Since the 1940s, 131-I radioiodine therapy (RIT) has been the primary treatment for metastatic differentiated thyroid cancer (DTC). Approximately half of these patients respond favorably to RIT, achieving partial or complete remission or maintaining long-term stable disease, while the other half develop radioiodine-refractory DTC (RAI-R DTC). The main genomic alteration involved in radioiodine resistance is the activated mitogen-activated protein kinase (MAPK) pathway, which results in the loss of sodium iodide symporters (NIS). Therefore, RAI-R DTC requires alternative treatment options such as tyrosine kinase inhibitors. Over the past decade, several studies have investigated pharmacological induction or enhancement of NIS expression through “redifferentiation” therapies, mainly targeting the MAPK pathway. These novel approaches can restore radioiodine sensitivity in previously refractory patients and, therefore, potentially reestablish the efficacy of RIT. This review discusses various redifferentiation strategies, including their molecular mechanisms and clinical implications.

Coronavirus disease 2019 (COVID-19) was first discovered in December 2019 in China and has rapidly spread worldwide. Clinical characteristics, laboratory findings, and their association with the outcome of patients with COVID-19 can be decisive in management and early diagnosis. Data were obtained retrospectively from medical records of 397 hospitalized COVID-19 patients between February and May 2020 in Imam Reza Hospital, northeast Iran. Clinical and laboratory features were evaluated among survivors and nonsurvivors. The correlation between variables and duration of hospitalization and admission to the intensive care unit (ICU) was determined. Male sex, age, hospitalization duration, and admission to ICU were significantly related to mortality rate. Headache was a more common feature in patients who survived ( ). It was also related to a shorter stay in the hospital ( ) as opposed to patients who experienced chest pain ( ). Decreased levels of consciousness and dyspnea were statistically more frequent in nonsurvivors ( and , respectively). Baseline white blood cell (WBC) count, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) were significantly higher in nonsurvivors ( ). Patients with higher WBC and CRP levels were more likely to be admitted to ICU ( and , respectively). Evaluating clinical and laboratory features can help clinicians find ways for risk stratifying patients and even make predictive tools. Chest pain, decreased level of consciousness, dyspnea, and increased CRP and WBC levels seem to be the most potent predictors of severe prognosis.

Background . BMI has been evaluated as an old criterion to evaluate obesity in individuals, but it does not assess abdominal obesity and lean mass. We aimed to evaluate the possible relationship of new anthropometric indices (namely, a body shape index (ABSI), the body roundness index (BRI), the visceral adiposity index (VAI), the visceral fat area (VFA), and waist‐hip ratio (WHR)), with one of the known critical factors of atherosclerosis, arterial stiffness. Methods . Overall 5921 individuals were enrolled and were divided into four groups according to BMI. Novel anthropometric parameters including, ABSI, BRI, VAI, VFA, and WHR were calculated. The carotid‐femoral pulse wave velocity (cf‐PWV) was used to evaluate arterial stiffness. Multiple regression analysis was performed to assess the relationship between cf‐PWV and innovative Anthropometric indices. Results . This study population consisted of 3109 women and 2812 males. In men with overweight, cf‐PWV was significantly related to BMI, ABSI, BRI, WC, VAI, VFA, and WHR. However, among men with obesity, cf‐PWV was associated with BRI, VAI, and VFA. Among women with overweight, cf‐PWV was also related to all mentioned indices except ABSI; although, cf‐PWV was only associated with VFA and WHR in women with obesity. Conclusion . Our results showed that VFA in women and VAI in men are strongly related to arterial stiffness and can be used to identify predictors of vascular disease or organic vascular dysfunction.

Background: Regarding the assessment of brain metastatic lesions, [ ¹⁸ F]FDG PET/CT encounters challenges due to heightened physiological uptake in normal brain tissue, resulting in poor tumor-to-background ratios (TBR). Detecting brain metastases accurately has long been a challenge with standard imaging methods. The elevated physiological uptake of [ ¹⁸ F]FDG in normal brain parenchyma limits its ability to distinguish metastatic lesions owing to poor tumor-to-background contrast. On the other hand, radiolabeled fibroblast activation protein inhibitors (FAPI) have emerged as a newer imaging tracer, showing promise. This study aimed to collect available literature to assess FAPI’s ability to detect brain metastases across various cancer types. Methods: We reviewed studies published up to April 2025, utilizing different databases, including Google Scholar, PubMed, and Scopus, primarily examining the performance of FAPI based on standardized uptake values (SUVs) and TBRs. Studies focusing on the diagnostic performance of FAPI-based PET for brain metastasis were included. The primary endpoint was the detection rate of FAPI-based tracers. Results: In 24 studies involving over 115 patients and more than 291 lesions, FAPI-based imaging detected of 87.9% lesions (95% CI: 76.1–94.3%, p < 0.001) compared to [ ¹⁸ F]FDG by the detection rate of 46.3% (95% CI: 30.6–62.8%, p = 0.667). The comparison of detection rates of these modalities showed an OR of 10.78 (95% CI: 5.15–22.55, p < 0.001). FAPI radiotracers exhibited higher TBR compared to [ ¹⁸ F]FDG PET/CT (SMD: 1.51, 95% CI: 0.86–2.16, p < 0.001). Conclusion: In conclusion, FAPI - based tracers seem to surpass [ ¹⁸ F]FDG PET in identifying brain metastases. FAPI imaging can potentially serve as a diagnostic tool that offers benefits over traditional methods. Further research is needed to verify its clinical effectiveness.

Background: Thyroid diseases are the second most common hormonal disorders, necessitating accurate diagnostics. Advances in artificial intelligence and radiomics have enhanced diagnostic precision by analyzing quantitative imaging features. However, reproducibility challenges arising from factors such as the field-of-view (FOV) zooming and segmentation variability limit the clinical application of radiomic-based models. Aim: This study focuses on evaluating the impact of segmentation and FOV zooming on the reproducibility of radiomic features and improved performance of machine learning (ML) when using reproducible features for classification of thyroid scintigraphy images into normal, diffuse goiter (DG), multinodular goiter (MNG), and thyroiditis. Patients and Methods: A retrospective analysis was conducted on 872 thyroid scintigraphy cases from 3 centers. Radiomic feature reproducibility was assessed using the intraclass correlation coefficient (ICC), with robust features (ICC≥0.80) identified under segmentation and zooming conditions. Four ML training scenarios were implemented to train models on Center A data, including (1) all, (2) zoom-robust, (3) segmentation-robust, and (4) mutually robust features, with 3 feature selection methods and 7 classifiers. Models were validated on external data sets (centers B and C). Results: FOV zooming significantly reduced feature reproducibility (ICC≥0.80: 49%), while segmentation effects were minimal (ICC≥0.80: 96%). Models trained on mutually robust features outperformed those trained using all features. Boruta-MLP achieved the highest accuracy (0.71, P-value <0.001 vs. all features) in zoomed data sets, and RFE-MLP performed best (0.69, P-value <0.001 vs. all features) in the baseline data set, with Gray-Level Co-occurrence Matrix (GLCM) features frequently selected. Conclusions: Utilizing robust radiomic features significantly improved the performance of ML models in thyroid disease classification, enabling more accurate and generalizable diagnostic outcomes across diverse data sets.