Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PETCT
المؤلفون المشاركون
Sollini, M.
Kirienko, Margarita
Silvestri, Giorgia
Mognetti, Serena
Voulaz, Emanuele
Antunovic, Lidija
Rossi, Alexia
Antiga, Luca
Chiti, Arturo
المصدر
Contrast Media & Molecular Imaging
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-6، 6ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-10-30
دولة النشر
مصر
عدد الصفحات
6
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1131289
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر