The expression levels of microRNA (miR)‑221‑3p and miR‑222‑3p in thyroid cancer have been found to be upregulated compared with those in normal tissues. The present study aimed to determine the effects and potential underlying mechanisms of miR‑221‑3p and miR‑222‑3p on the regulation of radioactive iodine (131I) uptake and radiosensitivity of thyroid cancer cells. The potential regulatory target genes of miR‑221‑3p and miR‑222‑3p were predicted by bioinformatics analysis, and reverse transcription‑quantitative polymerase chain reaction was used to verify miR‑221‑3p, miR‑222‑3p and target gene expression levels in thyroid cancer tissues and cell lines. Overexpression of miR‑221‑3p or miR‑222‑3p in cell models was performed using lentivirus infection. Knockdown of miR‑221‑3p and miR‑222‑3p in cells was achieved using oligonucleotide inhibitor transfection. Western blotting was used to analyze the expression levels of target proteins. In addition, the effects of miR‑221‑3p and miR‑222‑3p on the radiosensitivity of thyroid cancer cells were verified using a colony formation assay. The results of the present study revealed that the expression levels of miR‑221‑3p and miR‑222‑3p were significantly upregulated, while the expression levels of suppressor of cytokine signaling 3 (SOCS3) were downregulated in thyroid cancer tissues. Furthermore, miR‑221‑3p and miR‑222‑3p overexpression downregulated the expression levels of SOCS3, E‑cadherin and solute carrier family 5 member 5 (NIS), and upregulated the expression levels of phosphorylated STAT3 and vimentin. Following the overexpression of miR‑221‑3p or miR‑222‑3p in the FTC133 and TPC1 cell lines, their radiosensitivity was suppressed. In conclusion, the findings of the present study suggested that miR‑221‑3p and miR‑222‑3p may downregulate the expression levels of NIS and promote radioresistance. The potential mechanism was hypothesized to be associated with the miR‑221‑3p and miR‑222‑3p targeting of the SOCS3 gene, which may subsequently activate the STAT3 signaling pathway.

In this study, we aimed to clarify the role of PIM-1 in papillary thyroid carcinoma (PTC) in vitro and investigate the relationship between PIM-1 and redox proteins (NOX4, SOD2, and GPX2) at the tissue and cellular levels. As a PIM-1 inhibitor, SGI-1776 inhibited cell proliferation, colony formation, migration and induced an increase in apoptosis and reactive oxygen species in two PTC cell lines (BCPAP and TPC-1). The expressions of PIM-1, SOD2 and GPX2 were downregulated after siNOX4 exposure. Immunohistochemistry in 120 PTC patients showed that all four proteins exhibited higher expression levels in PTC tissues than in adjacent normal tissues. PIM-1 expression was related to NOX4, SOD2, and GPX2 expressions. The Cancer Genome Atlas database analysis showed the significant correlation between the expression of NOX4 and PIM-1. Our results demonstrated that PIM-1 played an important oncogenic role in PTC carcinogenesis that may be related to oxidative stress.

The purpose of this study is to compare the performance of GPT-4 and DeepSeek large language models in generating structured breast cancer multimodality imaging integrated reports from free-text radiology reports including mammography, ultrasound, MRI, and PET/CT. A retrospective analysis was conducted on 1358 free-text reports from 501 breast cancer patients across two institutions. The study design involved synthesizing multimodal imaging data into structured reports with three components: primary lesion characteristics, metastatic lesions, and TNM staging. Input prompts were standardized for both models, with GPT-4 using predesigned instructions and DeepSeek requiring manual input. Reports were evaluated based on physician satisfaction using a Likert scale, descriptive accuracy including lesion localization, size, SUV, and metastasis assessment, and TNM staging correctness according to NCCN guidelines. Statistical analysis included McNemar tests for binary outcomes and correlation analysis for multiclass comparisons with a significance threshold of P < .05. Physician satisfaction scores showed strong correlation between models with r-values of 0.665 and 0.558 and P-values below .001. Both models demonstrated high accuracy in data extraction and integration. The mean accuracy for primary lesion features was 91.7% for GPT-4% and 92.1% for DeepSeek, while feature synthesis accuracy was 93.4% for GPT4 and 93.9% for DeepSeek. Metastatic lesion identification showed comparable overall accuracy at 93.5% for GPT4 and 94.4% for DeepSeek. GPT-4 performed better in pleural lesion detection with 94.9% accuracy compared to 79.5% for DeepSeek, whereas DeepSeek achieved higher accuracy in mesenteric metastasis identification at 87.5% vs 43.8% for GPT4. TNM staging accuracy exceeded 92% for T-stage and 94% for M-stage, with N-stage accuracy improving beyond 90% when supplemented with physical exam data. Both GPT-4 and DeepSeek effectively generate structured breast cancer imaging reports with high accuracy in data mining, integration, and TNM staging. Integrating these models into clinical practice is expected to enhance report standardization and physician productivity.

This study aimed to assess the efficacy of fluor-18 fluorodeoxyglucose (¹⁸F-FDG) PET/CT using sub-regional-based radiomics in predicting epidermal growth factor receptor (EGFR) mutation status in pretreatment patients with solid lung adenocarcinoma. A retrospective analysis included 269 patients (134 EGFR+ and 135 EGFR-) who underwent pretreatment ¹⁸F-FDG PET/CT scans and EGFR mutation testing. The most metabolically active intratumoral sub-region was identified, and radiomics features from whole tumors or sub-regional regions were used to build classification models. The dataset was split into a 7:3 ratio for training and independent testing. Feature subsets were determined by Pearson correlation and the Kruskal Wallis test and radiomics classifiers were built with support vector machines or logistic regressions. Evaluation metrics, including accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were employed for different classifiers. Results indicated that the sub-region-based classifier outperformed the whole-tumor classifier in terms of accuracy (73.8% vs. 66.2%), AUC (0.768 vs. 0.632), specificity (65.0% vs. 50.0%), PPV (70.2% vs. 62.2%), and NPV (78.8% vs. 74.0%). The clinical classifier exhibited an accuracy of 75.0%, AUC of 0.768, sensitivity of 72.5%, specificity of 77.5%, PPV of 76.3%, and NPV of 73.8%. The combined classifier, incorporating sub-region analysis and clinical parameters, demonstrated further improvement with an accuracy of 77.5%, AUC of 0.807, sensitivity of 77.5%, specificity of 77.5%, and NPV of 77.5%. The study suggests that sub-region-based ¹⁸F-FDG PET/CT radiomics enhances EGFR mutation prediction in solid lung adenocarcinoma, providing a practical and cost-efficient alternative to invasive EGFR testing.

Accurately distinguishing benign from malignant adrenal lesions remains a clinical challenge, especially in oncology patients with indeterminate imaging findings. This study aimed to develop and interpret machine learning (ML) models for classifying adrenal lesions based on 18 F-FDG PET/CT imaging and clinical parameters. A retrospective cohort of 255 patients undergoing 18 F-FDG PET/CT was analyzed. Imaging features—including adrenal SUVmax, SUVpeak, tumor diameter, CT attenuation, and tumor-to-liver SUVmax ratio (T/L SUVmax)—along with clinical variables were extracted. Two classification tasks were constructed: (1) differentiation of benign and malignant adrenal lesions; and (2) subtyping of malignant lesions into lung cancer metastases or lymphoma. Seven ML models were trained and evaluated using 10-fold cross-validation. SHAP (SHapley Additive exPlanations) analysis was applied to elucidate feature contributions. For the benign/malignant classification, ensemble models (Random Forest, Bagging, XGBoost) achieved outstanding performance (AUC > 0.99), with Bagging yielding 100% recall. T/L SUVmax, adrenal SUVmax, and CT attenuation emerged as top predictors. In malignancy subtyping, the artificial neural network (ANN) attained the highest AUC (0.887) and F1-score (0.851). SHAP analysis highlighted distinct metabolic patterns, with lymphoma showing higher SUVmax and T/L ratios, and lung metastases associated with higher CT values. Machine learning models based on PET/CT-derived features enable highly accurate and interpretable classification of adrenal lesions. Integrating metabolic and anatomical parameters improves diagnostic precision, while SHAP analysis offers clinical transparency, supporting personalized decision-making in adrenal lesion management.