Machine Learning Methods in Classification of Prolonged Radiation Therapy in Oropharyngeal Cancer: National Cancer Database.

IF 2.6 3区医学 Q1 OTORHINOLARYNGOLOGY

Otolaryngology- Head and Neck Surgery Pub Date : 2024-12-01 Epub Date: 2024-07-31 DOI:10.1002/ohn.926

Seungjun Ahn, Eun Jeong Oh, Matthew I Saleem, Tristan Tham

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引用次数: 0

Abstract

Objective: To investigate the accuracy of machine learning (ML) algorithms in stratifying risk of prolonged radiation treatment duration (RTD), defined as greater than 50 days, for patients with oropharyngeal squamous cell carcinoma (OPSCC).

Study design: Retrospective cohort study.

Setting: National Cancer Database (NCDB).

Methods: The NCDB was queried between 2004 to 2016 for patients with OPSCC treated with radiation therapy (RT) or chemoradiation as primary treatment. To predict risk of prolonged RTD, 8 different ML algorithms were compared against traditional logistic regression using various performance metrics. Data was split into a distribution of 70% for training and 30% for testing.

Results: A total of 3152 patients were included (1928 prolonged RT, 1224 not prolonged RT). As a whole, based on performance metrics, random forest (RF) was found to most accurately predict prolonged RTD compared to both other ML methods and traditional logistic regression.

Conclusion: Our assessment of various ML techniques showed that RF was superior to traditional logistic regression at classifying OPSCC patients at risk of prolonged RTD. Application of such algorithms may have potential to identify high risk patients and enable early interventions to improve survival.

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口咽癌延长放疗分类中的机器学习方法：全国癌症数据库

目的调查机器学习（ML）算法对口咽鳞状细胞癌（OPSCC）患者放疗时间延长（RTD）（定义为超过50天）风险分层的准确性：研究设计：回顾性队列研究：国家癌症数据库（NCDB）：2004年至2016年期间，对NCDB中以放疗（RT）或化放疗作为主要治疗手段的OPSCC患者进行了查询。为了预测RTD延长的风险，使用各种性能指标将8种不同的ML算法与传统的逻辑回归进行了比较。数据被分成70%用于训练，30%用于测试：共纳入了 3152 例患者（1928 例长期 RT，1224 例非长期 RT）。总体而言，根据性能指标，随机森林（RF）与其他 ML 方法和传统的逻辑回归相比，能最准确地预测延长的 RTD：我们对各种ML技术的评估表明，RF在对有延长RTD风险的OPSCC患者进行分类方面优于传统的逻辑回归。应用这种算法有可能识别出高风险患者，并进行早期干预以提高生存率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Otolaryngology- Head and Neck Surgery 医学-耳鼻喉科学

CiteScore

6.70

自引率

2.90%

发文量

250

审稿时长

2-4 weeks

期刊介绍： Otolaryngology–Head and Neck Surgery (OTO-HNS) is the official peer-reviewed publication of the American Academy of Otolaryngology–Head and Neck Surgery Foundation. The mission of Otolaryngology–Head and Neck Surgery is to publish contemporary, ethical, clinically relevant information in otolaryngology, head and neck surgery (ear, nose, throat, head, and neck disorders) that can be used by otolaryngologists, clinicians, scientists, and specialists to improve patient care and public health.