Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer

医学 无线电技术 宫颈癌 淋巴结 放射科 放化疗 磁共振成像 逻辑回归 核医学 癌症 放射治疗 内科学
作者
François Lucia,Vincent Bourbonne,Clémence Pleyers,Pierre‐François Dupré,O. Miranda,Dimitris Visvikis,Olivier Pradier,Ronan Abgral,A. Mervoyer,Jean-Marc Classe,Caroline Rousseau,Wim Vos,Johanne Hermesse,Christine Gennigens,Marjolein De Cuypere,Frédéric Kridelka,Ulrike Schick,Mathieu Hatt,Roland Hustinx,Pierre Lovinfosse
出处
期刊:European Journal of Nuclear Medicine and Molecular Imaging [Springer Nature]
卷期号:50 (8): 2514-2528 被引量:20
标识
DOI:10.1007/s00259-023-06180-w
摘要

To develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using 18F-FDG PET/CT and MRI radiomics combined with clinical parameters. We retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital 18F-FDG PET/CT, pelvic MRI and surgical PALN staging. Only primary tumor volumes were delineated. Radiomics features were extracted using the Radiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Different prediction models were trained using a neural network approach with either clinical, radiomics or combined models. They were then evaluated on the testing and external validation sets and compared. In the training set (n = 102), the clinical model achieved a good prediction of the risk of PALN involvement with a C-statistic of 0.80 (95% CI 0.71, 0.87). However, it performed in the testing (n = 76) and external testing sets (n = 30 and n = 31) with C-statistics of only 0.57 to 0.67 (95% CI 0.36, 0.83). The ComBat-radiomic (GLDZM_HISDE_PET_FBN64 and Shape_maxDiameter2D3_PET_FBW0.25) and ComBat-combined (FIGO 2018 and same radiomics features) models achieved very high predictive ability in the training set and both models kept the same performance in the testing sets, with C-statistics from 0.88 to 0.96 (95% CI 0.76, 1.00) and 0.85 to 0.92 (95% CI 0.75, 0.99), respectively. Radiomic features extracted from pre-CRT analog and digital 18F-FDG PET/CT outperform clinical parameters in the decision to perform a para-aortic node staging or an extended field irradiation to PALN. Prospective validation of our models should now be carried out.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
邓布利多发布了新的文献求助10
1秒前
量子星尘发布了新的文献求助10
2秒前
caiia发布了新的文献求助10
2秒前
杨军发布了新的文献求助30
2秒前
HHW发布了新的文献求助10
4秒前
三岁半完成签到,获得积分10
8秒前
眼睛大书兰完成签到,获得积分20
9秒前
xiaowang发布了新的文献求助10
10秒前
10秒前
11秒前
上好佳完成签到,获得积分10
12秒前
12秒前
13秒前
李健应助眼睛大书兰采纳,获得30
13秒前
小二郎应助文艺的清炎采纳,获得10
14秒前
xinghui应助gcy采纳,获得10
14秒前
可鹿丽完成签到,获得积分10
15秒前
ElviraHuang发布了新的文献求助10
15秒前
Lyra发布了新的文献求助10
16秒前
上官若男应助辞树采纳,获得10
16秒前
16秒前
17秒前
18秒前
19秒前
19秒前
Soda8513发布了新的文献求助10
20秒前
21秒前
21秒前
科研通AI6应助优雅友菱采纳,获得10
21秒前
21秒前
SJJ应助xiaowang采纳,获得30
21秒前
晚湖发布了新的文献求助10
22秒前
jack_kunn发布了新的文献求助10
22秒前
23秒前
轶Y发布了新的文献求助10
23秒前
23秒前
涂文波完成签到,获得积分10
23秒前
温柔柜子发布了新的文献求助10
24秒前
qqaeao发布了新的文献求助10
24秒前
25秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Binary Alloy Phase Diagrams, 2nd Edition 6000
Encyclopedia of Reproduction Third Edition 3000
Comprehensive Methanol Science Production, Applications, and Emerging Technologies 2000
化妆品原料学 1000
The Political Psychology of Citizens in Rising China 800
1st Edition Sports Rehabilitation and Training Multidisciplinary Perspectives By Richard Moss, Adam Gledhill 600
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5637646
求助须知:如何正确求助?哪些是违规求助? 4743795
关于积分的说明 14999969
捐赠科研通 4795812
什么是DOI,文献DOI怎么找? 2562208
邀请新用户注册赠送积分活动 1521661
关于科研通互助平台的介绍 1481646