Predicting the nature of pleural effusion in patients with lung adenocarcinoma based on 18F-FDG PET/CT

Abstract Background This study aims to establish a predictive model on the basis of 18F-FDG PET/CT for diagnosing the nature of pleural effusion (PE) in patients with lung adenocarcinoma. Methods Lung adenocarcinoma patients with PE who underwent 18F-FDG PET/CT were collected and divided into training and test cohorts. PET/CT parameters and clinical information in the training cohort were collected to estimate the independent predictive factors of malignant pleural effusion (MPE) and to establish a predictive model. This model was then applied to the test cohort to evaluate the diagnostic efficacy. Results A total of 413 lung adenocarcinoma patients with PE were enrolled in this study, including 245 patients with MPE and 168 patients with benign PE (BPE). The patients were divided into training (289 patients) and test (124 patients) cohorts. CEA, SUVmax of tumor and attachment to the pleura, obstructive atelectasis or pneumonia, SUVmax of pleura, and SUVmax of PE were identified as independent significant factors of MPE and were used to construct a predictive model, which was graphically represented as a nomogram. This predictive model showed good discrimination with the area under the curve (AUC) of 0.970 (95% CI 0.954–0.986) and good calibration. Application of the nomogram in the test cohort still gave good discrimination with AUC of 0.979 (95% CI 0.961–0.998) and good calibration. Decision curve analysis demonstrated that this nomogram was clinically useful. Conclusions Our predictive model based on 18F-FDG PET/CT showed good diagnostic performance for PE, which was helpful to differentiate MPE from BPE in patients with lung adenocarcinoma.

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Value of 18F-FDG PET/CT-based radiomics model to distinguish the growth patterns of early invasive lung adenocarcinoma manifesting as ground-glass opacity nodules

EJNMMI Research ◽

10.1186/s13550-020-00668-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Xiaonan Shao ◽

Rong Niu ◽

Xiaoliang Shao ◽

Zhenxing Jiang ◽

Yuetao Wang

Keyword(s):

Lung Adenocarcinoma ◽

Fdg Pet ◽

Ground Glass Opacity ◽

Growth Patterns ◽

Ground Glass ◽

Pet Ct ◽

18F Fdg ◽

Invasive Lung Adenocarcinoma ◽

Fdg Pet Ct

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Extension study and evaluation of the therapeutic response in a patient with metastatic lung adenocarcinoma using sequential study with 18F-FDG PET-CT and 18F-fluoride PET-CT

Revista Española de Medicina Nuclear e Imagen Molecular (English Edition) ◽

10.1016/j.remnie.2014.03.001 ◽

2015 ◽

Vol 34 (1) ◽

pp. 45-48

Author(s):

M. Moragas ◽

M. Soler ◽

E. Riera ◽

J.R. García

Keyword(s):

Lung Adenocarcinoma ◽

Fdg Pet ◽

Therapeutic Response ◽

Extension Study ◽

Sequential Study ◽

Pet Ct ◽

Metastatic Lung ◽

18F Fdg ◽

Fdg Pet Ct ◽

18F Fluoride

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

Contrast Media & Molecular Imaging ◽

10.1155/2018/1382309 ◽

2018 ◽

Vol 2018 ◽

pp. 1-6 ◽

Cited By ~ 17

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.

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