and preoperative CT images of 143 cHCC-CC patients who underwent radical resection from three institutions. A total of 110 patients from institution 1 were randomly divided into training set (n = 78) and testing set (n = 32) in the ratio of 7-3. Univariate and multivariate logistic regression analysis were used to construct a nomogram prediction model in the training set, which was internally and externally validated in the testing set and the validation set (n = 33) from institutions 2 and 3. The area under the curve (AUC) of receiver operating characteristics (ROC), decision curve analysis (DCA), and calibration analysis were used to evaluate the model's performance. The combined model demonstrated superior predictive performance compared to the clinical model, the CT model, the pathological model and the clinic-CT model in predicting the early postoperative recurrence. The nomogram based on the combined model included AST, ALP, tumor size, tumor margin, arterial phase peritumoral enhancement, and MVI (Microvascular invasion). The model had AUCs of 0.89 (95% CI 0.81-0.96), 0.85 (95% CI 0.70-0.99), and 0.86 (95% CI 0.72-1.00) in the training, testing, and validation sets, respectively, indicating high predictive power. DCA showed that the combined model had good clinical value and correction effect. A nomogram incorporating clinical characteristics and preoperative CT features can be utilized to effectively predict the early postoperative recurrence in patients with cHCC-CC.

Zheng C ，Gu XT ，Huang XL ，Wei YC ，Chen L ，Luo NB ，Lin HS ，Jin-Yuan L ... -  《-》

Comprehensive radiomics nomogram for predicting survival of patients with combined hepatocellular carcinoma and cholangiocarcinoma.

Combined hepatocellular carcinoma (HCC) and cholangiocarcinoma (cHCC-CCA) is defined as a single nodule showing differentiation into HCC and intrahepatic cholangiocarcinoma and has a poor prognosis. To develop a radiomics nomogram for predicting post-resection survival of patients with cHCC-CCA. Patients with pathologically diagnosed cHCC-CCA were randomly divided into training and validation sets. Radiomics features were extracted from portal venous phase computed tomography (CT) images using the least absolute shrinkage and selection operator Cox regression and random forest analysis. A nomogram integrating the radiomics score and clinical factors was developed using univariate analysis and multivariate Cox regression. Nomogram performance was assessed in terms of the C-index as well as calibration, decision, and survival curves. CT and clinical data of 118 patients were included in the study. The radiomics score, vascular invasion, anatomical resection, total bilirubin level, and satellite lesions were found to be independent predictors of overall survival (OS) and were therefore included in an integrative nomogram. The nomogram was more strongly associated with OS (hazard ratio: 8.155, 95% confidence interval: 4.498-14.785, P < 0.001) than a model based on the radiomics score or only clinical factors. The area under the curve values for 1-year and 3-year OS in the training set were 0.878 and 0.875, respectively. Patients stratified as being at high risk of poor prognosis showed a significantly shorter median OS than those stratified as being at low risk (6.1 vs 81.6 mo, P < 0.001). This nomogram may predict survival of cHCC-CCA patients after hepatectomy and therefore help identify those more likely to benefit from surgery.

Tang YY ，Zhao YN ，Zhang T ，Chen ZY ，Ma XL ... -  《-》

Prediction of microvascular invasion in combined hepatocellular-cholangiocarcinoma based on preoperative contrast-enhanced CT and clinical data.

Microvascular invasion (MVI) is significantly associated with prognosis in combined hepatocellular-cholangiocarcinoma (cHCC-CCA) patients. The study aimed to explore the value of preoperative contrast-enhanced CT (CECT) features and clinical data in predicting MVI of cHCC-CCA. A total of 33 patients with MVI-positive and 27 with MVI-negative were enrolled, and underwent preoperative CECT imaging from January 2016 to December 2021. Preoperative clinical data and CECT imaging features were retrospectively analyzed. Univariable and multivariable logistic regression analysis were performed to identify potential predictors of MVI in cHCC-CCA. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curve and its area under the curve (AUC) value. The mean age of the patients was 54.0 ± 10.3 years, and 53 of the 60 patients (88.3%) were male. Preoperative imaging features on CECT (non-smooth contour and arterial phase peritumoral enhancement) and clinical data (hepatitis B virus (HBV) infection and protein induced by vitamin K absence or antagonist-II (PIVKA-II)) were highly distinct between those in MVI-positive group and MVI-negative group. On multivariable logistic analysis, arterial phase peritumoral enhancement (odds ratio (OR), 6.514; 95% confidence interval (CI), 1.588-26.728, p = 0.012) and high serum PIVKA-II level (OR, 6.810; 95% CI, 1.796-25.820, p = 0.005) were independent predictors associated with MVI of cHCC-CCA. The combination of these two predictors had high sensitivity (31/33, 93.9%; 95% CI, 80.4% - 98.3%) in the prediction of MVI with an area under the receiver operating characteristic (ROC) curve of 0.763 (95% CI, 0.635-0.863). The findings indicated that arterial phase peritumoral enhancement on preoperative CECT and high serum PIVKA-II level were identified as potential predictors for MVI in cHCC-CCA patients.

Zhang J ，Dong W ，Li Y ，Fu J ，Jia N ... -  《-》

DCE-MRI based radiomics nomogram for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from mass-forming intrahepatic cholangiocarcinoma.

To establish a radiomics nomogram based on dynamic contrast-enhanced (DCE) MR images to preoperatively differentiate combined hepatocellular-cholangiocarcinoma (cHCC-CC) from mass-forming intrahepatic cholangiocarcinoma (IMCC). A total of 151 training cohort patients (45 cHCC-CC and 106 IMCC) and 65 validation cohort patients (19 cHCC-CC and 46 IMCC) were enrolled. Findings of clinical characteristics and MR features were analyzed. Radiomics features were extracted from the DCE-MR images. A radiomics signature was built based on radiomics features by the least absolute shrinkage and selection operator algorithm. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical model. The radiomics signature and significant clinicoradiological variables were then incorporated into the radiomics nomogram by multivariate logistic regression analysis. Performance of the radiomics nomogram, radiomics signature, and clinical model was assessed by receiver operating characteristic and area under the curve (AUC) was compared. Eleven radiomics features were selected to develop the radiomics signature. The radiomics nomogram integrating the alpha fetoprotein, background liver disease (cirrhosis or chronic hepatitis), and radiomics signature showed favorable calibration and discrimination performance with an AUC value of 0.945 in training cohort and 0.897 in validation cohort. The AUCs for the radiomics signature and clinical model were 0.848 and 0.856 in training cohort and 0.792 and 0.809 in validation cohort, respectively. The radiomics nomogram outperformed both the radiomics signature and clinical model alone (p < 0.05). The radiomics nomogram based on DCE-MRI may provide an effective and noninvasive tool to differentiate cHCC-CC from IMCC, which could help guide treatment strategies. • The radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging is useful to preoperatively differentiate cHCC-CC from IMCC. • The radiomics nomogram showed the best performance in both training and validation cohorts for differentiating cHCC-CC from IMCC.

Zhou Y ，Zhou G ，Zhang J ，Xu C ，Zhu F ，Xu P ... -  《-》

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

Wang X ，Wang W ，Ma X ，Lu X ，Li S ，Zeng M ，Xu K ，Yang C ... -  《-》