A blood test-based nomogram to predict the progression-free survival of patients with intrahepatic cholangiocarcinoma after surgical resection

TL;DR

This study developed a blood test-based model to predict how long intrahepatic cholangiocarcinoma patients will remain cancer-free after surgery, outperforming traditional staging systems.

Contribution

A novel nomogram model using blood markers to predict progression-free survival in ICC patients after surgery, showing better performance than the AJCC-TNM system.

Findings

Pathological differentiation, CA19-9, NLR, and tMON were identified as independent predictors of progression-free survival.

The nomogram model showed higher AUC than the AJCC-TNM staging system for predicting PFS.

High-risk patients had significantly shorter median PFS compared to low-risk patients based on the nomogram's risk score.

Abstract

Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive malignancy with poor prognosis, and there is currently a lack of effective prognostic prediction models. The aim of this study was to develop a novel nomogram model based on blood tests for predicting predictors of progression free survival (PFS) in ICC patients.

A total of 99 ICC patients (70 for training, 29 for validation) were included in this study. Hematological indices and clinicopathological data were collected from ICC patients undergoing surgical resection. The independent predictors of PFS were screened by univariate and multivariate Cox regression analysis, and a nomogram model was constructed. The calibration curve was used to evaluate the consistency between the observed results and the predicted probability, and the model discrimination was evaluated by receiver operating characteristic curve (ROC). According to the risk score calculated by the constructed nomogram, patients were divided into high-risk and low-risk groups, and the predictive performance of nomogram was further tested by Kaplan Meier.

The median follow-up time of this study was 7.8 months (range: 1 ~ 69 months). We found that pathological differentiation, CA19-9, neutrophil-to-lymphocyte ratio (NLR) and after-treatment Monocyte count (MON)/before-treatment MON (tMON) were independent factors affecting the PFS of postoperative ICC patients. Based on risk factors, a nomogram prediction model was constructed. ROC analysis revealed that the area under the curve (AUC) of the nomogram for predicting PFS was higher than the AJCC-TNM staging system(P<0.05). The calibration curve and decision curve analysis (DCA) showed that the nomogram had high prognostic accuracy and clinical applicability. The risk score calculated by nomogram could divide ICC patients into high-risk and low-risk groups. The median PFS of the high-risk group was significantly shorter than that of the low-risk group (P <0.05).

The nomogram can serve as a valuable supplementary tool for predicting PFS in ICC patients after initial surgical resection. Its performance is better than the traditional TNM staging system. The model provides clinicians with an individualized prognostic assessment tool by integrating easily available blood markers, which is helpful to optimize postoperative monitoring and adjuvant treatment strategies.

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