Objective: Lung cancer, along with various other cancers, is characterized by increased glucose metabolism. The maximum standardized uptake value (SUVmax), derived from positron emission tomography-computed tomography (PET-CT), serves as an indicator of glucose metabolic activity in tumor lesions. This study aimed to evaluate the correlation between body mass index (BMI) and SUVmax in individuals with lung cancer.
Methods: This study included 41 patients with lung cancer, who were divided into two groups: Group 1 (n = 21), with a BMI greater than 22.4, and Group 2 (n = 20), with a BMI less than 22.4. All participants underwent 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET-CT) imaging. The SUVmax was calculated by manually delineating the regions of interest. A t-test was performed to assess whether the differences in SUVmax between the two groups were statistically significant.
Results: The mean SUVmax for Group 1 was 11.20 ± 5.45, while for Group 2 it was 10.65 ± 5.96. Although the mean SUVmax was higher in Group 1 compared to Group 2, the difference between the groups was not statistically significant (P = 0.757).
Conclusion: The findings indicate a non-significant difference in glucose metabolism in lung cancer lesions between patients with different BMI levels. These results offer valuable insights into the metabolic characteristics of lung cancer and contribute to a deeper understanding of its pathophysiology.
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http://dx.doi.org/10.1007/s12672-024-01539-3 | DOI Listing |
Lung Cancer
January 2025
Dept. of Medical Oncology, Princess Margaret Cancer Center, Toronto, ON, Canada.
Background: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with advanced lung cancer (aLC). We assessed the external validity of our NLP-extracted data by comparing our findings to those reported in the literature.
View Article and Find Full Text PDFLung Cancer
January 2025
Internal Medicine III, Wakayama Medical University, Wakayama, Japan.
Objectives: The lack of definitive biomarkers presents a significant challenge for chemo-immunotherapy in extensive-stage small-cell lung cancer (ES-SCLC). We aimed to identify key genes associated with chemo-immunotherapy efficacy in ES-SCLC through comprehensive gene expression analysis using machine learning (ML).
Methods: A prospective multicenter cohort of patients with ES-SCLC who received first-line chemo-immunotherapy was analyzed.
Clin Nucl Med
December 2024
From the Department of Nuclear Medicine, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, Guangdong, China.
A 53-year-old woman was found to have a soft tissue mass in the right lower lung lobe on chest CT, raising suspicion of lung cancer. For staging, 18F-FDG PET/CT was performed, which demonstrated intense tracer uptake in the mass (SUVmax, 14.6).
View Article and Find Full Text PDFChin Med J (Engl)
December 2024
Department of Anesthesiology, The Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan 646000, China.
PLoS One
January 2025
Department of Oncology, Peking University First Hospital, Taiyuan Hospital, Taiyuan, Shanxi, China.
This work established the cytotoxic, antioxidant and anticancer effects of copper nanoparticles (CuNPs) manufactured with fennel extract, especially on non-small cell lung cancer (NSCLC) as well. CuNPs caused cytotoxicity in a dose-dependent manner for two NSCLC cell lines, A549 and H1650. At 100 μg/ml, CuNPs reduced cell viability to 70% in A549 cells and 65% in H1650 cells.
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