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A nomogram‑based diagnostic prediction model for differentiating mucinous cystic neoplasms from simple hepatic cysts in patients with hepatic cystic lesions

   
@article{JGO119526,
	author = {Diao Kong and Yueqing Xu and Wei Peng},
	title = {A nomogram‑based diagnostic prediction model for differentiating mucinous cystic neoplasms from simple hepatic cysts in patients with hepatic cystic lesions},
	journal = {Journal of Gastrointestinal Oncology},
	volume = {17},
	number = {3},
	year = {2026},
	keywords = {},
	abstract = {Background: Hepatic mucinous cystic neoplasm (H-MCN), a rare type of epithelial cystic tumor of the liver, may undergo malignant transformation but is often misdiagnosed as a simple hepatic cyst (SHC). There are no reliable methods that can preoperatively differentiate between these two conditions. This study thus aimed to develop and internally validate a diagnostic prediction model for distinguishing between SHCs and H-MCNs.Methods: Patients who were pathologically diagnosed with SHC or H‑MCN at West China Hospital of Sichuan University between January 2010 and January 2024 were included in a single‑center retrospective study. Patients were randomly divided into a training set (n=761; 701 with SHC and 60 with H‑MCN) and an internal validation set (n=326; 303 with SHC and 23 with H‑MCN) at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by multivariate logistic regression to develop the nomogram. Model performance was assessed via the area under the receiver operating characteristic (ROC) curve, calibration plots (with 1,000 bootstrap resamples), and decision curve analysis (DCA).Results: A total of 1,004 patients with SHC and 83 with H-MCN were included. Multivariate logistic regression analysis identified eight independent risk factors for H-MCN: sex (P=0.02), presence of symptoms (P},
	issn = {2219-679X},	url = {https://jgo.amegroups.org/article/view/119526}
}