Classification of Lung Cancer Histology by Gold Nanoparticle Sensors

2017 ◽  
pp. 713-735
Author(s):  
Orna Barash ◽  
Nir Peled ◽  
Ulrike Tisch ◽  
Paul A. Bunn ◽  
Fred R. Hirsch ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Gold Nanoparticle

scholarly journals Classification of lung cancer histology by gold nanoparticle sensors

2012 ◽  
Vol 8 (5) ◽  
pp. 580-589 ◽  
Author(s):  
Orna Barash ◽  
Nir Peled ◽  
Ulrike Tisch ◽  
Paul A. Bunn ◽  
Fred R. Hirsch ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Gold Nanoparticle

Segmentation and Classification of Lung Cancer: A Review

2016 ◽  
Vol 16 (2) ◽  
pp. 82-99 ◽  
Author(s):  
Javeria Amin ◽  
Muhammad Sharif ◽  
Mussarat Yasmin
Keyword(s):  
Lung Cancer

scholarly journals Classification of Non-Small Cell Lung Cancer Based on Copy Number Alterations

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e88300 ◽  
Author(s):  
Bi-Qing Li ◽  
Jin You ◽  
Tao Huang ◽  
Yu-Dong Cai
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Copy Number ◽  
Small Cell ◽  
Copy Number Alterations ◽  
Small Cell Lung

scholarly journals The IASLC Lung Cancer Staging Project: The New Database to Inform the Eighth Edition of the TNM Classification of Lung Cancer

2014 ◽  
Vol 9 (11) ◽  
pp. 1618-1624 ◽  
Author(s):  
Ramón Rami-Porta ◽  
Vanessa Bolejack ◽  
Dorothy J. Giroux ◽  
Kari Chansky ◽  
John Crowley ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tnm Classification ◽  
Cancer Staging ◽  
Eighth Edition

scholarly journals Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Margarita Kirienko ◽  
Martina Sollini ◽  
Giorgia Silvestri ◽  
Serena Mognetti ◽  
Emanuele Voulaz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Fdg Pet ◽  
Area Under The Curve ◽  
Ct Images ◽  
Tnm System ◽  
Positron Emission ◽  
Pet Ct ◽  
Test Sets ◽  
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 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.

Sign in / Sign up

Export Citation Format

Share Document