基于 CT 的腮腺良恶性肿瘤术前瘤内和瘤周放射模型:一项双中心研究。

IF 3.6 3区 医学 Q2 ONCOLOGY
American journal of cancer research Pub Date : 2024-09-15 eCollection Date: 2024-01-01 DOI:10.62347/AXQW1100
Qian Shen, Cong Xiang, Kui Huang, Feng Xu, Fulin Zhao, Yongliang Han, Xiaojuan Liu, Yongmei Li
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引用次数: 0

摘要

目的研究基于三相计算机断层扫描(CT)的瘤内和瘤周放射组学区分恶性和良性腮腺肿瘤的能力:我们对374例腮腺肿瘤患者的数据进行了回顾性分析,所有患者均经组织病理学证实。中心1(2014年1月至2023年1月)的321名患者按7:3的比例随机分为训练集和内部测试集,而中心2(2020年1月至2022年6月)的53名患者构成外部测试集。对肿瘤及其周围区域(肿瘤周围 2 毫米和 5 毫米区域)的 CT 图像进行审查,并提取其放射学特征,以构建不同的放射学模型。此外,还利用多变量逻辑回归分析建立了一个临床-放射学综合模型。利用决策曲线分析(DCA)和接收者操作特征曲线(ROC)对模型的预测性能进行了评估:结果:在所评估的模型中,肿瘤+外部2模型显示出更优越的预测性能。该模型的曲线下面积(AUC)分别为:训练集 0.986,内部测试集 0.827,外部测试集 0.749。临床模型的独立预测因素包括症状、边界和淋巴结肿大。在三个队列中,临床-放射体组合模型的AUC分别为0.981、0.842和0.749,优于肿瘤模型和临床模型:结论:结合瘤内和瘤周放射学特征的基于CT的放射学模型能有效区分腮腺肿瘤的恶性和良性,而结合临床独立预测因子则能进一步提高预测准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preoperative CT-based intra- and peri-tumoral radiomic models for differentiating benign and malignant tumors of the parotid gland: a two-center study.

Objective: To investigate the ability of intra- and peritumoral radiomics based on three-phase computed tomography (CT) to distinguish between malignant and benign parotid tumors.

Methods: We conducted a retrospective analysis of data from 374 patients with parotid gland tumors, all confirmed by histopathology. A total of 321 patients from Center 1 (January 2014 to January 2023) were randomly divided into the training set and internal testing set at a ratio of 7:3, whereas 53 patients from Center 2 (January 2020 to June 2022) constituted the external testing set. CT images of both the tumor and surrounding areas (2 mm and 5 mm areas surrounding the tumor) were reviewed, and their radiomic features were extracted for the construction of different radiomic models. In addition, a combined clinical-radiomic model was developed using multivariate logistic regression analysis. The model's predictive performance was evaluated using decision curve analysis (DCA) and receiver operating characteristic (ROC) curves.

Results: Among the models evaluated, Tumor + External2 model demonstrated superior predictive performance. The areas under the curve (AUCs) of this model were 0.986 in the training set, 0.827 in the internal test set, and 0.749 in the external test set. For the clinical model, independent predictive factors included symptoms, boundaries, and lymph node swelling. The combined clinical-radiomic model achieved AUCs of 0.981, 0.842, and 0.749 in the three cohorts, outperforming both the Tumor model and the clinical model individually.

Conclusion: The CT-based radiomic models incorporating intratumoral and peritumoral radiomic features can effectively distinguish malignant from benign parotid tumors, and the predictive accuracy is further improved by incorporating clinically independent predictors.

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来源期刊
自引率
3.80%
发文量
263
期刊介绍: The American Journal of Cancer Research (AJCR) (ISSN 2156-6976), is an independent open access, online only journal to facilitate rapid dissemination of novel discoveries in basic science and treatment of cancer. It was founded by a group of scientists for cancer research and clinical academic oncologists from around the world, who are devoted to the promotion and advancement of our understanding of the cancer and its treatment. The scope of AJCR is intended to encompass that of multi-disciplinary researchers from any scientific discipline where the primary focus of the research is to increase and integrate knowledge about etiology and molecular mechanisms of carcinogenesis with the ultimate aim of advancing the cure and prevention of this increasingly devastating disease. To achieve these aims AJCR will publish review articles, original articles and new techniques in cancer research and therapy. It will also publish hypothesis, case reports and letter to the editor. Unlike most other open access online journals, AJCR will keep most of the traditional features of paper print that we are all familiar with, such as continuous volume, issue numbers, as well as continuous page numbers to retain our comfortable familiarity towards an academic journal.
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