利用机器学习确定放疗后ct定义的肌肉损失阈值对口腔癌患者生存的影响。

IF 4.7 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
European Radiology Pub Date : 2025-07-01 Epub Date: 2024-12-20 DOI:10.1007/s00330-024-11303-4
Jie Lee, Jhen-Bin Lin, Wan-Chun Lin, Ya-Ting Jan, Yi-Shing Leu, Yu-Jen Chen, Kun-Pin Wu
{"title":"利用机器学习确定放疗后ct定义的肌肉损失阈值对口腔癌患者生存的影响。","authors":"Jie Lee, Jhen-Bin Lin, Wan-Chun Lin, Ya-Ting Jan, Yi-Shing Leu, Yu-Jen Chen, Kun-Pin Wu","doi":"10.1007/s00330-024-11303-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial intelligence to identify the threshold of muscle loss associated with survival in OCSCC.</p><p><strong>Materials and methods: </strong>We enrolled 1087 patients with OCSCC treated with surgery and adjuvant radiotherapy at two tertiary centers (660 in the derivation cohort and 427 in the external validation cohort). Skeletal muscle index (SMI) was measured using pre- and post-radiotherapy computed tomography (CT) at the C3 vertebral level. Random forest (RF), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) models were developed to predict all-cause mortality, and their performances were evaluated using the area under the curve (AUC). Muscle loss threshold was identified using the SHapley Additive exPlanations (SHAP) method and validated using Cox regression analysis.</p><p><strong>Results: </strong>In the external validation cohort, the RF, XGBoost, and CatBoost models achieved favorable performance in predicting all-cause mortality (AUC: 0.898, 0.859, and 0.842). The SHAP method demonstrated that SMI change after radiotherapy was the most important feature for predicting all-cause mortality and consistently identified SMI loss ≥ 4.2% as the threshold in all three models. In multivariable analysis, SMI loss ≥ 4.2% was independently associated with increased all-cause mortality risk in both cohorts (derivation cohort: hazard ratio: 6.66, p < 0.001; external validation cohort: hazard ratio: 8.46, p < 0.001).</p><p><strong>Conclusion: </strong>This study can assist clinicians in identifying patients with considerable muscle loss after treatment and guide interventions to improve muscle mass.</p><p><strong>Key points: </strong>Question Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity cancer; however, the threshold of muscle loss remains unclear. Findings Explainable artificial intelligence identified muscle loss ≥ 4.2% as the threshold of increased all-cause mortality risk in both derivation and external validation cohorts. Clinical Relevance Muscle loss ≥ 4.2% may be the optimal threshold for survival in patients who receive adjuvant radiotherapy for oral cavity cancer. This threshold can guide clinicians in improving muscle mass after radiotherapy.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"4289-4299"},"PeriodicalIF":4.7000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning.\",\"authors\":\"Jie Lee, Jhen-Bin Lin, Wan-Chun Lin, Ya-Ting Jan, Yi-Shing Leu, Yu-Jen Chen, Kun-Pin Wu\",\"doi\":\"10.1007/s00330-024-11303-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial intelligence to identify the threshold of muscle loss associated with survival in OCSCC.</p><p><strong>Materials and methods: </strong>We enrolled 1087 patients with OCSCC treated with surgery and adjuvant radiotherapy at two tertiary centers (660 in the derivation cohort and 427 in the external validation cohort). Skeletal muscle index (SMI) was measured using pre- and post-radiotherapy computed tomography (CT) at the C3 vertebral level. Random forest (RF), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) models were developed to predict all-cause mortality, and their performances were evaluated using the area under the curve (AUC). Muscle loss threshold was identified using the SHapley Additive exPlanations (SHAP) method and validated using Cox regression analysis.</p><p><strong>Results: </strong>In the external validation cohort, the RF, XGBoost, and CatBoost models achieved favorable performance in predicting all-cause mortality (AUC: 0.898, 0.859, and 0.842). The SHAP method demonstrated that SMI change after radiotherapy was the most important feature for predicting all-cause mortality and consistently identified SMI loss ≥ 4.2% as the threshold in all three models. In multivariable analysis, SMI loss ≥ 4.2% was independently associated with increased all-cause mortality risk in both cohorts (derivation cohort: hazard ratio: 6.66, p < 0.001; external validation cohort: hazard ratio: 8.46, p < 0.001).</p><p><strong>Conclusion: </strong>This study can assist clinicians in identifying patients with considerable muscle loss after treatment and guide interventions to improve muscle mass.</p><p><strong>Key points: </strong>Question Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity cancer; however, the threshold of muscle loss remains unclear. Findings Explainable artificial intelligence identified muscle loss ≥ 4.2% as the threshold of increased all-cause mortality risk in both derivation and external validation cohorts. Clinical Relevance Muscle loss ≥ 4.2% may be the optimal threshold for survival in patients who receive adjuvant radiotherapy for oral cavity cancer. This threshold can guide clinicians in improving muscle mass after radiotherapy.</p>\",\"PeriodicalId\":12076,\"journal\":{\"name\":\"European Radiology\",\"volume\":\" \",\"pages\":\"4289-4299\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00330-024-11303-4\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00330-024-11303-4","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/20 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

摘要

目的:口腔鳞状细胞癌(OCSCC)患者放疗后肌肉损失与较差的生存率相关。然而,肌肉损失的阈值仍不清楚。本研究旨在利用可解释的人工智能来确定与OCSCC存活相关的肌肉损失阈值。材料和方法:我们在两个三级中心招募了1087例接受手术和辅助放疗治疗的OCSCC患者(衍生队列660例,外部验证队列427例)。骨骼肌指数(SMI)采用放疗前和放疗后的计算机断层扫描(CT)在C3椎体水平测量。建立了随机森林(RF)、极端梯度提升(XGBoost)和分类提升(CatBoost)模型来预测全因死亡率,并使用曲线下面积(AUC)来评估它们的性能。使用SHapley加性解释(SHAP)方法确定肌肉损失阈值,并使用Cox回归分析进行验证。结果:在外部验证队列中,RF、XGBoost和CatBoost模型在预测全因死亡率方面表现良好(AUC: 0.898、0.859和0.842)。SHAP方法表明,放疗后SMI变化是预测全因死亡率的最重要特征,并一致将SMI损失≥4.2%作为所有三种模型的阈值。在多变量分析中,在两个队列中,SMI损失≥4.2%与全因死亡风险增加独立相关(衍生队列:风险比:6.66,p)。结论:本研究可以帮助临床医生识别治疗后明显肌肉损失的患者,并指导干预措施改善肌肉质量。口腔癌患者放疗后肌肉损失与较差的生存率相关;然而,肌肉损失的阈值仍不清楚。可解释的人工智能将肌肉损失≥4.2%确定为衍生和外部验证队列中全因死亡风险增加的阈值。在接受口腔癌辅助放疗的患者中,肌肉损失≥4.2%可能是生存的最佳阈值。这个阈值可以指导临床医生改善放疗后的肌肉质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning.

Objectives: Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial intelligence to identify the threshold of muscle loss associated with survival in OCSCC.

Materials and methods: We enrolled 1087 patients with OCSCC treated with surgery and adjuvant radiotherapy at two tertiary centers (660 in the derivation cohort and 427 in the external validation cohort). Skeletal muscle index (SMI) was measured using pre- and post-radiotherapy computed tomography (CT) at the C3 vertebral level. Random forest (RF), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) models were developed to predict all-cause mortality, and their performances were evaluated using the area under the curve (AUC). Muscle loss threshold was identified using the SHapley Additive exPlanations (SHAP) method and validated using Cox regression analysis.

Results: In the external validation cohort, the RF, XGBoost, and CatBoost models achieved favorable performance in predicting all-cause mortality (AUC: 0.898, 0.859, and 0.842). The SHAP method demonstrated that SMI change after radiotherapy was the most important feature for predicting all-cause mortality and consistently identified SMI loss ≥ 4.2% as the threshold in all three models. In multivariable analysis, SMI loss ≥ 4.2% was independently associated with increased all-cause mortality risk in both cohorts (derivation cohort: hazard ratio: 6.66, p < 0.001; external validation cohort: hazard ratio: 8.46, p < 0.001).

Conclusion: This study can assist clinicians in identifying patients with considerable muscle loss after treatment and guide interventions to improve muscle mass.

Key points: Question Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity cancer; however, the threshold of muscle loss remains unclear. Findings Explainable artificial intelligence identified muscle loss ≥ 4.2% as the threshold of increased all-cause mortality risk in both derivation and external validation cohorts. Clinical Relevance Muscle loss ≥ 4.2% may be the optimal threshold for survival in patients who receive adjuvant radiotherapy for oral cavity cancer. This threshold can guide clinicians in improving muscle mass after radiotherapy.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
European Radiology
European Radiology 医学-核医学
CiteScore
11.60
自引率
8.50%
发文量
874
审稿时长
2-4 weeks
期刊介绍: European Radiology (ER) continuously updates scientific knowledge in radiology by publication of strong original articles and state-of-the-art reviews written by leading radiologists. A well balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes ER an indispensable source for current information in this field. This is the Journal of the European Society of Radiology, and the official journal of a number of societies. From 2004-2008 supplements to European Radiology were published under its companion, European Radiology Supplements, ISSN 1613-3749.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信