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
益安完成签到,获得积分10
1秒前
TaoTaooooII完成签到,获得积分10
1秒前
rsq完成签到,获得积分10
1秒前
1秒前
麦冬粑粑完成签到,获得积分10
1秒前
Adzuki0812完成签到,获得积分10
2秒前
Kriten完成签到,获得积分10
2秒前
知行合一完成签到,获得积分10
2秒前
3秒前
姜姜完成签到,获得积分10
3秒前
英俊的铭应助霸气的怜珊采纳,获得10
3秒前
爆杀小白鼠完成签到,获得积分10
4秒前
量子星尘发布了新的文献求助10
5秒前
超哥完成签到,获得积分10
5秒前
飞飞完成签到,获得积分10
5秒前
qqwxp完成签到,获得积分10
5秒前
5秒前
婷刘完成签到,获得积分10
5秒前
谦让碧菡完成签到,获得积分10
6秒前
guohuameike完成签到,获得积分10
6秒前
顺利灭绝完成签到,获得积分20
6秒前
单身的乐瑶完成签到,获得积分10
7秒前
Moonchild发布了新的文献求助10
7秒前
萌~Lucky完成签到,获得积分10
8秒前
喜喜完成签到,获得积分20
8秒前
9秒前
9秒前
zgrmws应助科研通管家采纳,获得10
9秒前
顾矜应助科研通管家采纳,获得10
9秒前
fff应助科研通管家采纳,获得10
9秒前
Tengami应助科研通管家采纳,获得10
9秒前
幸福大碗完成签到,获得积分10
9秒前
zgrmws应助科研通管家采纳,获得10
9秒前
传奇3应助科研通管家采纳,获得10
10秒前
Owen应助科研通管家采纳,获得10
10秒前
bkagyin应助科研通管家采纳,获得10
10秒前
Criminology34应助科研通管家采纳,获得10
10秒前
十一完成签到,获得积分10
10秒前
10秒前
laber应助guest采纳,获得50
10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 8000
Building Quantum Computers 800
Translanguaging in Action in English-Medium Classrooms: A Resource Book for Teachers 700
Natural Product Extraction: Principles and Applications 500
Exosomes Pipeline Insight, 2025 500
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 500
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 5664939
求助须知:如何正确求助?哪些是违规求助? 4873377
关于积分的说明 15110105
捐赠科研通 4823973
什么是DOI,文献DOI怎么找? 2582614
邀请新用户注册赠送积分活动 1536518
关于科研通互助平台的介绍 1495130