液体活检与 CT:1065 例转移瘤患者的肿瘤负荷定量比较。

IF 12.1 1区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Radiology Pub Date : 2024-11-01 DOI:10.1148/radiol.232674
Lama Dawi, Younes Belkouchi, Littisha Lawrance, Othilie Gautier, Samy Ammari, Damien Vasseur, Felix Wirth, Joya Hadchiti, Salome Morer, Clemence David, François Bidault, Corinne Balleyguier, Michèle Kind, Arnaud Bayle, Laila Belcaid, Mihaela Aldea, Claudio Nicotra, Arthur Geraud, Madona Sakkal, Felix Blanc-Durand, Sophie Moog, Maria Fernanda Mosele, Marco Tagliamento, Alice Bernard-Tessier, Benjamin Verret, Cristina Smolenschi, Nathalie Auger, Anas Gazzah, Jean-Baptiste Micol, Olivier Caron, Antoine Hollebecque, Yohann Loriot, Benjamin Besse, Ludovic Lacroix, Etienne Rouleau, Santiago Ponce, Fabrice André, Jean-Charles Soria, Fabrice Barlesi, Serge Muller, Paul-Henry Cournede, Hugues Talbot, Antoine Italiano, Nathalie Lassau
{"title":"液体活检与 CT:1065 例转移瘤患者的肿瘤负荷定量比较。","authors":"Lama Dawi, Younes Belkouchi, Littisha Lawrance, Othilie Gautier, Samy Ammari, Damien Vasseur, Felix Wirth, Joya Hadchiti, Salome Morer, Clemence David, François Bidault, Corinne Balleyguier, Michèle Kind, Arnaud Bayle, Laila Belcaid, Mihaela Aldea, Claudio Nicotra, Arthur Geraud, Madona Sakkal, Felix Blanc-Durand, Sophie Moog, Maria Fernanda Mosele, Marco Tagliamento, Alice Bernard-Tessier, Benjamin Verret, Cristina Smolenschi, Nathalie Auger, Anas Gazzah, Jean-Baptiste Micol, Olivier Caron, Antoine Hollebecque, Yohann Loriot, Benjamin Besse, Ludovic Lacroix, Etienne Rouleau, Santiago Ponce, Fabrice André, Jean-Charles Soria, Fabrice Barlesi, Serge Muller, Paul-Henry Cournede, Hugues Talbot, Antoine Italiano, Nathalie Lassau","doi":"10.1148/radiol.232674","DOIUrl":null,"url":null,"abstract":"<p><p>Background Tumor fraction (TF) at liquid biopsy is a potential noninvasive marker for tumor burden, but validation is needed. Purpose To evaluate TF as a potential surrogate for tumor burden, assessed at contrast-enhanced CT across diverse metastatic cancers. Methods This retrospective monocentric study included patients with cancer and metastatic disease, with TF results and contemporaneous contrast-enhanced CT performed between January 2021 and January 2023. The total tumor volume (TTV), representing CT tumor burden, was calculated by adding all lesion volumes and was computed by using manually outlined annotations of each lesion on the largest surface of the axial slice. TF greater than 10% was considered high. A training-validation split was applied. Correlations between TF and TTV were assessed using regression models and Spearman correlation coefficients. Receiver operating characteristic curve analysis established the TTV cutoff. The metastatic site, histology type, and TTV were used to predict liquid biopsy contributory status. Results Among 1065 patients (median age, 62 years [IQR: 53, 70]; 537 female), 56 288 lesions were annotated, mostly in the lung (<i>n</i> = 20 334), lymph nodes (<i>n</i> = 11 651), and liver (<i>n</i> = 10 277). A total of 763 liquid biopsies were contributive, 254 were noncontributive, and 48 failed. The training and validation sets included 745 and 320 patients, respectively. TF helped predict TTV with the linear model (<i>R</i><sup>2</sup> = 0.17; ρ = 0.41; <i>P</i> < .001). The TTV and TF categories achieved an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.71, 0.78), with an optimal cutoff of 151 cm<sup>3</sup> for TTV and a TF cutoff of 10%. The sensitivity was 57% (204 of 359) and the specificity was 80% (525 of 658). TTV helped predict contributory status, with an AUC of 0.71 (95% CI: 0.67, 0.76) and an optimal cutoff greater than 37 cm<sup>3</sup>. Liver lesion volumes were significantly associated with a contributory liquid biopsy in the validation cohort. Conclusion While correlated, TF at liquid biopsy did not accurately represent the TTV at CT. © RSNA, 2024 <i>Supplemental material is available for this article.</i> See also the editorial by Koh in this issue.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e232674"},"PeriodicalIF":12.1000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Liquid Biopsy versus CT: Comparison of Tumor Burden Quantification in 1065 Patients with Metastases.\",\"authors\":\"Lama Dawi, Younes Belkouchi, Littisha Lawrance, Othilie Gautier, Samy Ammari, Damien Vasseur, Felix Wirth, Joya Hadchiti, Salome Morer, Clemence David, François Bidault, Corinne Balleyguier, Michèle Kind, Arnaud Bayle, Laila Belcaid, Mihaela Aldea, Claudio Nicotra, Arthur Geraud, Madona Sakkal, Felix Blanc-Durand, Sophie Moog, Maria Fernanda Mosele, Marco Tagliamento, Alice Bernard-Tessier, Benjamin Verret, Cristina Smolenschi, Nathalie Auger, Anas Gazzah, Jean-Baptiste Micol, Olivier Caron, Antoine Hollebecque, Yohann Loriot, Benjamin Besse, Ludovic Lacroix, Etienne Rouleau, Santiago Ponce, Fabrice André, Jean-Charles Soria, Fabrice Barlesi, Serge Muller, Paul-Henry Cournede, Hugues Talbot, Antoine Italiano, Nathalie Lassau\",\"doi\":\"10.1148/radiol.232674\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Background Tumor fraction (TF) at liquid biopsy is a potential noninvasive marker for tumor burden, but validation is needed. Purpose To evaluate TF as a potential surrogate for tumor burden, assessed at contrast-enhanced CT across diverse metastatic cancers. Methods This retrospective monocentric study included patients with cancer and metastatic disease, with TF results and contemporaneous contrast-enhanced CT performed between January 2021 and January 2023. The total tumor volume (TTV), representing CT tumor burden, was calculated by adding all lesion volumes and was computed by using manually outlined annotations of each lesion on the largest surface of the axial slice. TF greater than 10% was considered high. A training-validation split was applied. Correlations between TF and TTV were assessed using regression models and Spearman correlation coefficients. Receiver operating characteristic curve analysis established the TTV cutoff. The metastatic site, histology type, and TTV were used to predict liquid biopsy contributory status. Results Among 1065 patients (median age, 62 years [IQR: 53, 70]; 537 female), 56 288 lesions were annotated, mostly in the lung (<i>n</i> = 20 334), lymph nodes (<i>n</i> = 11 651), and liver (<i>n</i> = 10 277). A total of 763 liquid biopsies were contributive, 254 were noncontributive, and 48 failed. The training and validation sets included 745 and 320 patients, respectively. TF helped predict TTV with the linear model (<i>R</i><sup>2</sup> = 0.17; ρ = 0.41; <i>P</i> < .001). The TTV and TF categories achieved an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.71, 0.78), with an optimal cutoff of 151 cm<sup>3</sup> for TTV and a TF cutoff of 10%. The sensitivity was 57% (204 of 359) and the specificity was 80% (525 of 658). TTV helped predict contributory status, with an AUC of 0.71 (95% CI: 0.67, 0.76) and an optimal cutoff greater than 37 cm<sup>3</sup>. Liver lesion volumes were significantly associated with a contributory liquid biopsy in the validation cohort. Conclusion While correlated, TF at liquid biopsy did not accurately represent the TTV at CT. © RSNA, 2024 <i>Supplemental material is available for this article.</i> See also the editorial by Koh in this issue.</p>\",\"PeriodicalId\":20896,\"journal\":{\"name\":\"Radiology\",\"volume\":\"313 2\",\"pages\":\"e232674\"},\"PeriodicalIF\":12.1000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1148/radiol.232674\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1148/radiol.232674","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

摘要

背景 液体活检中的肿瘤分数(TF)是肿瘤负荷的潜在非侵入性标记物,但需要验证。目的 通过对比增强 CT 评估不同转移性癌症患者的 TF,将其作为肿瘤负荷的潜在替代指标。方法 该回顾性单中心研究纳入了癌症和转移性疾病患者,他们在 2021 年 1 月至 2023 年 1 月期间接受了 TF 结果和同期对比增强 CT 检查。代表 CT 肿瘤负荷的肿瘤总体积(TTV)是通过将所有病灶体积相加计算得出的,并使用轴切片最大表面上每个病灶的人工勾画注释进行计算。TF大于10%即为高值。采用训练-验证分离法。使用回归模型和斯皮尔曼相关系数评估 TF 和 TTV 之间的相关性。接收者操作特征曲线分析确定了 TTV 临界值。转移部位、组织学类型和TTV用于预测液体活检的贡献状态。结果 在1065名患者(中位年龄62岁[IQR:53,70];女性537名)中,有56 288个病灶得到了注释,大部分在肺部(n = 20 334)、淋巴结(n = 11 651)和肝脏(n = 10 277)。共有 763 例液体活检有贡献,254 例无贡献,48 例失败。训练集和验证集分别包括 745 名和 320 名患者。TF有助于通过线性模型预测TTV(R2 = 0.17;ρ = 0.41;P < .001)。TTV和TF类别的接收者操作特征曲线下面积(AUC)为0.74(95% CI:0.71,0.78),TTV的最佳临界值为151 cm3,TF临界值为10%。灵敏度为 57%(359 例中的 204 例),特异性为 80%(658 例中的 525 例)。TTV 有助于预测贡献状态,其 AUC 为 0.71(95% CI:0.67,0.76),最佳临界值大于 37 立方厘米。在验证队列中,肝脏病变体积与有贡献的液体活检结果明显相关。结论 液体活检的 TF 虽然相关,但并不能准确代表 CT 的 TTV。© RSNA, 2024 这篇文章有补充材料。另请参阅本期 Koh 的社论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Liquid Biopsy versus CT: Comparison of Tumor Burden Quantification in 1065 Patients with Metastases.

Background Tumor fraction (TF) at liquid biopsy is a potential noninvasive marker for tumor burden, but validation is needed. Purpose To evaluate TF as a potential surrogate for tumor burden, assessed at contrast-enhanced CT across diverse metastatic cancers. Methods This retrospective monocentric study included patients with cancer and metastatic disease, with TF results and contemporaneous contrast-enhanced CT performed between January 2021 and January 2023. The total tumor volume (TTV), representing CT tumor burden, was calculated by adding all lesion volumes and was computed by using manually outlined annotations of each lesion on the largest surface of the axial slice. TF greater than 10% was considered high. A training-validation split was applied. Correlations between TF and TTV were assessed using regression models and Spearman correlation coefficients. Receiver operating characteristic curve analysis established the TTV cutoff. The metastatic site, histology type, and TTV were used to predict liquid biopsy contributory status. Results Among 1065 patients (median age, 62 years [IQR: 53, 70]; 537 female), 56 288 lesions were annotated, mostly in the lung (n = 20 334), lymph nodes (n = 11 651), and liver (n = 10 277). A total of 763 liquid biopsies were contributive, 254 were noncontributive, and 48 failed. The training and validation sets included 745 and 320 patients, respectively. TF helped predict TTV with the linear model (R2 = 0.17; ρ = 0.41; P < .001). The TTV and TF categories achieved an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.71, 0.78), with an optimal cutoff of 151 cm3 for TTV and a TF cutoff of 10%. The sensitivity was 57% (204 of 359) and the specificity was 80% (525 of 658). TTV helped predict contributory status, with an AUC of 0.71 (95% CI: 0.67, 0.76) and an optimal cutoff greater than 37 cm3. Liver lesion volumes were significantly associated with a contributory liquid biopsy in the validation cohort. Conclusion While correlated, TF at liquid biopsy did not accurately represent the TTV at CT. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Koh in this issue.

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
Radiology
Radiology 医学-核医学
CiteScore
35.20
自引率
3.00%
发文量
596
审稿时长
3.6 months
期刊介绍: Published regularly since 1923 by the Radiological Society of North America (RSNA), Radiology has long been recognized as the authoritative reference for the most current, clinically relevant and highest quality research in the field of radiology. Each month the journal publishes approximately 240 pages of peer-reviewed original research, authoritative reviews, well-balanced commentary on significant articles, and expert opinion on new techniques and technologies. Radiology publishes cutting edge and impactful imaging research articles in radiology and medical imaging in order to help improve human health.
×
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:481959085
Book学术官方微信