BMC medical imaging · 2026
A nomogram for malignancy prediction of pancreatic cystic lesions based on trans-abdominal ultrasound features.
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Ma Liyuan, Hu Ya, Xia Yu, Ji Jiang, Gu Jionghui, Luo Nengwen, Pan Aonan, Cao Yang, An Yuang, Gao Luying, Jiang Yuxin
Аннотация
BACKGROUND: Pancreatic cystic lesions (PCLs) have variable malignant potential, and distinguishing benign from malignant/premalignant cysts is challenging for optimal management. Trans-abdominal ultrasound (TAUS), a widely available, non-invasive, low-cost first-line pancreatic imaging tool, is underexplored for predicting PCL malignant potential. This study aimed to develop a TAUS feature-based nomogram for this purpose.
METHODS: This retrospective study included 161 patients with pathology-confirmed PCLs from December 2012 to July 2021, divided into benign (59 cases) and non-benign (premalignant/malignant, 102 cases) groups. Relevant clinical characteristics and TAUS features were collected. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis was used to optimize feature selection. Multivariate logistic regression analysis was applied to construct the nomogram. The performance of the nomogram was assessed via receiver operating characteristic curves, calibration curves and decision curve analysis (DCA).
RESULTS: Among the 26 features collected, 11 features were chosen via LASSO analysis. Multivariate analysis identified echogenicity, the configuration of cysts, solid content and septation/wall thickening as independent predictors. The prediction nomogram model developed with these four variables showed moderate discriminative performance in differentiating non-benign from benign PCLs, with an area under the curve (AUC) of 0.781. Regarding internal verification, tenfold cross-validation yielded a C-index of 0.749. The Hosmer-Lemeshow test yielded a P = 0.945, suggesting that the model had a good fit. Additionally, DCA demonstrated good net clinical benefit.
CONCLUSIONS: This study explored the value of TAUS features for predicting the malignant potential of PCLs. The incorporation of TAUS features into a nomogram may offer a potential non-invasive tool to help clinicians in risk stratification during the initial evaluation and follow-up assessment of PCLs patients.
METHODS: This retrospective study included 161 patients with pathology-confirmed PCLs from December 2012 to July 2021, divided into benign (59 cases) and non-benign (premalignant/malignant, 102 cases) groups. Relevant clinical characteristics and TAUS features were collected. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis was used to optimize feature selection. Multivariate logistic regression analysis was applied to construct the nomogram. The performance of the nomogram was assessed via receiver operating characteristic curves, calibration curves and decision curve analysis (DCA).
RESULTS: Among the 26 features collected, 11 features were chosen via LASSO analysis. Multivariate analysis identified echogenicity, the configuration of cysts, solid content and septation/wall thickening as independent predictors. The prediction nomogram model developed with these four variables showed moderate discriminative performance in differentiating non-benign from benign PCLs, with an area under the curve (AUC) of 0.781. Regarding internal verification, tenfold cross-validation yielded a C-index of 0.749. The Hosmer-Lemeshow test yielded a P = 0.945, suggesting that the model had a good fit. Additionally, DCA demonstrated good net clinical benefit.
CONCLUSIONS: This study explored the value of TAUS features for predicting the malignant potential of PCLs. The incorporation of TAUS features into a nomogram may offer a potential non-invasive tool to help clinicians in risk stratification during the initial evaluation and follow-up assessment of PCLs patients.