Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PETCT
Joint Authors
Sollini, M.
Kirienko, Margarita
Silvestri, Giorgia
Mognetti, Serena
Voulaz, Emanuele
Antunovic, Lidija
Rossi, Alexia
Antiga, Luca
Chiti, Arturo
Source
Contrast Media & Molecular Imaging
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-10-30
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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 training, validation, and test sets) submitted to staging FDG-PET/CT within 60 days before biopsy or surgery.
TNM system seventh edition was used as reference.
Postprocessing was performed to generate an adequate dataset.
The input of CNNs was a bounding box on both PET and CT images, cropped around the lesion centre.
The results were classified as Correct (concordance between reference and prediction) and Incorrect (discordance between reference and prediction).
Accuracy (Correct/[Correct + Incorrect]), recall (Correctly predicted T3-T4/[all T3-T4]), and specificity (Correctly predicted T1-T2/[all T1-T2]), as commonly defined in deep learning models, were used to evaluate CNN performance.
The area under the curve (AUC) was calculated for the final model.
Results.
The algorithm, composed of two networks (a “feature extractor” and a “classifier”), developed and tested achieved an accuracy, recall, specificity, and AUC of 87%, 69%, 69%, and 0.83; 86%, 77%, 70%, and 0.73; and 90%, 47%, 67%, and 0.68 in the training, validation, and test sets, respectively.
Conclusion.
We obtained proof of concept that CNNs can be used as a tool to assist in the staging of patients affected by lung cancer.
American Psychological Association (APA)
Kirienko, Margarita& Sollini, M.& Silvestri, Giorgia& Mognetti, Serena& Voulaz, Emanuele& Antunovic, Lidija…[et al.]. 2018. Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PETCT. Contrast Media & Molecular Imaging،Vol. 2018, no. 2018, pp.1-6.
https://search.emarefa.net/detail/BIM-1131289
Modern Language Association (MLA)
Kirienko, Margarita…[et al.]. Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PETCT. Contrast Media & Molecular Imaging No. 2018 (2018), pp.1-6.
https://search.emarefa.net/detail/BIM-1131289
American Medical Association (AMA)
Kirienko, Margarita& Sollini, M.& Silvestri, Giorgia& Mognetti, Serena& Voulaz, Emanuele& Antunovic, Lidija…[et al.]. Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PETCT. Contrast Media & Molecular Imaging. 2018. Vol. 2018, no. 2018, pp.1-6.
https://search.emarefa.net/detail/BIM-1131289
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131289