CT-Based Radiomics Analysis for Noninvasive Prediction of Perineural Invasion of Perihilar Cholangiocarcinoma.

Autor:	Zhan PC; Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.; Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China., Lyu PJ; Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China., Li Z; Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China., Liu X; Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China., Wang HX; Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China., Liu NN; Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.; Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China., Zhang Y; Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China., Huang W; Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.; Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China., Chen Y; Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China., Gao JB; Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.; Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China.
Jazyk:	angličtina
Zdroj:	Frontiers in oncology [Front Oncol] 2022 Jun 20; Vol. 12, pp. 900478. Date of Electronic Publication: 2022 Jun 20 (Print Publication: 2022).
DOI:	10.3389/fonc.2022.900478
Abstrakt:	Purpose: The study aimed to construct and evaluate a CT-Based radiomics model for noninvasive detecting perineural invasion (PNI) of perihilar cholangiocarcinoma (pCCA) preoperatively. Materials and Methods: From February 2012 to October 2021, a total of 161 patients with pCCA who underwent resection were retrospectively enrolled in this study. Patients were allocated into the training cohort and the validation cohort according to the diagnostic time. Venous phase images of contrast-enhanced CT were used for radiomics analysis. The intraclass correlation efficient (ICC), the correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics features and built radiomics signature. Logistic regression analyses were performed to establish a clinical model, a radiomics model, and a combined model. The performance of the predictive models was measured by area under the receiver operating characteristic curve (AUC), and pairwise ROC comparisons between models were tested using the Delong method. Finally, the model with the best performance was presented as a nomogram, and its calibration and clinical usefulness were assessed. Results: Finally, 15 radiomics features were selected to build a radiomics signature, and three models were developed through logistic regression. In the training cohort, the combined model showed a higher predictive capability (AUC = 0.950) than the radiomics model and the clinical model (AUC: radiomics = 0.914, clinical = 0.756). However, in the validation cohort, the AUC of the radiomics model (AUC = 0.885) was significantly higher than the other two models (AUC: combined = 0.791, clinical = 0.567). After comprehensive consideration, the radiomics model was chosen to develop the nomogram. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had a good consistency and clinical utility. Conclusion: We developed a CT-based radiomics model with good performance to noninvasively predict PNI of pCCA preoperatively. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Zhan, Lyu, Li, Liu, Wang, Liu, Zhang, Huang, Chen and Gao.)
Databáze:	MEDLINE
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