©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports
Yu-Liang Zhu, Xin-Lei Deng, Xu-Cheng Zhang, Li Tian, Chun-Yan Cui, Feng Lei, Gui-Qiong Xu, Hao-Jiang Li, Li-Zhi Liu, Hua-Li Ma
Yu-Liang Zhu, Feng Lei, Gui-Qiong Xu, Department of Nasopharyngeal Head and Neck Tumor Radiotherapy, Zhongshan City People's Hospital, Zhongshan 528400, Guangdong Province, China
Xin-Lei Deng, Xu-Cheng Zhang, School of Public Health, Sun Yat-sen University, Guangzhou 510060, Guangdong Province, China
Li Tian, Chun-Yan Cui, Hao-Jiang Li, Li-Zhi Liu, Hua-Li Ma, 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
Co-first authors: Yu-Liang Zhu and Xin-Lei Deng.
Co-corresponding authors: Li-Zhi Liu and Hua-Li Ma.
Author contributions: Li HJ, Ma HL, and Liu LZ conceptualized and designed the research; Zhang XC, Tian L, Cui CY, and Liu LZ screened the patients and acquired the imaging and clinical data; Zhu YL, Deng XL, Lei F, Xu GQ, and Ma HL performed data analysis; Zhu YL, Deng XL, and Ma HL wrote the paper; all the authors have read and approved the final manuscript. All authors were involved in review and editing of the manuscript and had final approval of the manuscript. Zhu YL and Deng XL performed data analysis and prepared the first draft of the manuscript. Both authors have made crucial and indispensable contributions towards the completion of the project and thus qualified as the co-first authors of the paper. Both Ma HL and Liu LZ have played important and indispensable roles in the study design, data interpretation, and manuscript preparation as the co-corresponding authors. Ma HL was responsible for data re-analysis, and preparation and submission of the current version of the manuscript. Liu LZ conceptualized, designed, and supervised the whole process of the project. He searched the literature, and revised and submitted the early version of the manuscript. This collaboration between Ma HL and Liu LZ is crucial for the publication of this manuscript.
Institutional review board statement: This study was approved by the Institutional Ethics Committee of the Sun Yat-sen University Cancer Center (No. SL-G2022-004-03).
Informed consent statement: The need for patient consent was waived due to the retrospective nature of the study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at
mahual@sysucc.org.cn. Participants gave informed consent for data sharing.
Corresponding author: Hua-Li Ma, MD, Doctor, 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, No. 651 Dongfeng Road East, Guangzhou 510060, Guangdong Province, China.
mahual@sysucc.org.cn
Received: March 9, 2024
Revised: May 13, 2024
Accepted: May 28, 2024
Published online: June 28, 2024
Processing time: 108 Days and 20.5 Hours
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.
Core Tip: A total of 469 imaging variables obtained from detailed magnetic resonance imaging (MRI) reports of 792 patients with nasopharyngeal carcinoma (NPC) with non-distant metastasis were evaluated in this retrospective study. Data were stratified and randomly split into training (50%) and testing (50%) sets. Gradient boosting tree (GBT) models were built based on the training set and used to select imaging variables to predict distant metastasis. The number of metastatic cervical nodes was the top contributor for predicting distant metastasis based on the relative importance in GBT models. The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC.
