Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports.

Autor:	Zhu YL; Department of Nasopharyngeal Head and Neck Tumor Radiotherapy, Zhongshan City People's Hospital, Zhongshan 528400, Guangdong Province, China., Deng XL; School of Public Health, Sun Yat-sen University, Guangzhou 510060, Guangdong Province, China., Zhang XC; School of Public Health, Sun Yat-sen University, Guangzhou 510060, Guangdong Province, China., Tian L; Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, Guangdong Province, China., Cui CY; Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, Guangdong Province, China., Lei F; Department of Nasopharyngeal Head and Neck Tumor Radiotherapy, Zhongshan City People's Hospital, Zhongshan 528400, Guangdong Province, China., Xu GQ; Department of Nasopharyngeal Head and Neck Tumor Radiotherapy, Zhongshan City People's Hospital, Zhongshan 528400, Guangdong Province, China., Li HJ; Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, Guangdong Province, China., Liu LZ; Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, Guangdong Province, China., Ma HL; Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, Guangdong Province, China. mahual@sysucc.org.cn.
Jazyk:	angličtina
Zdroj:	World journal of radiology [World J Radiol] 2024 Jun 28; Vol. 16 (6), pp. 203-210.
DOI:	10.4329/wjr.v16.i6.203
Abstrakt:	Background: Development of distant metastasis (DM) is a major concern during treatment of nasopharyngeal carcinoma (NPC). However, studies have demonstrated improved distant control and survival in patients with advanced NPC with the addition of chemotherapy to concomitant chemoradiotherapy. Therefore, precise prediction of metastasis in patients with NPC is crucial. Aim: To develop a predictive model for metastasis in NPC using detailed magnetic resonance imaging (MRI) reports. Methods: This retrospective study included 792 patients with non-distant metastatic NPC. A total of 469 imaging variables were obtained from detailed MRI reports. Data were stratified and randomly split into training (50%) and testing sets. Gradient boosting tree (GBT) models were built and used to select variables for predicting DM. A full model comprising all variables and a reduced model with the top-five variables were built. Model performance was assessed by area under the curve (AUC). Results: Among the 792 patients, 94 developed DM during follow-up. The number of metastatic cervical nodes (30.9%), tumor invasion in the posterior half of the nasal cavity (9.7%), two sides of the pharyngeal recess (6.2%), tubal torus (3.3%), and single side of the parapharyngeal space (2.7%) were the top-five contributors for predicting DM, based on their relative importance in GBT models. The testing AUC of the full model was 0.75 (95% confidence interval [CI]: 0.69-0.82). The testing AUC of the reduced model was 0.75 (95%CI: 0.68-0.82). For the whole dataset, the full (AUC = 0.76, 95%CI: 0.72-0.82) and reduced models (AUC = 0.76, 95%CI: 0.71-0.81) outperformed the tumor node-staging system (AUC = 0.67, 95%CI: 0.61-0.73). Conclusion: The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC. The number of metastatic cervical nodes was identified as the principal contributing variable. Competing Interests: Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article. (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)
Databáze:	MEDLINE
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