ArticleFrontiers in oncology2025
A blood test-based nomogram to predict the progression-free survival of patients with intrahepatic cholangiocarcinoma after surgical resection.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: 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. Methods: 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. Results: 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( Conclusion: 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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