SU-E-J-203: Texture Analysis of 3D and 4D PET/CT Images of Lung Cancer

2013 ◽  
Vol 40 (6Part10) ◽  
pp. 198-198
Author(s):  
J Oliver ◽  
M Budzevich ◽  
G Zhang ◽  
K Latifi ◽  
C Kuykendall ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Texture Analysis ◽  
Ct Images ◽  
Pet Ct

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.

Texture Analysis on [18F]FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types

2019 ◽  
Vol 21 (6) ◽  
pp. 1200-1209 ◽  
Author(s):  
Francesco Bianconi ◽  
Isabella Palumbo ◽  
Mario Luca Fravolini ◽  
Rita Chiari ◽  
Matteo Minestrini ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Texture Analysis ◽  
Fdg Pet ◽  
Small Cell ◽  
Small Cell Lung ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct ◽  
Ct Features

scholarly journals Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study

2019 ◽  
Vol 92 (1101) ◽  
pp. 20190286 ◽  
Author(s):  
Emine Acar ◽  
Asım Leblebici ◽  
Berat Ender Ellidokuz ◽  
Yasemin Başbınar ◽  
Gamze Çapa Kaya
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Texture Analysis ◽  
Area Under The Curve ◽  
Ct Images ◽  
Metastatic Lesions ◽  
Psma Pet ◽  
Pet Ct ◽  
Sclerotic Lesions ◽  
Psma Expression

Objective:Using CT texture analysis and machine learning methods, this study aims to distinguish the lesions imaged via 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/CT as metastatic and completely responded in patients with known bone metastasis and who were previously treated.Methods:We retrospectively reviewed the 68Ga-PSMA PET/CT images of 75 patients after treatment, who were previously diagnosed with prostate cancer and had known bone metastasis. A texture analysis was performed on the metastatic lesions showing PSMA expression and completely responded sclerotic lesions without PSMA expression through CT images. Textural features were compared in two groups. Thus, the distinction of metastasis/completely responded lesions and the most effective parameters in this issue were determined by using various methods [decision tree, discriminant analysis, support vector machine (SVM), k-nearest neighbor (KNN), ensemble classifier] in machine learning.Results:In 28 of the 35 texture analysis findings, there was a statistically significant difference between the two groups. The Weighted KNN method had the highest accuracy and area under the curve, has been chosen as the best model. The weighted KNN algorithm was succeeded to differentiate sclerotic lesion from metastasis or completely responded lesions with 0.76 area under the curve. GLZLM_SZHGE and histogram-based kurtosis were found to be the most important parameters in differentiating metastatic and completely responded sclerotic lesions.Conclusions:Metastatic lesions and completely responded sclerosis areas in CT images, as determined by 68Ga-PSMA PET, could be distinguished with good accuracy using texture analysis and machine learning (Weighted KNN algorithm) in prostate cancer.Advances in knowledge:Our findings suggest that, with the use of newly emerging software, CT imaging can contribute to identifying the metastatic lesions in prostate cancer.

scholarly journals Artificial Neural Network Based Lung Cancer Detection for PET/CT Images

2017 ◽  
Vol 10 (42) ◽  
pp. 1-13 ◽  
Author(s):  
Punithavathy Kannuswami ◽  
Sumathi Poobal ◽  
M. M. Ramya ◽  
◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Cancer Detection ◽  
Ct Images ◽  
Pet Ct ◽  
Artificial Neural ◽  
Lung Cancer Detection

scholarly journals Co-Learning Feature Fusion Maps From PET-CT Images of Lung Cancer

2020 ◽  
Vol 39 (1) ◽  
pp. 204-217 ◽  
Author(s):  
Ashnil Kumar ◽  
Michael Fulham ◽  
Dagan Feng ◽  
Jinman Kim
Keyword(s):  
Lung Cancer ◽  
Feature Fusion ◽  
Ct Images ◽  
Pet Ct

scholarly journals FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy

2016 ◽  
Vol 43 (8) ◽  
pp. 1453-1460 ◽  
Author(s):  
Pierre Lovinfosse ◽  
Zsolt Levente Janvary ◽  
Philippe Coucke ◽  
Sébastien Jodogne ◽  
Claire Bernard ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Radiation Therapy ◽  
Texture Analysis ◽  
Fdg Pet ◽  
Stereotactic Body Radiation Therapy ◽  
Stereotactic Body Radiation ◽  
Pet Ct ◽  
Ct Texture Analysis ◽  
Fdg Pet Ct
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