Literature DB >> 30422775

Role of FDG PET/CT in the Eighth Edition of TNM Staging of Non-Small Cell Lung Cancer.

Asha Kandathil1, Fernando U Kay1, Yasmeen M Butt1, Jason W Wachsmann1, Rathan M Subramaniam1.   

Abstract

Lung cancer is the leading cause of cancer-related mortality in the United States, and accurate staging plays a vital role in determining prognosis and treatment. The recently revised eighth edition of the TNM staging system for lung cancer defines new T and M descriptors and updates stage groupings on the basis of substantial differences in survival. There are new T descriptors that are based on the findings at histopathologic examination, and T descriptors are reassigned on the basis of tumor size and extent. No changes were made to the N descriptors in the eighth edition of the TNM staging of lung cancer, because the four N categories that are based on the location of the diseased nodes can be used to consistently predict prognosis. The eighth edition includes a new M1b descriptor for patients with a single extrathoracic metastatic lesion in a single organ (M1b), because they have better survival and different treatment options, compared with those with multiple extrathoracic lesions (M1c). Examination with fluorine 18 fluorodeoxyglucose (FDG) PET/CT is the standard of care and is an integral part of the clinical staging of patients with lung cancer. To provide the treating physicians with accurate staging information, radiologists and nuclear medicine physicians should be aware of the updated classification system and should be cognizant of the site-specific strengths and limitations of FDG PET/CT. In this article, the eighth edition of the TNM staging system is reviewed, as well as the role of FDG PET/CT in the staging of non-small cell lung carcinoma. ©RSNA, 2018.

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Year:  2018        PMID: 30422775     DOI: 10.1148/rg.2018180060

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  21 in total

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2.  18F-FDG PET/CT and circulating tumor cells in treatment-naive patients with non-small-cell lung cancer.

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3.  Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules.

Authors:  Michael N Kammer; Dhairya A Lakhani; Aneri B Balar; Sanja L Antic; Amanda K Kussrow; Rebekah L Webster; Shayan Mahapatra; Udaykamal Barad; Chirayu Shah; Thomas Atwater; Brenda Diergaarde; Jun Qian; Alexander Kaizer; Melissa New; Erin Hirsch; William J Feser; Jolene Strong; Matthew Rioth; York E Miller; Yoganand Balagurunathan; Dianna J Rowe; Sherif Helmey; Sheau-Chiann Chen; Joseph Bauza; Stephen A Deppen; Kim Sandler; Fabien Maldonado; Avrum Spira; Ehab Billatos; Matthew B Schabath; Robert J Gillies; David O Wilson; Ronald C Walker; Bennett Landman; Heidi Chen; Eric L Grogan; Anna E Barón; Darryl J Bornhop; Pierre P Massion
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4.  Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.

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5.  Interventions of Advanced Lung Cancer Patient Receiving Chemotherapy by Computed Tomography Image Information Data Analysis-Based Soothing Care Plans.

Authors:  Juan Wang; Shuangping Lu; Qundan Zhang
Journal:  Comput Math Methods Med       Date:  2022-06-09       Impact factor: 2.809

6.  Comparison of recurrence risk between patients with clinically node-positive and -negative stage I non-small cell lung cancer following surgery: A propensity score matching analysis.

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Review 7.  Modern radiopharmaceuticals for lung cancer imaging with positron emission tomography/computed tomography scan: A systematic review.

Authors:  Athanasios S Theodoropoulos; Ioannis Gkiozos; Georgios Kontopyrgias; Adrianni Charpidou; Elias Kotteas; George Kyrgias; Maria Tolia
Journal:  SAGE Open Med       Date:  2020-09-28

8.  Total-body PET/CT using half-dose FDG and compared with conventional PET/CT using full-dose FDG in lung cancer.

Authors:  Hui Tan; Xiuli Sui; Hongyan Yin; Haojun Yu; Yusen Gu; Shuguang Chen; Pengcheng Hu; Wujian Mao; Hongcheng Shi
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-11-27       Impact factor: 9.236

9.  Upregulated circRNA ARHGAP10 Predicts an Unfavorable Prognosis in NSCLC through Regulation of the miR-150-5p/GLUT-1 Axis.

Authors:  Mingming Jin; Chunzi Shi; Chen Yang; Jianjun Liu; Gang Huang
Journal:  Mol Ther Nucleic Acids       Date:  2019-08-21       Impact factor: 8.886

10.  Evaluation of an AI-Powered Lung Nodule Algorithm for Detection and 3D Segmentation of Primary Lung Tumors.

Authors:  Thomas Weikert; Tugba Akinci D'Antonoli; Jens Bremerich; Bram Stieltjes; Gregor Sommer; Alexander W Sauter
Journal:  Contrast Media Mol Imaging       Date:  2019-07-01       Impact factor: 3.161

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