AI Article Synopsis

  • The study focused on differentiating hepatic perivascular epithelioid cell tumors (PEComa) from hepatocellular carcinoma (HCC) using imaging data from Gd-EOB-DTPA-enhanced MRI in patients without cirrhosis.
  • A multivariate logistic regression model identified two key predictors: an early draining vein and T1D value of tumors, achieving high accuracy with a ROC curve AUC of 0.91.
  • The developed nomogram, validated through resampling, showed strong predictive ability and clinical usefulness for distinguishing between PEComa and HCC, which may assist healthcare practitioners in making informed decisions.

Article Abstract

Aim: Hepatic perivascular epithelioid cell tumors (PEComa) often mimic hepatocellular carcinoma (HCC) in patients without cirrhosis. This study aimed to develop a nomogram using imaging characteristics on Gd-EOB-DTPA-enhanced MRI and to distinguish PEComa from HCC in a noncirrhotic liver.

Methods: Forty patients with non-cirrhotic Gd-EOB-DTPA-enhanced magnetic resonance imaging(MRI) were included in our study. A multivariate logistic regression model was used to select significant variables to distinguish PEComa from HCC. A nomogram was developed based on the regression model. The performance of the nomogram was assessed with respect to the ROC curve and calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical usefulness of the nomogram.

Results: Two significant predictors were identified: the appearance of an early draining vein and the T1D value of tumors. The ROC curve showed that the area under the curve (AUC) of the model to predict the risk of PEComa was 0.91 (95% CI: 0.80~1) and showed that the model had high specificity (92.3%) and sensitivity (88.9%). The nomogram incorporating these two predictors showed favorable calibration, which was validated using 1000 resampling procedures, and the corrected C-index of this model was 0.90. Furthermore, DCA analysis showed that the model had clinical practicability.

Conclusion: In conclusion, the nomogram model showed favorable predictive accuracy for distinguishing PEComa from HCC in non-cirrhotic patients and may aid in clinical decision-making.

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Source
http://dx.doi.org/10.2174/0115734056269369231213102554DOI Listing

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