Multicentric development and evaluation of [ 18 F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy
[en] Purpose To develop machine learning models to predict regional and/or distant recurrence in patients with early-stage non-small cell lung cancer (ES-NSCLC) after stereotactic body radiation therapy (SBRT) using [ 18 F]FDG PET/CT and CT radiomics combined with clinical and dosimetric parameters. Methods We retrospectively collected 464 patients (60% for training and 40% for testing) from University Hospital of Liège and 63 patients from University Hospital of Brest (external testing set) with ES-NSCLC treated with SBRT between 2010 and 2020 and who had undergone pretreatment [ 18 F]FDG PET/CT and planning CT. Radiomic features were extracted using the PyRadiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Clinical, radiomic, and combined models were trained and tested using a neural network approach to predict regional and/ or distant recurrence. Results In the training (n = 273) and testing sets (n = 191 and n = 63), the clinical model achieved moderate performances to predict regional and/or distant recurrence with C-statistics from 0.53 to 0.59 (95% CI, 0.41, 0.67). The radiomic (origi-nal_firstorder_Entropy, original_gldm_LowGrayLevelEmphasis and original_glcm_DifferenceAverage) model achieved higher predictive ability in the training set and kept the same performance in the testing sets, with C-statistics from 0.70 to 0.78 (95% CI, 0.63, 0.88) while the combined model performs moderately well with C-statistics from 0.50 to 0.62 (95% CI, 0.37, 0.69). Conclusion Radiomic features extracted from pre-SBRT analog and digital [ 18 F]FDG PET/CT outperform clinical parameters in the prediction of regional and/or distant recurrence and to discuss an adjuvant systemic treatment in ES-NSCLC. Prospective validation of our models should now be carried out.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Lucia, François ; Département de Physique Médicale > Service médical de médecine nucléaire et imagerie onco ; Belgium ; Belgium
LOUIS, Thomas ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco ; Belgium ; Belgium
Cousin, François ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiodiagnostic ; Belgium
Bourbonne, Vincent; University Hospital, Brest, France > Radiation Oncology Department
Visvikis, Dimitris; LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
MIEVIS, Carole ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiothérapie ; Belgium
Jansen, Nicolas ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de radiothérapie ; Belgium
Duysinx, Bernard ; Centre Hospitalier Universitaire de Liège - CHU > > Service de pneumologie - allergologie ; Belgium
Le Pennec, Romain; Nuclear Medicine Department, University Hospital of Brest, Brest, France
Nebbache, Malik; Radiation Oncology Department, University Hospital, Brest, France
Rehn, Martin; Radiation Oncology Department, University Hospital, Brest, France
Hamya, Mohamed; Radiation Oncology Department, University Hospital, Brest, France
Geier, Margaux; Radiation Oncology Department, University Hospital, Brest, France
Salaun, Pierre-Yves; Service de Médecine Nucléaire, CHRU de Brest, Brest, France
Schick, Ulrike; Department of Radiotherapy, University Hospital of Brest, Brest, France
Hatt, Mathieu; LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
Coucke, Philippe ; Université de Liège - ULiège > Département des sciences cliniques > Radiothérapie ; Belgium
Hustinx, Roland ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco ; Belgium ; Belgium
Lovinfosse, Pierre ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco ; Belgium
Multicentric development and evaluation of [ 18 F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy
Publication date :
2023
Journal title :
European Journal of Nuclear Medicine and Molecular Imaging
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