Retrospective Study Open Access
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Jun 28, 2024; 16(6): 203-210
Published online Jun 28, 2024. doi: 10.4329/wjr.v16.i6.203
Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports
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
ORCID number: Li-Zhi Liu (0000-0002-3977-0518); Hua-Li Ma (0000-0003-1369-2897).
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.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
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

Abstract
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.

Key Words: Nasopharyngeal carcinoma, Distant metastasis, Machine learning, Detailed magnetic resonance imaging report, Gradient boosting tree model

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.



INTRODUCTION

Nasopharyngeal carcinoma (NPC) is a head and neck malignancy that commonly affects the people of Southeast China[1]. Distant metastasis (DM) is a major cause of treatment failure in patients with NPC[2-4]. Recently, studies have demonstrated that the addition of chemotherapy to concomitant chemoradiotherapy can improve distant control and survival in patients with advanced NPC[5-8]. Hence, the precise prediction of metastasis in patients with NPC is of immense clinical value. Patients with lower chances of metastasis can opt for a personalized treatment plan aimed at reducing side-effects, pursuing better health, and lowering medical expenses. Conversely, patients who are more likely to develop metastasis can opt for an individualized regimen with greater focus on chemotherapy.

The American Joint Committee on Cancer tumor node (TN) staging system is the most widely used method to assess the risk of DM in patients with NPC. Magnetic resonance imaging (MRI)-based tumor and node characteristics are used to define the T and N classifications of NPC. However, the factors adopted by the TN-staging system encompass only a part of the MRI data, thus, some useful image data may be neglected. This may be the reason for the moderate predictive ability of TN-staging for DM in patients with NPC[9]. Detailed MRI reports can offer more substantial information about tumor invasion and lymph node metastasis than the TN-staging system and may be able to predict disease prognosis more accurately. However, they may also provide confounding information which may lead to low efficacy and pose difficulties if used in predictive models clinically. Consequently, statistical tools that can analyze large amounts of complex data with good predictive performance are required to address this issue.

The most extensively used method for clinical prediction is the generalized linear model[10]. However, when multiple highly correlated or non-normally distributed factors are included in these models, they fail to meet the assumptions of linearity and additivity, leading to unfavorable results. In contrast, machine learning methods incorporate larger numbers of predictors and can address the collinearity problem[11,12]. Thus, predictive models generated by machine learning methods might reduce the number of confounding variables in detailed MRI reports, increasing the efficacy of metastasis prediction. Among the several machine learning methods, the gradient boosting tree (GBT) model is particularly attractive[11]. It does not require factors to be normally distributed, can handle multiple variables simultaneously in a single model, and deals with complex collinearity and outliers flexibly. Moreover, this model can identify the chief contributors based on the relative importance of variables[13,14].

Therefore, this study aimed to build and validate a well-performed predictive model based on detailed MRI reports using the GBT model to examine the most contributing variables for NPC metastasis.

MATERIALS AND METHODS
Datasets

Between January 2010 and January 2013, a total of 3814 patients with NPC were hospitalized at the Sun Yat-sen University Cancer Center (Guangzhou, China), 792 of whom were included in this study. All patients had pathologically confirmed NPC and received intensity-modulated radiation therapy treatment. Patients with incomplete clinical data (n = 2973) or MRI data (n = 5), and those with DM at diagnosis (n = 24) or presence of other tumors (n = 20) were excluded from the study.

This study was approved by the Ethics Committee of the Sun Yat-sen University Cancer Center (No. SL-G2022-004-03).

MRI imaging protocol

All patients underwent pretreatment MRI with a 1.5-T or 3.0-T system. The scan ranged from the suprasellar cistern to the superior border of the thoracic cage. The detailed MRI sequences are shown in Supplementary material 1.

Imaging variables

All MRI images were independently assessed by three radiologists with 18 (Liu LZ), 12 (Cui CY), and 10 (Tian L) years of work experience. Any disagreements were resolved by consensus within 3 d. A detailed MRI report including large amounts of information regarding tumor invasion and lymph node metastasis for each patient was recorded, as shown in Supplementary material 2. A total of 469 imaging variables extracted from the detailed MRI reports were used to describe the size of the lesion and its relationship with the surrounding tissues, including tumors and lymph nodes.

Statistical analysis

We utilized several techniques for data preprocessing. The continuous variables were standardized and categorical variables were one-hot encoded. In total, 469 imaging variables were one-hot encoded and normally rescaled. We used a complete dataset without any missing records. Additionally, we adopted a well-established algorithm, the Synthetic Minority Over-sampling Technique (SMOTE), to address the data imbalance issue, as it can over-sample the minority and under-sample the majority. For variable selection, we used the method proposed by Deng et al[15]. Specifically, we stratified the dataset according to the metastasis outcome variable and randomly split the dataset into a training set (50%) and a testing set (50%). The training set was balanced with SMOTE and used for variable selection. Additionally, we generated two types of random variables, binary and continuous, which were included in the variable selection model. We then selected the variables with importance scores greater than those of random variables (as the threshold of random effect). The whole variable selection process was repeated 20 times, using different random seeds, to ensure the robustness of the analysis. In this case, the selected variables had greater average impacts than the average random effects (considered as statistically significant). Thereafter, the GBT model (full model) was rebuilt with the training set using all the selected variables and a reduced GBT model was constructed using the top-five selected variables. A five-fold cross validation together with a grid search was adopted to address the over-fitting problem and tune the models’ parameters, including the number of trees, maximum depth of trees, and learning rate. Model performance was assessed using area under the curve (AUC). The 95% confidence interval (CI) of AUC was assessed using the bootstrap method. The R software (version 3.0.2; R Foundation for Statistical Computing, Vienna, Austria) was used for analyses.

RESULTS

The demographic and clinicopathological characteristics of the 792 patients are listed in Table 1. The median patient age was 45 years (interquartile range, 38-53 years) and 576 (72.7%) patients were male, and 216 (27.3%) were female. Most of the patients had World Health Organization type III histological type (94.2%) of NPC. The tumor stage distribution for the entire series was as follows: 9.2% stage I, 22.1% stage II, 38.3% stage III, and 30.4% stage IV. The majority of the patients (86.5%) received neoadjuvant, concomitant, or adjuvant platinum-based chemotherapy.

Table 1 Demographic and clinicopathological characteristics of patients, n (%).
Variable
No. of patients
Median age (years)45 (38-53)
Sex
    Male576 (72.7)
    Female216 (27.3)
WHO histologic type
    I5 (0.6)
    II41 (5.2)
    III746 (94.2)
T classification
    T1204 (25.8)
    T297 (12.2)
    T3296 (37.4)
    T4195 (24.6)
N classification
    N0182 (23.0)
    N1438 (55.3)
    N2113 (14.3)
    N359 (7.4)
AJCC stage
    I73 (9.2)
    II175 (22.1)
    III303 (38.3)
    IV241 (30.4)
Chemotherapy
    No107 (13.5)
    Yes685 (86.5)

After the end of intensity-modulated radiation therapy, follow-up was conducted every 3 mo in the first 2 years and every 6 mo for the next 5 years, or until death. The median follow-up was 62.1 mo (range, 1.4 to 83.4 mo). Among the 792 patients, 78 developed local and/or regional recurrence, 94 developed DM, and 87 died during the follow-up period. The 5-year overall survival and DM-free survival rates were 88.3% and 87.7%, respectively. The median overall survival and DM-free survival were not reached during follow-up.

Figure 1 shows the relative importance scores of the top-ten selected variables for predicting DM from the full predictive model. The number of metastatic cervical nodes contributed the maximum in predicting the occurrence of DM, accounting for 30.9% of the relative importance scores on the basis of GBT models. The next top-nine contributors were as follows: Tumor invasion in the posterior half of the nasal cavity (9.7%), two sides of the pharyngeal recess (6.2%), tubal torus (3.3%), single side of the parapharyngeal space (2.7%), carotid sheath (2.5%), chondroseptum (1.3%), prestyloid space (1.0%), posterior pharynx (0.7%), and cavernous sinus (0.5%).

Figure 1
Figure 1 Relative importance of top-ten variables based on the predictive full model. The full model included 23 variables. Only the top-ten variables with greater relative importance score are showed.

Figure 2A illustrates the performance of the full model with 23 selected variables and the reduced model with the top-five selected variables. The training AUC for the full model was 0.79 (95%CI: 0.72-0.86), which was similar to that of the reduced model (AUC = 0.78, 95%CI: 0.71-0.84). In addition, the testing AUC of the full model (AUC = 0.75, 95%CI: 0.69-0.82) was slightly lower than the training AUC but also very similar to that of the reduced model (AUC = 0.75, 95%CI: 0.68-0.82).

Figure 2
Figure 2 Performance comparison of the gradient boosting tree models and the tumor node-staging system. A: Performance assessment of the full model and reduced model. The full model included 23 selected variables. The reduced model only included the top-five variables. Area under the curve (AUC) is showed with 95% confidence interval (CI); B: Comparison of metastasis predictive performance among different predictive strategies in the whole dataset. Predictive strategies included using T-staging scores, N-stating scores, TN-staging scores, reduced model, and full model. The reduced model only included the top-five variables, while the full model included 23 selected variables. AUC is showed with 95%CI. AUC: Area under the curve.

For the whole dataset, a comparison of the DM predictive performance among the different predictive strategies is presented in Figure 2B. TN-staging scores (AUC = 0.67, 95%CI: 0.61-0.73) provided better prediction than using only T-staging scores (AUC = 0.63, 95%CI: 0.57-0.69) or N-staging scores (AUC = 0.64, 95%CI: 0.58-0.70). However, both the full model (AUC = 0.76, 95%CI: 0.72-0.82) and the reduced model (AUC = 0.76, 95%CI: 0.71-0.81) outperformed the TN-staging scores. Furthermore, the performance of the reduced model was nearly identical to that of the full model. Based on the receiver operating characteristic curve and Youden index, the sensitivity for the full model was 0.744 and specificity was 0.631. The sensitivity for the reduced model was 0.739 and specificity was 0.636.

DISCUSSION

In this study, we successfully built a predictive model, based on detailed MRI reports using GBT models, which outperformed the traditional TN-staging system in predicting DM in patients with NPC. The number of metastatic cervical nodes was identified as the most contributing factor in DM prediction.

MRI is routinely used for the detection, diagnosis, and assessment of the disease process in NPC. TN-staging of patients with NPC before treatment, which is the primary basis for clinical treatment planning, is usually based on MRI examination. However, regardless of the T or N stage, only a few MRI factors are selected for use in the staging system. According to previous studies, several other MRI factors may also be important for the prognosis of patients with NPC[16-19]. For example, extranodal extension[17] and the number of positive lymph nodes on MRI[18] were found to be independent factors for overall survival, DM-free survival, progression-free survival, and local recurrence-free survival in patients with NPC. This implies that some useful MRI data may be neglected by the TN-staging system. Conversely, in our study, detailed MRI reports with a total of 469 imaging variables offered extensive information about tumor invasion and lymph node metastasis. This is one of the reasons for the more efficient predictive performance of our model compared with that of the TN-staging system. In addition, the predictive performance of our model was greater than that of a previously reported model which used MR image features extracted by a three-dimensional convolutional neural network, both using GBT models (concordance indexes were 0.640 to 0.711 in different validation cohorts)[20].

The application of GBT models is another reason for the good performance of our model. Compared to the traditional statistical models, GBT models can efficiently deal with complex and non-linear correlations while maintaining a relatively high prediction accuracy[14]. The predictive performance of our model was comparable to that of another machine learning model using MRI-based tumor burden features and all clinical factors (concordance indexes were 0.766 and 0.760 in internal validation and external validation sets, respectively)[21]. Despite there being differences in demographics, study populations, and exposures between our study and the aforementioned study, it is evident that machine learning methods reduce the noise of detailed MRI reports and greatly increase the efficacy of prediction. Our model prefers sensitivity over specificity because of the severity of DM among patients with NPC. In another word, our model has a better ability to find a patient with potential DM, which conveys very important information for future treatment plans. The overfitting issue in machine learning models is well-known. To address this issue, we applied multiple approaches including splitting the datasets into training dataset (50%) and testing dataset (50%) and using grid search with 5-fold cross-validation for finding the best hyperparameters. From our results, we can conclude that our model did not have overfitting issues. Our reduced model with top-five variables performed as efficiently as the full model, which included all selected variables, enhancing the credibility of our results. Overall, our study provides a reference for predicting DM in patients with NPC.

In this study, we found that the number of metastatic cervical nodes played a substantial role in predicting DM in patients with NPC, with a much higher relative importance score than other variables in the GBT models. This finding is consistent with that of our previous study[18], wherein using a Cox model, we identified positive lymph node number as an independent risk factor for DM-free survival in patients with NPC, which was superior to other nodal factors. Thus, the present study confirmed the prognostic importance of metastatic nodal number in patients with NPC. The importance of metastatic nodal number may be explained as follows: In patients with NPC, LN metastases often follow an orderly manner from upper to lower level LNs[22]. This implies that the number of metastatic LNs is adequate to simultaneously reflect the location and laterality of positive LNs, and can better reflect the metastatic lymph node burden in patients. Currently, counting the number of metastatic lymph nodes on MRI can be a laborious task when a patient has many lymph nodes. However, the application of artificial intelligence in medical imaging will make counting of lymph nodes relatively easy. Moreover, we hope that the importance of metastatic nodal number and its potential clinical value, such as usage in selecting patients who may benefit from the additional chemotherapy, attract the attention of clinicians for further study.

Nonetheless, our study had some drawbacks. First, this was a retrospective study. Second, we did not control for some unknown confounding factors; however, we included 469 imaging variables, and some of these variables may be related to uncontrolled factors, which might have addressed this problem to some extent. Moreover, although our data were collected from only one hospital, we randomly split the dataset into training and testing sets. With the testing set as our external validation, our results can be applied to other populations to some extent. Third, we did not compare other machine-learning methods in this study. Instead, we used cross-validation and grid search to tune the parameters of the models, and the performance of our models was good. Further studies comparing other methods to this type of model are warranted in the future. Finally, we did not include any clinical data (e.g., age, sex, and EBV-DNA) in the model because we focused on the imaging variables obtained from the detailed MRI reports. Models combining imaging data and clinical data should be further studied in future, especially in large sample, multi-center, and prospective studies.

CONCLUSION

We have successfully built a predictive model for predicting DM in patients with NPC, which outperformed the traditional TN-staging system. The number of metastatic cervical nodes was found to be the most contributing factor.

Footnotes

Provenance and peer review: Invited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Radiology, nuclear medicine and medical imaging

Country of origin: China

Peer-review report’s classification

Scientific Quality: Grade C, Grade C

Novelty: Grade B, Grade C

Creativity or Innovation: Grade B, Grade C

Scientific Significance: Grade B, Grade B

P-Reviewer: Jennane R, France; Yin J, China S-Editor: Li L L-Editor: Wang TQ P-Editor: Che XX

References
1.  Wei KR, Zheng RS, Zhang SW, Liang ZH, Li ZM, Chen WQ. Nasopharyngeal carcinoma incidence and mortality in China, 2013. Chin J Cancer. 2017;36:90.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 164]  [Cited by in F6Publishing: 210]  [Article Influence: 30.0]  [Reference Citation Analysis (0)]
2.  Lee AW, Poon YF, Foo W, Law SC, Cheung FK, Chan DK, Tung SY, Thaw M, Ho JH. Retrospective analysis of 5037 patients with nasopharyngeal carcinoma treated during 1976-1985: overall survival and patterns of failure. Int J Radiat Oncol Biol Phys. 1992;23:261-270.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 498]  [Cited by in F6Publishing: 482]  [Article Influence: 15.1]  [Reference Citation Analysis (0)]
3.  Sun XS, Liu SL, Luo MJ, Li XY, Chen QY, Guo SS, Wen YF, Liu LT, Xie HJ, Tang QN, Liang YJ, Yan JJ, Lin DF, Bi MM, Liu Y, Liang YF, Ma J, Tang LQ, Mai HQ. The Association Between the Development of Radiation Therapy, Image Technology, and Chemotherapy, and the Survival of Patients With Nasopharyngeal Carcinoma: A Cohort Study From 1990 to 2012. Int J Radiat Oncol Biol Phys. 2019;105:581-590.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 46]  [Cited by in F6Publishing: 71]  [Article Influence: 14.2]  [Reference Citation Analysis (0)]
4.  Mao YP, Tang LL, Chen L, Sun Y, Qi ZY, Zhou GQ, Liu LZ, Li L, Lin AH, Ma J. Prognostic factors and failure patterns in non-metastatic nasopharyngeal carcinoma after intensity-modulated radiotherapy. Chin J Cancer. 2016;35:103.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 69]  [Cited by in F6Publishing: 112]  [Article Influence: 14.0]  [Reference Citation Analysis (0)]
5.  Hui EP, Ma BB, Leung SF, King AD, Mo F, Kam MK, Yu BK, Chiu SK, Kwan WH, Ho R, Chan I, Ahuja AT, Zee BC, Chan AT. Randomized phase II trial of concurrent cisplatin-radiotherapy with or without neoadjuvant docetaxel and cisplatin in advanced nasopharyngeal carcinoma. J Clin Oncol. 2009;27:242-249.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 377]  [Cited by in F6Publishing: 405]  [Article Influence: 25.3]  [Reference Citation Analysis (0)]
6.  Chen Y, Liu MZ, Liang SB, Zong JF, Mao YP, Tang LL, Guo Y, Lin AH, Zeng XF, Ma J. Preliminary results of a prospective randomized trial comparing concurrent chemoradiotherapy plus adjuvant chemotherapy with radiotherapy alone in patients with locoregionally advanced nasopharyngeal carcinoma in endemic regions of china. Int J Radiat Oncol Biol Phys. 2008;71:1356-1364.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 163]  [Cited by in F6Publishing: 181]  [Article Influence: 11.3]  [Reference Citation Analysis (0)]
7.  Sun Y, Li WF, Chen NY, Zhang N, Hu GQ, Xie FY, Sun Y, Chen XZ, Li JG, Zhu XD, Hu CS, Xu XY, Chen YY, Hu WH, Guo L, Mo HY, Chen L, Mao YP, Sun R, Ai P, Liang SB, Long GX, Zheng BM, Feng XL, Gong XC, Li L, Shen CY, Xu JY, Guo Y, Chen YM, Zhang F, Lin L, Tang LL, Liu MZ, Ma J. Induction chemotherapy plus concurrent chemoradiotherapy vs concurrent chemoradiotherapy alone in locoregionally advanced nasopharyngeal carcinoma: a phase 3, multicentre, randomised controlled trial. Lancet Oncol. 2016;17:1509-1520.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 537]  [Cited by in F6Publishing: 610]  [Article Influence: 76.3]  [Reference Citation Analysis (0)]
8.  Ribassin-Majed L, Marguet S, Lee AWM, Ng WT, Ma J, Chan ATC, Huang PY, Zhu G, Chua DTT, Chen Y, Mai HQ, Kwong DLW, Cheah SL, Moon J, Tung Y, Chi KH, Fountzilas G, Bourhis J, Pignon JP, Blanchard P. What Is the Best Treatment of Locally Advanced Nasopharyngeal Carcinoma? An Individual Patient Data Network Meta-Analysis. J Clin Oncol. 2017;35:498-505.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 236]  [Cited by in F6Publishing: 218]  [Article Influence: 31.1]  [Reference Citation Analysis (0)]
9.  Pan JJ, Ng WT, Zong JF, Chan LL, O'Sullivan B, Lin SJ, Sze HC, Chen YB, Choi HC, Guo QJ, Kan WK, Xiao YP, Wei X, Le QT, Glastonbury CM, Colevas AD, Weber RS, Shah JP, Lee AW. Proposal for the 8th edition of the AJCC/UICC staging system for nasopharyngeal cancer in the era of intensity-modulated radiotherapy. Cancer. 2016;122:546-558.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 172]  [Cited by in F6Publishing: 225]  [Article Influence: 25.0]  [Reference Citation Analysis (0)]
10.  Zhang Z, Zhao Y, Canes A, Steinberg D, Lyashevska O; written on behalf of AME Big-Data Clinical Trial Collaborative Group. Predictive analytics with gradient boosting in clinical medicine. Ann Transl Med. 2019;7:152.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 56]  [Cited by in F6Publishing: 85]  [Article Influence: 17.0]  [Reference Citation Analysis (0)]
11.  Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, Shilton A, Yearwood J, Dimitrova N, Ho TB, Venkatesh S, Berk M. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View. J Med Internet Res. 2016;18:e323.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 366]  [Cited by in F6Publishing: 522]  [Article Influence: 65.3]  [Reference Citation Analysis (0)]
12.  Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8-17.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1362]  [Cited by in F6Publishing: 1088]  [Article Influence: 108.8]  [Reference Citation Analysis (0)]
13.  Chen Y, Liu Y. Which Risk Factors Matter More for Psychological Distress during the COVID-19 Pandemic? An Application Approach of Gradient Boosting Decision Trees. Int J Environ Res Public Health. 2021;18.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 12]  [Cited by in F6Publishing: 5]  [Article Influence: 1.7]  [Reference Citation Analysis (0)]
14.  Ding C, Wang D, Ma X, Li H. Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees. Sustainability. 2016;8:1100.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 95]  [Cited by in F6Publishing: 95]  [Article Influence: 11.9]  [Reference Citation Analysis (1)]
15.  Deng X, Thurston G, Zhang W, Ryan I, Jiang C, Khwaja H, Romeiko X, Marks T, Ye B, Qu Y, Lin S. Application of data science methods to identify school and home risk factors for asthma and allergy-related symptoms among children in New York. Sci Total Environ. 2021;770:144746.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 8]  [Cited by in F6Publishing: 6]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]
16.  Zhou X, He X, Xue F, Ou X, Hu C. Impact of Paranasal Sinus Invasion on Oncologic and Dosimetric Outcomes in Nasopharyngeal Carcinoma Following Intensity-Modulated Radiation Therapy-Implications for Risk Stratification and Planning Optimization. Front Oncol. 2020;10:407.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
17.  Ai QY, King AD, Poon DMC, Mo FKF, Hui EP, Tong M, Ahuja AT, Ma BBY, Chan ATC. Extranodal extension is a criterion for poor outcome in patients with metastatic nodes from cancer of the nasopharynx. Oral Oncol. 2019;88:124-130.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 26]  [Cited by in F6Publishing: 28]  [Article Influence: 5.6]  [Reference Citation Analysis (0)]
18.  Ma H, Liang S, Cui C, Zhang Y, Xie F, Zhou J, Dong A, Chen M, Xie C, Li H, Liu L. Prognostic significance of quantitative metastatic lymph node burden on magnetic resonance imaging in nasopharyngeal carcinoma: A retrospective study of 1224 patients from two centers. Radiother Oncol. 2020;151:40-46.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 7]  [Cited by in F6Publishing: 21]  [Article Influence: 5.3]  [Reference Citation Analysis (0)]
19.  Bin Y, Meng Z, Huang LL, Hu XY, Song JM, Xie YT, Kang M, Wang RS. Prognostic value of the cervical lymph node necrosis ratio in nasopharyngeal carcinoma. Radiother Oncol. 2022;177:185-190.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
20.  Qiang M, Li C, Sun Y, Ke L, Xie C, Zhang T, Zou Y, Qiu W, Gao M, Li Y, Li X, Zhan Z, Liu K, Chen X, Liang C, Chen Q, Mai H, Xie G, Guo X, Lv X. A Prognostic Predictive System Based on Deep Learning for Locoregionally Advanced Nasopharyngeal Carcinoma. J Natl Cancer Inst. 2021;113:606-615.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 17]  [Cited by in F6Publishing: 39]  [Article Influence: 9.8]  [Reference Citation Analysis (0)]
21.  21 Chen X, Li Y, Li X, Cao X, Xiang Y, Xia W, Li J, Gao M, Sun Y, Liu K, Qiang M, Liang C, Miao J, Cai Z, Guo X, Li C, Xie G, Lv X. An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features. Oral Oncol. 2021;118:105335.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 6]  [Cited by in F6Publishing: 7]  [Article Influence: 2.3]  [Reference Citation Analysis (0)]
22.  Sham JS, Choy D, Wei WI. Nasopharyngeal carcinoma: orderly neck node spread. Int J Radiat Oncol Biol Phys. 1990;19:929-933.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 114]  [Cited by in F6Publishing: 108]  [Article Influence: 3.2]  [Reference Citation Analysis (0)]