Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for the classification of temporal lobe epilepsy patients from healthy controls.

Background: This study aimed to investigate the clinical application of 18F-FDG PET radiomics features for temporal lobe epilepsy and to create PET radiomics-based machine learning models for differentiating temporal lobe epilepsy (TLE) patients from healthy controls. Methods: A total of 347 subjects who underwent 18F-FDG PET scans from March 2014 to January 2020 (234 TLE patients: 25.50 ± 8.89 years, 141 male patients and 93 female patients; and 113 controls: 27.59 ± 6.94 years, 48 male individuals and 65 female individuals) were allocated to the training (n = 248) and test (n = 99) sets. All 3D PET images were registered to the Montreal Neurological Institute template. PyRadiomics was used to extract radiomics features from the temporal regions segmented according to the Automated Anatomical Labeling (AAL) atlas. The least absolute shrinkage and selection operator (LASSO) and Boruta algorithms were applied to select the radiomics features significantly associated with TLE. Eleven machine-learning algorithms were used to establish models and to select the best model in the training set. Results: The final radiomics features (n = 7) used for model training were selected through the combinations of the LASSO and the Boruta algorithms with cross-validation. All data were randomly divided into a training set (n = 248) and a testing set (n = 99). Among 11 machine-learning algorithms, the logistic regression (AUC 0.984, F1-Score 0.959) model performed the best in the training set. Then, we deployed the corresponding online website version (https://wane199.shinyapps.io/TLE_Classification/), showing the details of the LR model for convenience. The AUCs of the tuned logistic regression model in the training and test sets were 0.981 and 0.957, respectively. Furthermore, the calibration curves demonstrated satisfactory alignment (visually assessed) for identifying the TLE patients. Conclusion: The radiomics model from temporal regions can be a potential method for distinguishing TLE. Machine learning-based diagnosis of TLE from preoperative FDG PET images could serve as a useful preoperative diagnostic tool.

Newly identified adverse events for gemcitabine using the Food and Drug Administration Adverse Event Reporting System.

BACKGROUND: Our research aimed to identify previously undocumented adverse events (AEs) in the gemcitabine drug insert with the goal of informing clinical practice. METHODS: We extracted adverse events associated with gemcitabine use through 2023 using the Food and Drug Administration Adverse Event Reporting System (FAERS) database. Four algorithms (Reporting Odds Ratio, Proportional Reporting Ratio, Bayesian Confidence Propagation Neural Network, and Empirical Bayesian Geometric Mean) were employed to detect new AE signals. AEs were considered positive signals only if they were detected by all four algorithms. RESULTS: From 2014 to 2023, a total of 42,360 AEs were reported in 14,905 individuals following gemcitabine use. These AEs totaled 437 preferred terms (PTs) across 20 system organ classes (SOCs). We identified unexpected AEs related to the ocular disorders, the nervous system, and the ear and the labyrinth. The ocular organ system will present with retinopathy, purtscher retinopathy, choroidal effusion, amaurosis, necrotizing scleritis, etc. The nervous system may experience reversible posterior encephalopathy syndrome, cerebellar syndrome, cauda equina syndrome, athetosis, transverse myelitis, etc. The ears and labyrinth may exhibit ototoxicity. CONCLUSION: Our study identified previously undetected signals following gemcitabine treatment, thereby providing new insights for future medication guidance.

Newly identified adverse events of enzalutamide using the food and drug administration adverse event reporting system.

OBJECTIVES: Enzalutamide, a second-generation anti-androgen drug, is an androgen receptor inhibitor developed to overcome resistance to first-generation anti-androgens, such as bicalutamide. This study aimed to identify previously undisclosed adverse events associated with enzalutamide. METHODS: Adverse reactions following enzalutamide administration were extracted from the Food and Drug Administration Adverse Event Reporting System (FAERS) database, and the data obtained were from 2014 to 2023. Four algorithms, namely ROR, PRR, BCPNN, and EBGM, were used to detect signs of adverse reactions associated with enzalutamide use. RESULTS: This study determined several adverse reactions in the nervous system, including hypogeusia, ageusia, dysgeusia, normal-pressure hydrocephalus, dementia, amnesia, balance disorders, and seizure-like phenomena. The mental aspects manifested as laziness, confusion, and eating disorders. Gastrointestinal system-related adverse reactions included dysphagia, constipation, fecal hardening, and abdominal discomfort. We identified several previously unreported adverse reactions, including normal-pressure hydrocephalus, dementia, balance disorders, eating disorders, and dysphagia. CONCLUSION: Our study revealed novel adverse events associated with enzalutamide, particularly in the nervous system, that have not been previously documented. These findings have important implications for future clinical medication guidelines.

Population-specific brain [18F]-FDG PET templates of Chinese subjects for statistical parametric mapping.

Statistical Parametric Mapping (SPM) is a computational approach for analysing functional brain images like Positron Emission Tomography (PET). When performing SPM analysis for different patient populations, brain PET template images representing population-specific brain morphometry and metabolism features are helpful. However, most currently available brain PET templates were constructed using the Caucasian data. To enrich the family of publicly available brain PET templates, we created Chinese-specific template images based on 116 [18F]-fluorodeoxyglucose ([18F]-FDG) PET images of normal participants. These images were warped into a common averaged space, in which the mean and standard deviation templates were both computed. We also developed the SPM analysis programmes to facilitate easy use of the templates. Our templates were validated through the SPM analysis of Alzheimer's and Parkinson's patient images. The resultant SPM t-maps accurately depicted the disease-related brain regions with abnormal [18F]-FDG uptake, proving the templates' effectiveness in brain function impairment analysis.