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

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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.

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Most cited references 22

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Machine learning applications in cancer prognosis and prediction

Konstantina Kourou, Themis Exarchos, Konstantinos P Exarchos … (2014)

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

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Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Wei Luo, Dinh Phung, Truyen Tran … (2016)

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.

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Induction chemotherapy plus concurrent chemoradiotherapy versus concurrent chemoradiotherapy alone in locoregionally advanced nasopharyngeal carcinoma: a phase 3, multicentre, randomised controlled trial.

Ying Sun, Wen-Fei Li, Nian-Yong Chen … (2016)

The value of adding cisplatin, fluorouracil, and docetaxel (TPF) induction chemotherapy to concurrent chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma is unclear. We aimed to compare TPF induction chemotherapy plus concurrent chemoradiotherapy with concurrent chemoradiotherapy alone in a suitably powered trial.

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Author and article information

Contributors

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

Journal

Journal ID (nlm-ta): World J Radiol

Journal ID (publisher-id): WJR

Title: World Journal of Radiology

Publisher: Baishideng Publishing Group Inc

ISSN (Electronic): 1949-8470

Publication date (Print): 28 June 2024

Publication date (Electronic): 28 June 2024

Volume: 16

Issue: 6

Pages: 203-210

Affiliations

Department of Nasopharyngeal Head and Neck Tumor Radiotherapy, Zhongshan City People's Hospital, Zhongshan 528400, Guangdong Province, China

School of Public Health, Sun Yat-sen University, Guangzhou 510060, Guangdong Province, China

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

Department of Nasopharyngeal Head and Neck Tumor Radiotherapy, Zhongshan City People's Hospital, Zhongshan 528400, Guangdong Province, China

Author notes

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.

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@ 123456sysucc.org.cn

Article

Other ID: jWJR.v16.i6.pg203 Publisher-manuscript ID: 94017

DOI: 10.4329/wjr.v16.i6.203

PMC ID: 11229946

PubMed ID: 38983838

SO-VID: 109de04b-5f77-4faa-bc1a-92c78737958b

License:

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.

History

Date received : 9 March 2024

Date revision received : 13 May 2024

Date accepted : 28 May 2024

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

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Abstract

BACKGROUND

AIM

METHODS

RESULTS

CONCLUSION

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Annual Reviews AI, Machine Learning, and Society

Most cited references 22

Machine learning applications in cancer prognosis and prediction

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Induction chemotherapy plus concurrent chemoradiotherapy versus concurrent chemoradiotherapy alone in locoregionally advanced nasopharyngeal carcinoma: a phase 3, multicentre, randomised controlled trial.

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