PET/computed tomography radiomics combined with clinical features in predicting sarcopenia and prognosis of diffuse large B-cell lymphoma.

IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Nuclear Medicine Communications Pub Date : 2024-11-05 DOI:10.1097/MNM.0000000000001925
Fanghu Wang, Yang Chen, Xiaoyue Tan, Xu Han, Wantong Lu, Lijun Lu, Hui Yuan, Lei Jiang
{"title":"PET/computed tomography radiomics combined with clinical features in predicting sarcopenia and prognosis of diffuse large B-cell lymphoma.","authors":"Fanghu Wang, Yang Chen, Xiaoyue Tan, Xu Han, Wantong Lu, Lijun Lu, Hui Yuan, Lei Jiang","doi":"10.1097/MNM.0000000000001925","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The study aimed to assess the role of 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) radiomics combined with clinical features using machine learning (ML) in predicting sarcopenia and prognosis of patients with diffuse large B-cell lymphoma (DLBCL).</p><p><strong>Methods: </strong>A total of 178 DLBCL patients (118 and 60 applied for training and test sets, respectively) who underwent pretreatment 18F-FDG PET/CT were retrospectively enrolled. Clinical characteristics and PET/CT radiomics features were analyzed, and feature selection was performed using univariate logistic regression and correlation analysis. Sarcopenia prediction models were built by ML algorithms and evaluated. Besides, prognostic models were also developed, and their associations with progression-free survival (PFS) and overall survival (OS) were identified.</p><p><strong>Results: </strong>Fourteen features were finally selected to build sarcopenia prediction and prognosis models, including two clinical (maximum standard uptake value of muscle and BMI), nine PET (seven gray-level and two first-order), and three CT (three gray-level) radiomics features. Among sarcopenia prediction models, combined clinical-PET/CT radiomics features models outperformed other models; especially the support vector machine algorithm achieved the highest area under curve of 0.862, with the sensitivity, specificity, and accuracy of 79.2, 83.3, and 78.3% in the test set. Furthermore, the consistency index based on the prognostic models was 0.753 and 0.807 for PFS and OS, respectively. The enrolled patients were subsequently divided into high-risk and low-risk groups with significant differences, regardless of PFS or OS (P < 0.05).</p><p><strong>Conclusion: </strong>ML models incorporating clinical and PET/CT radiomics features could effectively predict the presence of sarcopenia and assess the prognosis in patients with DLBCL.</p>","PeriodicalId":19708,"journal":{"name":"Nuclear Medicine Communications","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nuclear Medicine Communications","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/MNM.0000000000001925","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Background: The study aimed to assess the role of 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) radiomics combined with clinical features using machine learning (ML) in predicting sarcopenia and prognosis of patients with diffuse large B-cell lymphoma (DLBCL).

Methods: A total of 178 DLBCL patients (118 and 60 applied for training and test sets, respectively) who underwent pretreatment 18F-FDG PET/CT were retrospectively enrolled. Clinical characteristics and PET/CT radiomics features were analyzed, and feature selection was performed using univariate logistic regression and correlation analysis. Sarcopenia prediction models were built by ML algorithms and evaluated. Besides, prognostic models were also developed, and their associations with progression-free survival (PFS) and overall survival (OS) were identified.

Results: Fourteen features were finally selected to build sarcopenia prediction and prognosis models, including two clinical (maximum standard uptake value of muscle and BMI), nine PET (seven gray-level and two first-order), and three CT (three gray-level) radiomics features. Among sarcopenia prediction models, combined clinical-PET/CT radiomics features models outperformed other models; especially the support vector machine algorithm achieved the highest area under curve of 0.862, with the sensitivity, specificity, and accuracy of 79.2, 83.3, and 78.3% in the test set. Furthermore, the consistency index based on the prognostic models was 0.753 and 0.807 for PFS and OS, respectively. The enrolled patients were subsequently divided into high-risk and low-risk groups with significant differences, regardless of PFS or OS (P < 0.05).

Conclusion: ML models incorporating clinical and PET/CT radiomics features could effectively predict the presence of sarcopenia and assess the prognosis in patients with DLBCL.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
PET/计算机断层扫描放射组学结合临床特征预测弥漫大B细胞淋巴瘤的肌肉疏松症和预后。
研究背景该研究旨在利用机器学习(ML)评估18F-氟脱氧葡萄糖(FDG)PET/计算机断层扫描(CT)放射组学结合临床特征在预测弥漫大B细胞淋巴瘤(DLBCL)患者肌少症和预后中的作用:回顾性研究共纳入了178名接受治疗前18F-FDG PET/CT检查的弥漫大B细胞淋巴瘤患者(分别有118人和60人应用于训练集和测试集)。分析了临床特征和 PET/CT 放射组学特征,并使用单变量逻辑回归和相关分析进行了特征选择。通过 ML 算法建立并评估了肌少症预测模型。此外,还建立了预后模型,并确定了它们与无进展生存期(PFS)和总生存期(OS)的关系:结果:最终选择了 14 个特征来建立肌肉疏松症预测和预后模型,其中包括 2 个临床特征(肌肉最大标准摄取值和体重指数)、9 个 PET 特征(7 个灰阶特征和 2 个一阶特征)以及 3 个 CT 特征(3 个灰阶特征)。在肌少症预测模型中,临床-PET/CT放射组学组合特征模型的表现优于其他模型,尤其是支持向量机算法的曲线下面积最高,达到了 0.862,测试集的灵敏度、特异度和准确度分别为 79.2%、83.3% 和 78.3%。此外,基于预后模型的 PFS 和 OS 一致性指数分别为 0.753 和 0.807。随后,入组患者被分为高危和低危两组,无论PFS还是OS,两组均有显著差异(P 结论:高危组的PFS和OS均高于低危组(P 结论:低危组的PFS和OS均高于高危组):结合临床和 PET/CT 放射组学特征的 ML 模型可有效预测 DLBCL 患者是否存在肌肉疏松症并评估其预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
2.20
自引率
6.70%
发文量
212
审稿时长
3-8 weeks
期刊介绍: Nuclear Medicine Communications, the official journal of the British Nuclear Medicine Society, is a rapid communications journal covering nuclear medicine and molecular imaging with radionuclides, and the basic supporting sciences. As well as clinical research and commentary, manuscripts describing research on preclinical and basic sciences (radiochemistry, radiopharmacy, radiobiology, radiopharmacology, medical physics, computing and engineering, and technical and nursing professions involved in delivering nuclear medicine services) are welcomed, as the journal is intended to be of interest internationally to all members of the many medical and non-medical disciplines involved in nuclear medicine. In addition to papers reporting original studies, frankly written editorials and topical reviews are a regular feature of the journal.
期刊最新文献
PET/computed tomography radiomics combined with clinical features in predicting sarcopenia and prognosis of diffuse large B-cell lymphoma. Radionuclide therapy of bevacizumab-based PNA-mediated pretargeting. Correlation of PSA blood levels with standard uptake value maximum (SUV max ) and total metabolic tumor volume (TMTV) in 18F-PSMA-1007 and 18F-choline PET/CT in patients with biochemically recurrent prostate cancer. Effect of single-photon emission computed tomography acquisition method and sampling angles on image quality and quantitative accuracy in xSPECT-reconstructed images. Significance of incidental thyroid 18 F-fluorocholine uptake in patients with hyperparathyroidism imaged for localizing hyperfunctioning parathyroid glands.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1