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Purpose <p>The present study aimed to develop and validate a preoperative model based on Gd-DTPA-enhanced magnetic resonance imaging (MRI) and clinical factors for predicting perineural invasion (PNI) in patients with intrahepatic cholangiocarcinoma (ICC), enabling clinicians to perform more accurate patient evaluation and and make individualized therapeutic decisions.</p> Methods <p>Between July 2019 and February 2024, a total of 173 patients with pathologically confirmed ICC who underwent preoperative Gd‑DTPA‑enhanced MRI were retrospectively enrolled. These patients were randomly assigned to training and test cohorts at a 7:3 ratio. Multivariate logistic regression was used to identify independent predictors of PNI status and a predictive model was developed and presented as a nomogram. The model performance was assessed in terms of discrimination, calibration, and clinical utility.</p> Results <p>Peritumoral arterial hyper-enhancement (OR 14.99, 95% CI:1.95,115.22, <i>P</i> = 0.009), satellite nodules (OR 10.07, 95% CI: 1.34,75.48, <i>P</i> = 0.025), target sign on DWI (OR 0.03, 95% CI: 0.00,0.38, <i>P</i> = 0.007), and tumor location (OR 0.03, 95% CI: 0.00,0.28, <i>P</i> = 0.003) were independent risk factors to PNI status and constituted the model. The model exhibited strong discriminative ability, with an area under the curve (AUC) of 0.939 (95% CI: 0.891–0.988), sensitivity of 0.907, specificity of 0.870, and accuracy of 0.893 in the training set; corresponding values in the testing set were 0.866 (95% CI: 0.767–0.966), 0.750, 0.800, and 0.769. The decision curve analysis (DCA) curve showed that the model achieved great clinical benefits.</p> Conclusion <p>In conclusion, we developed and validated a noninvasive preoperative model that integrates Gd-DTPA-enhanced MRI features to accurately predict PNI in ICC patients. This model shows significant potential to assist clinicians in preoperative risk assessment, thus facilitating improved prognostic stratification and the formulation of personalized treatment strategies.</p>

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Noninvasive preoperative prediction of perineural invasion in intrahepatic cholangiocarcinoma based on dynamic contrast-enhanced MRI

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月4日

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  • Sisi Zhang,
  • Yayuan Feng,
  • Da He,
  • Yuxian Wu,
  • Ningyang Jia,
  • Xingpeng Pan,
  • Yiping Liu

摘要

Purpose

The present study aimed to develop and validate a preoperative model based on Gd-DTPA-enhanced magnetic resonance imaging (MRI) and clinical factors for predicting perineural invasion (PNI) in patients with intrahepatic cholangiocarcinoma (ICC), enabling clinicians to perform more accurate patient evaluation and and make individualized therapeutic decisions.

Methods

Between July 2019 and February 2024, a total of 173 patients with pathologically confirmed ICC who underwent preoperative Gd‑DTPA‑enhanced MRI were retrospectively enrolled. These patients were randomly assigned to training and test cohorts at a 7:3 ratio. Multivariate logistic regression was used to identify independent predictors of PNI status and a predictive model was developed and presented as a nomogram. The model performance was assessed in terms of discrimination, calibration, and clinical utility.

Results

Peritumoral arterial hyper-enhancement (OR 14.99, 95% CI:1.95,115.22, P = 0.009), satellite nodules (OR 10.07, 95% CI: 1.34,75.48, P = 0.025), target sign on DWI (OR 0.03, 95% CI: 0.00,0.38, P = 0.007), and tumor location (OR 0.03, 95% CI: 0.00,0.28, P = 0.003) were independent risk factors to PNI status and constituted the model. The model exhibited strong discriminative ability, with an area under the curve (AUC) of 0.939 (95% CI: 0.891–0.988), sensitivity of 0.907, specificity of 0.870, and accuracy of 0.893 in the training set; corresponding values in the testing set were 0.866 (95% CI: 0.767–0.966), 0.750, 0.800, and 0.769. The decision curve analysis (DCA) curve showed that the model achieved great clinical benefits.

Conclusion

In conclusion, we developed and validated a noninvasive preoperative model that integrates Gd-DTPA-enhanced MRI features to accurately predict PNI in ICC patients. This model shows significant potential to assist clinicians in preoperative risk assessment, thus facilitating improved prognostic stratification and the formulation of personalized treatment strategies.