PET/CT in the Staging of Non-Small Cell Lung Cancer

QJM ◽  
2021 ◽  
Vol 114 (Supplement_1) ◽  
Author(s):  
Nada Mohammed Farid Hassan Ghoneim ◽  
Remon Zaher Elia ◽  
Aliaa Sayed Sheha
Keyword(s):  
Treatment Options ◽  
University Hospitals ◽  
Lung Cancers ◽  
Tnm System ◽  
Imaging Tool ◽  
Pet Ct ◽  
Malignant Lesions ◽  
A Prospective Study ◽  
Fdg Pet Ct ◽  
Nsclc Patients

Abstract Background NSCLC accounts for about 80% of all lung cancers. The current criteria for its staging is based on the TNM system that determines treatment options and predicts survival rate in patients. Objective To evaluate the diagnostic accuracy of 18F-FDG PET/CT in staging of NSCLC patients Methods A prospective study, conducted at Ain Shams University hospitals on pathologically proven patients of NSCLC, the patients were investigated using CT and PET/CT in the period between October 2018 till end of June 2019. Results A total of 40 patients were evaluated with the age ranging from 37 to 77 years old, whether the mean was 55.63 years (SD ± 10.29). There were 31 male cases and 9 female cases. When we compared CECT against PET-CT for staging, PET-CT helped upstage disease in 10 of 40 patients (25%) and downstage in 3 of 40 patients (7.5%). Conclusion PET/CT is a useful imaging tool in initial staging of the newely diagnosed patients with NSCLC. It is better thаn СT alone for detection of malignant lesions for accurate staging. It can change the strategy of treatment according to its findings

Enhancing 18F-FDG-PET/CT analysis in lung cancer patients

2015 ◽  
Vol 54 (06) ◽  
pp. 247-254 ◽  
Author(s):  
A. Kapfhammer ◽  
T. Winkens ◽  
T. Lesser ◽  
A. Reissig ◽  
M. Steinert ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
Chest Wall ◽  
Fdg Pet ◽  
Ct Image ◽  
Lung Cancers ◽  
Pet Ct ◽  
Peripheral Lung ◽  
Fdg Pet Ct ◽  
Tumour Infiltration

SummaryAim: To retrospectively evaluate the feasibility and value of CT-CT image fusion to assess the shift of peripheral lung cancers with/-out chest wall infiltration, comparing computed tomography acquisitions in shallow-breathing (SB-CT) and deep-inspiration breath-hold (DIBH-CT) in patients undergoing FDG-PET/ CT for lung cancer staging. Methods: Image fusion of SB-CT and DIBH-CT was performed with a multimodal workstation used for nuclear medicine fusion imaging. The distance of intrathoracic landmarks and the positional shift of tumours were measured using semitransparent overlay of both CT series. Statistical analyses were adjusted for confounders of tumour infiltration. Cutoff levels were calculated for prediction of no-/infiltration. Results: Lateral pleural recessus and diaphragm showed the largest respiratory excursions. Infiltrating lung cancers showed more limited respiratory shifts than non-infiltrating tumours. A large respiratory tumour-motility accurately predicted non-infiltration. However, the tumour shifts were limited and variable, limiting the accuracy of prediction. Conclusion: This pilot fusion study proved feasible and allowed a simple analysis of the respiratory shifts of peripheral lung tumours using CT-CT image fusion in a PET/CT setting. The calculated cutoffs were useful in predicting the exclusion of chest wall infiltration but did not accurately predict tumour infiltration. This method can provide additional qualitative information in patients with lung cancers with contact to the chest wall but unclear CT evidence of infiltration undergoing PET/CT without the need of additional investigations. Considering the small sample size investigated, further studies are necessary to verify the obtained results.

scholarly journals Differential and staging diagnosis for NSCLC patients With [68Ga] Ga-DOTA-FAPI-04 and 2-[18F]FDG PET/CT

2021 ◽  
Vol 96-97 ◽  
pp. S46-S47
Author(s):  
Xin Zhou ◽  
Xiaoxia Xu ◽  
Shuailiang Wang ◽  
Hua Zhu ◽  
Zhi Yang ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct ◽  
Nsclc Patients

Cell-free DNA concentration and fragment size fraction correlate with FDG PET/CT-derived parameters in NSCLC patients

2021 ◽  
Author(s):  
JM. González de Aledo-Castillo ◽  
S. Casanueva-Eliceiry ◽  
A. Soler-Perromat ◽  
D. Fuster ◽  
V. Pastor ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Fragment Size ◽  
Size Fraction ◽  
Cell Free Dna ◽  
Dna Concentration ◽  
Free Dna ◽  
Pet Ct ◽  
Fdg Pet Ct ◽  
Nsclc Patients

scholarly journals FDG-PET/CT in high-risk primary breast cancer—a prospective study of stage migration and clinical impact

2020 ◽  
Author(s):  
Marianne Vogsen ◽  
Jeanette Dupont Jensen ◽  
Ivar Yannick Christensen ◽  
Oke Gerke ◽  
Anne Marie Bak Jylling ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Prospective Study ◽  
Fdg Pet ◽  
Primary Breast Cancer ◽  
Clinical Impact ◽  
Stage Migration ◽  
Pet Ct ◽  
A Prospective Study ◽  
Fdg Pet Ct

P.10.7 THE VALUE OF (18)FDG-PET/CT IN PATIENTS WITH RESECTABLE PANCREATIC CANCER: A PROSPECTIVE STUDY

2013 ◽  
Vol 45 ◽  
pp. S160 ◽  
Author(s):  
S. Crippa ◽  
M. Salgarello ◽  
S. Laiti ◽  
S. Partelli ◽  
C. Zardini ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Prospective Study ◽  
Fdg Pet ◽  
Resectable Pancreatic Cancer ◽  
Pet Ct ◽  
A Prospective Study ◽  
Fdg 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.

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