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.

Autor: Lucia, François, Bourbonne, Vincent, Pleyers, Clémence, Dupré, Pierre-François, Miranda, Omar, Visvikis, Dimitris, Pradier, Olivier, Abgral, Ronan, Mervoyer, Augustin, Classe, Jean-Marc, Rousseau, Caroline, Vos, Wim, Hermesse, Johanne, Gennigens, Christine, De Cuypere, Marjolein, Kridelka, Frédéric, Schick, Ulrike, Hatt, Mathieu, Hustinx, Roland, Lovinfosse, Pierre
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Zdroj: European Journal of Nuclear Medicine & Molecular Imaging; Jul2023, Vol. 50 Issue 8, p2514-2528, 15p, 6 Diagrams, 5 Charts, 1 Graph
Abstrakt: Purpose: 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. Methods: 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. Results: 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. Conclusions: 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. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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