Nomograms predict survival outcome of Klatskin tumors patients

Galanin System in the Human Bile Duct and Perihilar Cholangiocarcinoma

BACKGROUND

Perihilar cholangiocarcinoma (pCCA) is characterised by poor outcomes. Early diagnosis is essential for patient survival. The peptide galanin (GAL) and its receptors GAL1-3 are expressed in various tumours. Detailed characterisation of the GAL system in pCCA is lacking. Our study sought to characterise GAL and GAL1-3 receptor (GAL1-3-R) expression in the healthy human bile duct, in cholestasis and pCCA.

METHODS

Immunohistochemical staining was performed in healthy controls (n = 5) and in the peritumoural tissues (with and without cholestasis) (n = 20) and tumour tissues of pCCA patients (n = 33) using validated antibodies. The score values of GAL and GAL1-3-R expression were calculated and statistically evaluated.

RESULTS

GAL and GAL1-R were expressed in various bile duct cell types. GAL2-R was only slightly but still expressed in almost all the examined tissues, and GAL3-R specifically in cholangiocytes and capillaries. In a small pCCA patient cohort (n = 18), high GAL expression correlated with good survival, whereas high GAL3-R correlated with poor survival.

CONCLUSIONS

Our in-depth characterisation of the GAL system in the healthy human biliary duct and pCCA in a small patient cohort revealed that GAL and GAL3-R expression in tumour cells of pCCA patients could potentially represent suitable biomarkers for survival.

Development and validation of a nomogram to predict cancer-specific survival with unresected cholangiocarcinoma undergoing external radiotherapy

Objective

To analyze the prognostic factors of patients with cholangiocarcinoma (CCA) who were unresected and received radiotherapy to establish a nomogram model for the prediction of patient cancer-specific survival (CSS).

Methods

Suitable patient cases were selected from the Surveillance, Epidemiology, and End Results (SEER) database, survival rates were calculated using the Kaplan-Meier method, prognostic factors were analyzed by Lasso, Cox regression, and nomogram was developed based on independent prognostic factors to predict 6 and 12 months CSS. The consistency index (C-index), calibration curve, and decision curve analysis (DCA) were tested for the predictive efficacy of the model, respectively.

Results

The primary site, tumor size, T-stage, M-stage, and chemotherapy (P < 0.05) were identified as independent risk factors after Cox and Lasso regression analysis. Patients in training cohort had a 6 months CSS rates was 68.6 ± 2.6%, a 12-month CSS rates was 49.0 ± 2.8%. The median CSS time of 12.00 months (95% CI: 10.17-13.83 months). The C-index was 0.664 ± 0.039 for the training cohort and 0.645 ± 0.042 for the validation cohort. The nomogram predicted CSS and demonstrated satisfactory and consistent predictive performance in 6 (73.4 vs. 64.9%) and 12 months (72.2 vs. 64.9%), respectively. The external validation calibration plot is shown AUC for 6- and 12-month compared with AJCC stage was (71.2 vs. 63.0%) and (65.9 vs. 59.8%). Meanwhile, the calibration plot of the nomogram for the probability of CSS at 6 and 12 months indicates that the actual and nomogram predict that the CSS remains largely consistent. DCA showed that using a nomogram to predict CSS results in better clinical decisions compared to the AJCC staging system.

Conclusion

A nomogram model based on clinical prognostic characteristics can be used to provide CSS prediction reference for patients with CCA who have not undergone surgery but have received radiotherapy.

Hospitalisation costs of primary liver cancer in Australia: evidence from a data-linkage study

ObjectiveThis study aimed to estimate the public hospital costs associated with primary liver cancer (PLC) in the first and second years following the cancer diagnosis.MethodsThis study linked administrative datasets of patients diagnosed with PLC in Victoria, Australia, from January 2008 to December 2015. The health system perspective was adopted to estimate the direct healthcare costs associated with PLC, based on inpatient and emergency costs. Costs were estimated for the first 12 months and 12-24 months after the PLC diagnosis and expressed in 2017 Australian dollars (A$). The cost estimated was then extrapolated nationally. The linear mixed model with a Box-Cox transformation of the costs was used to explore the relationship between costs and patients' sociodemographic and clinical characteristics.ResultsFor the first 12 months, the total and annual per-patient cost was A$211.4 million and A$63 664, respectively. Costs for the subsequent year were A$49.7 million and A$46 751, respectively. Regarding the cost extrapolation to Australia, the total cost was A$137 million for the first 12 months after notification and A$42.6 million for the period from 12 to 24 months. Higher costs per episode of care were mostly associated with older age, hepatocellular carcinoma type of PLC, metropolitan hospitals, and Asian birth region.ConclusionThis study showed the public hospital admission and emergency costs associated with PLC and the substantial economic burden this cancer has placed on the Australian health system.

Development and Validation of a Prognostic Model to Predict Recurrence-Free Survival After Curative Resection for Perihilar Cholangiocarcinoma: A Multicenter Study

Background

Recurrence is the main cause of death in perihilar cholangiocarcinoma (pCCA) patients after surgery. Identifying patients with a high risk of recurrence is important for decision-making regarding neoadjuvant therapy to improve long-term outcomes.

Aim

The objective of this study was to develop and validate a prognostic model to predict recurrence-free survival (RFS) after curative resection of pCCA.

Methods

Patients following curative resection for pCCA from January 2008 to January 2016 were identified from a multicenter database. Using random assignment, 70% of patients were assigned to the training cohort, and the remaining 30% were assigned to the validation cohort. Independent predictors of RFS after curative resection for pCCA were identified and used to construct a prognostic model. The predictive performance of the model was assessed using calibration curves and the C-index.

Results

A total of 341 patients were included. The median overall survival (OS) was 22 months, and the median RFS was 14 months. Independent predictors associated with RFS included lymph node involvement, macrovascular invasion, microvascular invasion, maximum tumor size, tumor differentiation, and carbohydrate antigen 19-9. The model incorporating these factors to predict 1-year RFS demonstrated better calibration and better performance than the 8th American Joint Committee on Cancer (AJCC) staging system in both the training and validation cohorts (C-indexes: 0.723 vs. 0.641; 0.743 vs. 0.607).

Conclusions

The prognostic model could identify patients at high risk of recurrence for pCCA to inform patients and surgeons, help guide decision-making for postoperative adjuvant therapy, and improve survival.

Prognostic nomogram for predicting cancer-specific survival in patients with resected hilar cholangiocarcinoma: a large cohort study

BACKGROUND

The aim of the study was to establish and validate a novel prognostic nomogram of cancer-specific survival (CSS) in resected hilar cholangiocarcinoma (HCCA) patients.

METHODS

A training cohort of 536 patients and an internal validation cohort of 270 patients were included in this study. The demographic and clinicopathological variables were extracted from the Surveillance, Epidemiology and End Results (SEER) database. Univariate and multivariate Cox regression analysis were performed in the training cohort, followed by the construction of nomogram for CSS. The performance of the nomogram was assessed by concordance index (C-index) and calibration plots and compared with the American Joint Committee on Cancer (AJCC) staging systems. Decision curve analysis (DCA) was applied to measure the predictive power and clinical value of the nomogram.

RESULTS

The nomogram incorporating age, tumor size, tumor grade, lymph node ratio (LNR) and T stage parameters was with a C-index of 0.655 in the training cohort, 0.626 in the validation cohort, compared with corresponding 0.631, 0.626 for the AJCC 8th staging system. The calibration curves exhibited excellent agreement between CSS probabilities predicted by nomogram and actual observation in the training cohort and validation cohort. DCA indicated that this nomogram generated substantial clinical value.

CONCLUSIONS

The proposed nomogram provided a more accurate prognostic prediction of CSS for individual patients with resected HCCA than the AJCC 8th staging system, which might be served as an effective tool to stratify resected HCCA patients with high risk and facilitate optimizing therapeutic benefit.

Development and validation of preoperative magnetic resonance imaging-based survival predictive nomograms for patients with perihilar cholangiocarcinoma after radical resection: A pilot study

PURPOSE

We aim to develop survival predictive tools to inform clinical decision-making in perihilar cholangiocarcinoma (pCCA).

MATERIALS AND METHODS

A total of 184 patients who had curative resection and magnetic resonance imaging (MRI) examination for pCCA between January 2010 and December 2018 were enrolled. 110 patients were randomly selected for model development, while the other 74 patients for model testing. Preoperative clinical, laboratory, and imaging data were analyzed. Preoperative clinical predictors were used independently or integrated with radiomics signatures to construct different preoperative models through the multivariable Cox proportional hazards method. The nomograms were constructed to predict overall survival (OS), and the performance of which was evaluated by the discrimination ability, time-dependent receiver operating characteristic curve (ROC), calibration curve, and decision curve.

RESULTS

The clinical model (Model^clinic) was constructed based on three independent variables including preoperative CEA, cN stage, and invasion of hepatic artery in images. The model yield the best performance (Model^{clinic&AP&PVP}) was build using three independent variables, Signature^AP and Signature^PVP. In training and testing cohorts, the concordance indexes (C-indexes) of Model^clinic were 0.846 (95 % CI, 0.735-0.957) and 0.755 (95 % CI, 0.540-969), and Model^{clinic&AP&PVP} achieved C-indexes of 0.962 (95 % CI, 0.905-1) and 0.814 (95 % CI, 0.569-1). Both Model^clinic and Model^{clinic&AP&PVP} outperformed the TNM staging system. Good agreement was observed in the calibration curves, and favorable clinical utility was validated using the decision curve analysis for Model^clinic and Model^{clinic&AP&PVP}.

CONCLUSION

Two preoperative nomograms were constructed to predict 1-, 3-, and 5-years survival for individual pCCA patients, demonstrating the potential for clinical application to assist decision-making.

Surgical outcomes of perihilar cholangiocarcinoma based on the learning curve of a single surgeon at a tertiary academic hospital: A retrospective study

Backgrounds/Aims

Although it is difficult to master the surgical learning curve for treatment of perihilar cholangiocarcinoma (HCCA), there have been no studies on surgical outcomes between a novice and an experienced surgeon. Thus, the current study attempted to evaluate surgical outcomes from a single surgeon based on learning curve for surgical treatment of HCCA.

Methods

From January 2008 to December 2016, a single surgeon performed surgical treatment for 108 patients with HCCA at Severance Hospital, Seoul, Korea. Among them, 101 patients with curative surgical resection were included in this study. The learning curve was assessed by a moving average graph and CUSUM method using operation time. Surgical outcomes between the early period group (EPG) and the late period group (LPG) were compared according to learning curve.

Results

Operation time (603.17±117.59 and 432.03±91.77 minutes; p<0.001), amount of bleeding during operation (1127.86±689.54 and 613.05±548.31 ml; p<0.001), and severe complication rates (47.6% and 27.1%, p=0.034) were significantly smaller in the LPG. There was no significant difference in R0 resection rate (85.7% and 76.3%; p=0.241) as well as long-term survival rate.

Conclusions

In this study, operation time, amount of bleeding during operation, length of hospital stay, and severe complication rate were improved after stabilization of the learning curve. However, R0 resection rate and survival outcomes were not significantly influenced by the learning curve for surgical treatment of HCCA.