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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:14
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.
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