Objective: To evaluate the role of (18)F-FDG PET/CT in characterizating solitary pulmonary nodule (SPN) and bone lesions.
Methods: 105 patients with a SPN smaller than 30 mm in axial diameter were recruited for this study. PET/CT images were obtained 60 min after intravenous injection of (18)F-FDG. Logistic regression analysis was performed to identify clinical predictors of SPN malignancy including age, sex, smoking history, malignant history, family history, symptoms, size, location, CT appearances, (18)F-FDG uptake, and to develop a clinical prediction model to estimate the probability of malignancy in the patients with SPN. The model fit was evaluated and the area under curve (AUC) of receiver operating characteristic (ROC) was used to evaluate the power of the model.
Results: The logistic regression analysis indicated that male, a positive smoking history, older age, larger nodule diameter, nodule with specula and nodule with high (18)F-FDG uptake were more likely to have malignant SPN. The clinical prediction model is described by the following equation: Logit(P) = -8.722 + 2.448 (gender) + 2.023(smoking) + 0. 851(age) + 1.057 (diameter) + 2.432 (spiculation) + 1.502 (FDG uptake). The AUC of the model was 0.892 (95% confidence interval 0.817 - 0.941). The prediction model had high accuracy in predicting malignant SPN, with 90.2%, 84.1 % and 87.6% sensitivity, specificity and accuracy respectively when the cut off value was set at 0.67.
Conclusion: The prediction model is valid in predicting the probability of malignant SPN.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Complementary cognitive roles for D2-MSNs and D1-MSNs during interval timing.
Elife
January 2025
Department of Neurology, University of Iowa, Iowa City, United States.
The role of striatal pathways in cognitive processing is unclear. We studied dorsomedial striatal cognitive processing during interval timing, an elementary cognitive task that requires mice to estimate intervals of several seconds and involves working memory for temporal rules as well as attention to the passage of time. We harnessed optogenetic tagging to record from striatal D2-dopamine receptor-expressing medium spiny neurons (D2-MSNs) in the indirect pathway and from D1-dopamine receptor-expressing MSNs (D1-MSNs) in the direct pathway.
View Article and Find Full Text PDFCOL1A1, COL1A2, CHN1, and FN1 Promote Tumorogenesis and Act as Markers of Diagnosis and Survival in Gastric Cancer Patients.
Curr Pharm Biotechnol
January 2025
Department of Intensive Care Unit, Affiliated Hospital of Guangdong Medical University, 524000 Zhanjiang, China.
Objectives: This study aimed to comprehensively investigate the molecular landscape of gastric cancer (GC) by integrating various bioinformatics tools and experimental validations.
Methodology: GSE79973 dataset, limma package, STRING, UALCAN, GEPIA, OncoDB, cBioPortal, DAVID, TISIDB, Gene Set Cancer Analysis (GSCA), tissue samples, RT-qPCR, and cell proliferation assay were employed in this study.
Results: Analysis of the GSE79973 dataset identified 300 differentially expressed genes (DEGs), from which COL1A1, COL1A2, CHN1, and FN1 emerged as pivotal hub genes using protein-protein interaction network analysis.
Oral Microbiota Associated with Clinical Efficacy of Ustekinumab in Crohn's Disease.
Endocr Metab Immune Disord Drug Targets
January 2025
Department of Stomatology, The Affiliated Huaian No.1 People's Hospital, Nanjing Medical University, No.1 Huanghe West Road, Huaian, 223300, Jiangsu Province, China.
Background: Crohn's Disease (CD) is a chronic inflammatory gastrointestinal disease. Ustekinumab (UST) has been utilized as a therapeutic option for CD patients. However, approximately 40-60% of patients exhibit an inadequate response to UST.
View Article and Find Full Text PDFMachine-Learning Based Computed Tomography Radiomics Nomgram For Predicting Perineural Invasion In Gastric Cancer.
Curr Med Imaging
January 2025
Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
Objective: The aim of this study was to develop and validate predictive models for perineural invasion (PNI) in gastric cancer (GC) using clinical factors and radiomics features derived from contrast-enhanced computed tomography (CE-CT) scans and to compare the performance of these models.
Methods: This study included 205 GC patients, who were randomly divided into a training set (n=143) and a validation set (n=62) in a 7:3 ratio. Optimal radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm.
Directed Electrostatics Strategy Integrated as a Graph Neural Network Approach for Accelerated Cluster Structure Prediction.
J Chem Theory Comput
January 2025
Advanced Artificial Intelligence Theoretical and Computational Chemistry Laboratory, School of Chemistry, University of Hyderabad, Hyderabad, Telangana 500046, India.
We present a directed electrostatics strategy integrated as a graph neural network (DESIGNN) approach for predicting stable nanocluster structures on their potential energy surfaces (PESs). The DESIGNN approach is a graph neural network (GNN)-based model for building structures of large atomic clusters with specific sizes and point-group symmetry. This model assists in the structure building of atomic metal clusters by predicting molecular electrostatic potential (MESP) topography minima on their structural evolution paths.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!