The Effect of Magnetic Resonance Imaging Based Radiomics Models in Discriminating stage I-II and III-IVa Nasopharyngeal Carcinoma

作者全名："Li, Quanjiang; Yu, Qiang; Gong, Beibei; Ning, Youquan; Chen, Xinwei; Gu, Jinming; Lv, Fajin; Peng, Juan; Luo, Tianyou"

作者地址："[Li, Quanjiang; Yu, Qiang; Gong, Beibei; Ning, Youquan; Chen, Xinwei; Gu, Jinming; Lv, Fajin; Peng, Juan; Luo, Tianyou] Chongqing Med Univ, Dept Radiol, Affiliated Hosp 1, Chongqing 400016, Peoples R China"

通信作者："Peng, J; Luo, TY (通讯作者)，Chongqing Med Univ, Dept Radiol, Affiliated Hosp 1, Chongqing 400016, Peoples R China."

来源：DIAGNOSTICS

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000914492700001

JCR分区：Q1

影响因子：3

年份：2023

卷号：13

期号：2

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结束页：　

文献类型：Article

关键词：nasopharyngeal carcinoma; cancer staging; magnetic resonance imaging

摘要："Background: Nasopharyngeal carcinoma (NPC) is a common tumor in China. Accurate stages of NPC are crucial for treatment. We therefore aim to develop radiomics models for discriminating early-stage (I-II) and advanced-stage (III-IVa) NPC based on MR images. Methods: 329 NPC patients were enrolled and randomly divided into a training cohort (n = 229) and a validation cohort (n = 100). Features were extracted based on axial contrast-enhanced T1-weighted images (CE-T1WI), T1WI, and T2-weighted images (T2WI). Least absolute shrinkage and selection operator (LASSO) was used to build radiomics signatures. Seven radiomics models were constructed with logistic regression. The AUC value was used to assess classification performance. The DeLong test was used to compare the AUCs of different radiomics models and visual assessment. Results: Models A, B, C, D, E, F, and G were constructed with 13, 9, 7, 9, 10, 7, and 6 features, respectively. All radiomics models showed better classification performance than that of visual assessment. Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance (AUC: 0.847) in the training cohort. CE-T1WI showed the greatest significance for staging NPC. Conclusion: Radiomics models can effectively distinguish early-stage from advanced-stage NPC patients, and Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance."

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