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
刚刚
铠甲勇士完成签到,获得积分20
刚刚
刚刚
个性的夜柳完成签到,获得积分10
刚刚
刚刚
李健应助留胡子的香露采纳,获得10
1秒前
2秒前
2秒前
鄂枷旭完成签到,获得积分10
2秒前
在水一方应助咕噜咕噜采纳,获得10
3秒前
3秒前
3秒前
闪闪凝冬完成签到,获得积分10
3秒前
今后应助FLZLC采纳,获得10
4秒前
4秒前
popowannaslp发布了新的文献求助10
4秒前
科研大佬完成签到,获得积分10
4秒前
fpneal发布了新的文献求助10
5秒前
爆米花应助星辰采纳,获得10
5秒前
5秒前
5秒前
米娅完成签到,获得积分10
5秒前
5秒前
123xxy完成签到,获得积分10
5秒前
铠甲勇士发布了新的文献求助10
5秒前
BeSideWorld发布了新的文献求助10
5秒前
搜集达人应助任性的尔容采纳,获得10
6秒前
ayingjiang发布了新的文献求助10
6秒前
量子星尘发布了新的文献求助10
6秒前
SciGPT应助方法采纳,获得10
7秒前
LLL发布了新的文献求助10
7秒前
7秒前
ruen发布了新的文献求助30
8秒前
嗯嗯嗯嗯嗯完成签到 ,获得积分10
8秒前
霸王宝宝蛋完成签到,获得积分20
9秒前
9秒前
yyyy发布了新的文献求助10
10秒前
10秒前
芳芳发布了新的文献求助10
10秒前
王钟萱发布了新的文献求助10
11秒前
高分求助中
Theoretical Modelling of Unbonded Flexible Pipe Cross-Sections 10000
(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
《药学类医疗服务价格项目立项指南(征求意见稿)》 880
花の香りの秘密―遺伝子情報から機能性まで 800
3rd Edition Group Dynamics in Exercise and Sport Psychology New Perspectives Edited By Mark R. Beauchamp, Mark Eys Copyright 2025 600
1st Edition Sports Rehabilitation and Training Multidisciplinary Perspectives By Richard Moss, Adam Gledhill 600
Digital and Social Media Marketing 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 5619979
求助须知:如何正确求助?哪些是违规求助? 4704479
关于积分的说明 14928024
捐赠科研通 4760640
什么是DOI,文献DOI怎么找? 2550712
邀请新用户注册赠送积分活动 1513458
关于科研通互助平台的介绍 1474498