Dataset Information

Morbidity, Prognostic Factors, and Competing Risk Nomogram for Combined Hepatocellular-Cholangiocarcinoma.

ABSTRACT:

Background

Combined hepatocellular-cholangiocarcinoma (CHC) is a rare and heterogeneous histological subtype of primary liver cancer, which is still poorly understood. This study aimed to describe the epidemiological and clinical features, investigate the prognostic indicators, and develop a competing risk nomogram for CHC.

Methods

The study cohort was taken from the Surveillance, Epidemiology, and End Results database. The annual percent change (APC) in incidence was calculated using the joinpoint regression. The nomogram was developed based on multivariate competing risk survival analyses and validated by calibration curves. Akaike information criterion, Bayesian information criterion, Harrell's C-index, and area under the receiver operating characteristic curves were obtained to compare prognostic performance. Decision curve analysis was introduced to examine the clinical value of the models.

Results

The overall incidence of CHC was 0.062 per 100,000 individuals in 2004 and 0.081 per 100,000 individuals in 2018, with an APC of 1.0% (P > 0.05). CHC displayed intermediate clinicopathological features of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Race, tumor size, vascular invasion, extrahepatic invasion, distant metastasis, grade, surgery, and Metavir stage were confirmed as the independent predictors of cancer-specific survival. The constructed nomogram was well calibrated, which showed better discrimination power and higher net benefits than the current American Joint Committee on Cancer staging system. Patients with liver transplantation had better survival than those with hepatectomy, especially patients within the Milan Criteria (P=0.022 and P=0.015). There was no survival difference between liver transplantation and hepatectomy in patients beyond the Milan Criteria (P=0.340).

Conclusion

The morbidity of CHC remained stable between 2004 and 2018. The constructed nomogram could predict the prognosis with good performance, which was meaningful to individual treatment strategies optimization. CHC patients should also be considered as potential liver transplantation recipients, especially those within the Milan Criteria, but the finding still needs more evidence to be further confirmed.

SUBMITTER: Chen X

PROVIDER: S-EPMC8683178 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCombined hepatocellular cholangiocarcinoma (CHCC-CCA) is a rare type of primary liver cancer having aggressive behavior. Few studies have investigated the prognostic factors of CHCC-CCA. Therefore, this study aimed to establish a nomogram to evaluate the risk of microvascular invasion (MVI) and the presence of satellite nodules and lymph node metastasis (LNM), which are associated with prognosis.MethodsOne hundred and seventy-one patients pathologically diagnosed with CHCC-CCA were divided into a training set (n=116) and validation set (n=55). Logistic regression analysis was used to assess the relative value of clinical factors associated with the presence of MVI and satellite nodules. The least absolute shrinkage and selection operator (LASSO) algorithm was used to establish the imaging model of all outcomes, and to build clinical model of LNM. Nomograms were constructed by incorporating clinical risk factors and imaging features. The model performance was evaluated on the training and validation sets to determine its discrimination ability, calibration, and clinical utility. Kaplan Meier analysis and time dependent receiver operating characteristic (ROC) were displayed to evaluate the prognosis value of the predicted nomograms of MVI and satellite nodule.ResultsA nomogram comprising the platelet to lymphocyte ratio (PLR), albumin-to-alkaline phosphatase ratio (AAPR) and imaging model was established for the prediction of MVI. Carcinoembryonic antigen (CEA) level and size were combined with the imaging model to establish a nomogram for the prediction of the presence of satellite nodules. Favorable calibration and discrimination were observed in the training and validation sets for the MVI nomogram (C-indexes of 0.857 and 0.795), the nomogram for predicting satellite nodules (C-indexes of 0.919 and 0.883) and the LNM nomogram (C-indexes of 0.872 and 0.666). Decision curve analysis (DCA) further confirmed the clinical utility of the nomograms. The preoperatively predicted MVI and satellite nodules by the combined nomograms achieved satisfactory performance in recurrence-free survival (RFS) and overall survival (OS) prediction.ConclusionsThe proposed nomograms incorporating clinical risk factors and imaging features achieved satisfactory performance for individualized preoperative predictions of MVI, the presence of satellite nodules, and LNM. The prediction models were demonstrated to be good indicator for predicting the prognosis of CHCC-CCA, facilitating treatment strategy optimization for patients with CHCC-CCA.

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Project description:Simple summaryAccurately estimate the prognosis of patients with ECCA is important. However, the TNM system has some limitations, such as low accuracy, exclusion of other factors (e.g., age and sex), and poor performance in predicting individual survival risk. In contrast, a nomogram-based clinical model related to a comprehensive analysis of all risk factors is intuitive and straightforward, facilitating the probabilistic analysis of tumor-related risk factors. Simultaneously, a nomogram can also effectively drive personalized medicine and facilitate clinicians for prognosis prediction. Therefore, we construct a novel practical nomogram and risk stratification system to predict CSS in patients with ECCA.BackgroundAccurately estimate the prognosis of patients with extrahepatic cholangiocarcinoma (ECCA) was important, but the existing staging system has limitations. The present study aimed to construct a novel practical nomogram and risk stratification system to predict cancer-specific survival (CSS) in ECCA patients.Methods3415 patients diagnosed with ECCA between 2010 and 2015 were selected from the SEER database and randomized into a training cohort and a validation cohort at 7:3. The nomogram was identified and calibrated using the C-index, receiver operating characteristic curve (ROC), and calibration plots. Decision curve analysis (DCA), net reclassification index (NRI), integrated discrimination improvement (IDI) and the risk stratification were used to compare the nomogram with the AJCC staging system.ResultsNine variables were selected to establish the nomogram. The C-index (training cohort:0.785; validation cohort:0.776) and time-dependent AUC (>0.7) showed satisfactory discrimination. The calibration plots also revealed that the nomogram was consistent with the actual observations. The NRI (training cohort: 1-, 2-, and 3-year CSS:0.27, 0.27,0.52; validation cohort:1-,2-,3-year CSS:0.48,0.13,0.34), IDI (training cohort: 1-, 2-, 3-year CSS:0.22,0.18,0.16; validation cohort: 1-,2-,3-year CSS:0.18,0.16,0.17), and DCA indicated that the established nomogram significantly outperformed the AJCC staging system (P<0.05) and had better recognition compared to the AJCC staging system.ConclusionsWe developed a practical prognostic nomogram to help clinicians assess the prognosis of patients with ECCA.