Predicting High-risk Capsular Features in Pleomorphic Adenoma of the Parotid Gland Through a Nomogram Model Based on ADC Imaging.

Autor:	Mai W; Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou 510630, PR China., Tan J; Department of Radiology, YueBei People's hospital, Shaoguan 512026, PR China., Zhang L; Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou 510630, PR China., Wang L; Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou 510630, PR China., Zhang D; Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou 510630, PR China., Shi C; Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou 510630, PR China., Liu X; The First Affiliated Hospital of Jinan University, Clinical Research Platform for Interdiscipline of Stomatology, School of Stomatology, Jinan University, Guangzhou 510630, PR China. Electronic address: liuxiangning2003@126.com.
Jazyk:	angličtina
Zdroj:	Academic radiology [Acad Radiol] 2024 Nov; Vol. 31 (11), pp. 4408-4418. Date of Electronic Publication: 2024 Jun 21.
DOI:	10.1016/j.acra.2024.06.003
Abstrakt:	Rationale and Objectives: Based on Apparent Diffusion Coefficient (ADC) images, a nomogram model is established to accurately predict the high-risk capsular characteristics associated with pleomorphic adenoma of the parotid gland (PAP) recurrence. Materials and Methods: This retrospective study analyzed 190 patients with PAPs. Significant clinical radiological factors were identified through univariate difference analysis and multivariate regression analysis. The optimal threshold was determined by analyzing the average ADC value of the entire tumor, using the best Youden index and sensitivity analysis, and tumor subregions were delineated accordingly. Three radiomic models were constructed for the whole tumor and for high/low ADC areas, with the best model determined through statistical analysis. Ultimately, a nomogram model was constructed by combining the independent predictive factor of high-risk capsular features with the optimal radiomic predictive score. Model performance was comprehensively assessed by the area under the receiver operating characteristic curve (ROC AUC), accuracy, sensitivity, and specificity. Results: The best ADC division threshold as 1.25 × 10 -3 mm 2 /s. Multivariate analysis identified High-ADC Zone Volume Percentage as an independent predictor for PAPs with high-risk capsular characteristics. The radiomic model based on the low ADC tumor subregion was optimal (AUC 0.899). The nomogram model, combining independent predictors and optimal imaging studies predictive score, demonstrated high performance (AUC 0.909). Decision curve analysis confirmed the nomogram's clinical applicability. Conclusion: The nomogram model constructed from ADC quantitative imaging can predict PAPs patients with high-risk capsular features. These patients require intraoperative preventive measures to avoid tumor spillage and residuals, as well as extended postoperative follow-up. Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Wenfeng Mai reports financial support was provided by Jinan University First Affiliated Hospital. Wenfeng Mai reports a relationship with Corresponding author that includes: board membership. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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