ArticleBMC cancer2024
Development and validation of a novel preoperative clinical model for predicting lymph node metastasis in perihilar cholangiocarcinoma.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Interpretable multimodal deep learning improves postoperative risk stratification in intrahepatic cholangiocarcinoma in multicentre cohorts.NPJ digital medicine · 2025Article
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- Multimodal predictive model for strangulation risk in adhesive small bowel obstruction using deep learning and electronic health record data.The Journal of international medical research · 2025Observational
- Preoperative factors predicting outcomes in patients with suspected perihilar cholangiocarcinoma referred for curative resection- a single-center 10-year experience.Langenbeck's archives of surgery · 2024Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroudWe aimed to develop a novel preoperative nomogram to predict lymph node metastasis (LNM) in perihilar cholangiocarcinoma (pCCA) patients.
methods160 pCCA patients were enrolled at Lihuili Hospital from July 2006 to May 2022. A novel nomogram model was established to predict LNM in pCCA patients based on the independent predictive factors selected by the multivariate logistic regression model. The precision of the nomogram model was evaluated through internal and external validation with calibration curve statistics and the concordance index (C-index). Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate and determine the clinical utility of the nomogram.
resultsMultivariate logistic regression demonstrated that age (OR = 0.963, 95% CI: 0.930-0.996, P = 0.030), CA19-9 level (> 559.8 U/mL vs. ≤559.8 U/mL: OR = 3.162, 95% CI: 1.519-6.582, P = 0.002) and tumour diameter (OR = 1.388, 95% CI: 1.083-1.778, P = 0.010) were independent predictive factors of LNM in pCCA patients. The C-index was 0.763 (95% CI: 0.667-0.860) and 0.677 (95% CI: 0.580-0.773) in training cohort and validation cohort, respectively. ROC curve analysis indicated the comparative stability and adequate discriminative ability of nomogram. The sensitivity and specificity were 0.820 and 0.652 in training cohort and 0.704 and 0.649 in validation cohort, respectively. DCA revealed that the nomogram model could augment net benefits in the prediction of LNM in pCCA patients.
conclusionsThe novel prediction model is useful for predicting LNM in pCCA patients and showed adequate discriminative ability and high predictive accuracy.
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