18F-FDG PET/CT; Cervical cancer; Digital 18F-FDG PET/CT; MRI; Para-aortic lymph node; Radiomics; Radiology, Nuclear Medicine and imaging; General Medicine
[en] [en] 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.
Radiology, nuclear medicine & imaging
Author, co-author :
Lucia, François ; Centre Hospitalier Universitaire de Liège - CHU ; Radiation Oncology Department, University Hospital, Brest, France. firstname.lastname@example.org ; LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France. email@example.com
Bourbonne, Vincent; Radiation Oncology Department, University Hospital, Brest, France ; LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
PLEYERS, Clémence ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiothérapie
Dupré, Pierre-François; Department of Gynecology and Surgery, University Hospital, Brest, France
Miranda, Omar; Radiation Oncology Department, University Hospital, Brest, France
Visvikis, Dimitris; LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
Pradier, Olivier; Radiation Oncology Department, University Hospital, Brest, France ; LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
Abgral, Ronan; Nuclear Medicine Department, University Hospital, Brest, France ; EA GETBO 3878, IFR 148, University of Brest, UBO, Brest, France
Mervoyer, Augustin; Department of Radiation Oncology, Institut de Cancérologie de l'Ouest Centre René Gauducheau, Saint Herblain, France
Classe, Jean-Marc; Department of Surgical Oncology, Institut de Cancérologie de l'Ouest Centre René Gauducheau, Saint Herblain, France
Rousseau, Caroline; Université de Nantes, CNRS, Inserm, CRCINA, F-44000, Nantes, France ; ICO René Gauducheau, F-44800, Saint-Herblain, France
Vos, Wim; Radiomics SA, Liège, Belgium
HERMESSE, Johanne ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiothérapie
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