Cargando…
Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT
AIM: To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images. METHODS: We retrospectively selected a cohort of 472 patients (divided in the tra...
Autores principales: | Kirienko, Margarita, Sollini, Martina, Silvestri, Giorgia, Mognetti, Serena, Voulaz, Emanuele, Antunovic, Lidija, Rossi, Alexia, Antiga, Luca, Chiti, Arturo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6232785/ https://www.ncbi.nlm.nih.gov/pubmed/30510492 http://dx.doi.org/10.1155/2018/1382309 |
Ejemplares similares
-
Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
por: Sollini, Martina, et al.
Publicado: (2019) -
The Development of an Intelligent Agent to Detect and Non-Invasively Characterize Lung Lesions on CT Scans: Ready for the “Real World”?
por: Sollini, Martina, et al.
Publicado: (2023) -
Imaging Correlates between Headache and Breast Cancer: An [(18)F]FDG PET Study
por: Antunovic, Lidija, et al.
Publicado: (2023) -
PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology
por: Sollini, M., et al.
Publicado: (2017) -
COVID-19 vaccination, implications for PET/CT image interpretation and future perspectives
por: Kirienko, Margarita, et al.
Publicado: (2022)