Objective To investigate the impact of visit-to-visit systolic blood pressure variability (SBPV) on brain structure and cognitive function among non-frail and pre-frail populations using data from the UK Biobank database. Methods Clinical and neuroimaging data from 59 087 non-frail and pre-frail individuals at baseline were retrospectively collected from the UK Biobank. SBPV was quantified by calculating the standard deviation (SD) of systolic blood pressure measurements across three visits. Based on SBPV tertiles, participants were categorized into three groups: the T1 group (low SBPV, n=5242), the T2 group (moderate SBPV, n=13 949), and the T3 group (high SBPV, n=39 896). Quantitative brain structural metrics were extracted via MRI, and cognitive function was evaluated using standardized assessments. Multivariable regression models were constructed to analyze the associations of SBPV with MRI-derived brain structural features and cognitive function, while restricted cubic splines (RCS) were applied to explore potential non-linear relationships. Results Multivariable regression analysis revealed that, after adjusting for confounding factors, elevated SBPV was significantly associated with degenerative changes in brain structure. Compared with the low SBPV group, the high SBPV group exhibited a significant reduction in total brain volume (β=-8697.41, 95% CI: -10496.65--6898.17, P<0.001) and a significant increase in white matter hyperintensity (WMH) volume (β=600.09, 95% CI: 466.48-733.69, P<0.001). RCS analysis demonstrated an inverted U-shaped relationship between SBPV and brain volume metrics, and a J-shaped relationship with WMH volume. Furthermore, cognitive function analysis indicated that SBPV exerted complex non-linear effects on specific cognitive domains, including prospective memory and reaction time. Conclusion Among non-frail and pre-frail populations, elevated visit-to-visit SBPV is closely associated with brain structural degeneration and partial cognitive decline. These findings provide objective evidence that maintaining long-term blood pressure stability may serve as a potential interventional target for early neuroprotection.

Objective To investigate the correlation of epicardial adipose tissue volume (EATV) and pericoronary fat attenuation index (FAI) with coronary atherosclerotic plaques and coronary heart disease, and to evaluate the clinical value of their combined application. Methods A retrospective analysis was conducted on 420 patients who underwent coronary computed tomography angiography at the Fifth Affiliated Hospital of Southern Medical University from January 2020 to December 2023. Patients were categorized into three groups based on coronary artery calcium score (CACS): a no-plaque group (CACS=0, n=120), a low-calcification plaque group (CACS≤100, n=175), and a moderate-to-severe calcification plaque group (CACS>100, n=125). EATV was measured using 3D Slicer software, and FAI was measured in standard segments of the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA) using ShuKun AI software. Statistical analyses were performed in conjunction with clinical data. Results EATV was significantly higher in the plaque groups than in the no-plaque group (P<0.01). LAD-FAI differed significantly among the three groups (P=0.003), whereas no significant differences were observed for LCX-FAI and RCA-FAI. EATV was positively correlated with body mass index (r=0.35, P<0.01) and hyperlipidemia (r=0.28, P<0.05). Similarly, FAI was positively correlated with BMI (r=0.32, P<0.01) and hyperlipidemia (r=0.25, P<0.05). Multivariate logistic regression identified both EATV and LAD-FAI as independent predictors of plaque presence (P<0.05). ROC curve analysis demonstrated that the combined use of EATV and FAI achieved an area under the curve of 0.809 (95% CI: 0.78-0.86) for plaque diagnosis, which was superior to either parameter alone. Conclusion EATV and FAI are effective non-invasive imaging biomarkers reflecting systemic metabolic disorders and local coronary inflammation, respectively. Their combined application provides complementary information, significantly improves risk stratification of coronary atherosclerotic plaques, and holds important potential for clinical translation.

Objective To investigate the value of ultrasonographic features of the superficial fascia of the breast in differentiating benign and malignant BI-RADS category 4 lesions, and to develop a nomogram incorporating these features to evaluate its potential clinical utility. Methods A retrospective study was conducted in 357 patients with BI-RADS category 4 breast lesions (357 representative nodules) who underwent ultrasound examination at Inner Mongolia Autonomous Region People's Hospital from January 2022 to December 2024. In patients with multiple lesions, the largest nodule was selected for analysis. All lesions were confirmed by surgical pathology. Two radiologists with at least 5 years of experience independently reviewed the images while blinded to the pathological results. Reproducibility was assessed in 20 stratified randomly selected cases and showed good agreement (κ=0.73, ICC=0.94). Key variables included the relationship with the superficial fascia of the breast (no contact, blurred, or interrupted) and the proportion outside the superficial fascia of the breast (area beyond the superficial fascia divided by total lesion area×100%). Candidate predictive variables were identified through univariable and multivariable logistic regression analyses, and a nomogram was subsequently developed. Model performance was evaluated using ROC curves, the DeLong test, calibration curves, 2000 bootstrap resamples for internal validation, and decision curve analysis (DCA). Results After 2000 bootstrap resamples, the nomogram achieved an optimism-corrected AUC of 0.92 (95% CI: 0.90-0.96). At the optimal Youden cut-off value of 0.37, the sensitivity was 86.0% and the specificity was 88.7%. The calibration slope was 0.92, the intercept was -0.04, and the Brier score was 0.10. The DeLong test showed that the nomogram outperformed BI-RADS classification alone (ΔAUC=0.08, FDR-adjusted q<0.001) and the combination of superficial fascia features with BI-RADS (ΔAUC=0.05, FDR-adjusted q<0.001). Decision curve analysis demonstrated a positive net benefit across a threshold probability range of 0.10-0.70. Conclusion Ultrasonographic features of the superficial fascia of the breast improve the differentiation of benign and malignant BI-RADS category 4 lesions. The derived prediction model demonstrates favorable diagnostic performance and potential clinical applicability.

Objective To develop a machine learning model based on contrast-enhanced MRI radiomics features for predicting BUB1B gene expression in nasopharyngeal carcinoma (NPC), and to explore the potential biological role of BUB1B in NPC through bioinformatics analysis using public databases. Methods A total of 57 patients with nasopharyngeal carcinoma from the Affiliated Tumor Hospital of Guangxi Medical University from April to October 2022 were retrospectively enrolled. Clinical, pathological, and imaging data were collected. Radiomics features were extracted from contrast-enhanced MRI images, and patients were randomly divided into training and testing sets at a ratio of 7:3. Prediction models were constructed using a clinical model, logistic regression (LR), support vector machine (SVM), and naive Bayes (NB), and their performance was compared. A nomogram was further developed based on the clinical model and LR model. In addition, datasets from the GEO database were used to analyze the prognostic value of BUB1B in NPC. Results The AUC of the LR model was 0.735 in the training set and 0.743 in the testing set. The nomogram model achieved AUCs of 0.755 and 0.743 in the training and testing sets, respectively. There was no statistically significant difference in diagnostic performance between the two models (P>0.05). GEO database analysis showed that BUB1B was upregulated in NPC tissues, and its expression was associated with patient prognosis. GO and KEGG enrichment analyses suggested that BUB1B may be involved in cell cycle regulation. Conclusion The machine learning model based on contrast-enhanced MRI radiomics features can predict BUB1B expression in nasopharyngeal carcinoma to a certain extent. Bioinformatics analysis of external cohorts suggests that BUB1B may be involved in the occurrence and progression of nasopharyngeal carcinoma through cell cycle-related pathways.

Objective To explore the correlation between the organ doses of the eyes and the four types of CT radiation dose values, and to explore the feasibility of a rapid method for estimating the organ doses of patients undergoing head CT examinations. Methods From May 2024 to February 2025, 90 patients who underwent head CT examinations at Xuzhou First People's Hospital were prospectively collected. Using the block randomization method (by age and gender stratification), they were divided into three groups: using fixed tube current technology, CARE Dose 4D, and X-CARE technology, with 30 cases in each group. During the scan, a thermoluminescent dosimeter was placed on the surface of the patient's eyes, attached to the center of the upper eyelid and arranged in three rows without covering the root of the eyelashes, and the organ doses of the eyes were recorded. Then, the CT radiation dose value is calculated based on the equivalent water diameter (Dw) and the volumetric CT dose index (CTDI_vol). Specifically, the body size-specific dose estimation value (SSDE_center) for the central layer is calculated based on the Dw of the central slice within the scanning range. The average Dw of all slices is used to calculate the average body size-specific dose estimation value (SSDE_mean) for all slices, as well as the body size-specific dose estimation value (SSDE_organ) for the eye organ layer. Linear correlation analysis was used to evaluate the correlation between the organ dose of the eyes and each CT radiation dose value, and conversion factors were derived for rapid estimation of the organ dose based on CT radiation dose values. Results The organ doses of the fixed tube current group were fitted to the curves of SSDE_mean, SSDE_center and SSDE_organ as y₁=0.60x (r²=0.687, P<0.05), y₂=0.56x (r²=0.733, P<0.05), y₃=0.56x (r²=0.807, P<0.05); the organ doses of the CARE Dose 4D group were fitted to the curves of CTDI_vol, SSDE_mean, SSDE_center and SSDE_organ as y₁=0.96x (r²=0.395, P<0.05), y₂=0.83x (r²=0.421, P<0.05), y₃=0.76x (r²=0.422, P<0.05), y₄=0.77x (r²=0.621, P<0.05); the organ doses of the X-CARE group were fitted to the curves of CTDI_vol, SSDE_mean, SSDE_center and SSDE_organ as y₁=0.83x (r²=0.568, P<0.05), y₂=0.83x (r²=0.687, P<0.05), y₃=0.76x (r²=0.687, P<0.05); y₄=0.77x (r²=0.741, P<0.05). Conclusion In head CT examinations, SSDE_organ has the best correlation with the organ dose of the eyes. Utilizing CT radiation dose values as an estimation index for organ dose in head CT examinations is feasible and can serve as a reference for rapid estimation, which is conducive to individualized and precise management of radiation dose and risk assessment for patients in clinical practice.

Objective To investigate the diagnostic performance of dual-time-point ¹⁸F-prostate-specific membrane antigen (PSMA)-1007 PET/CT imaging in differentiating prostate cancer from benign prostatic lesions. The study also aimed to evaluate whether dual-time-point parameters provide advantages over single-time-point parameters. Methods A total of 46 patients with prostate diseases who were treated at the First Affiliated Hospital of Xinjiang Medical University form March 2024 to April 2025 were included. According to pathological results, patients were divided into a prostate cancer group (CA group, n=31) and a benign lesion group (benign group, n=15). All patients underwent ¹⁸F-PSMA-1007 PET/CT dual-time-point imaging. The standard phase was acquired at 68.20±12.89 min after injection, and the delayed phase at 160±17.98 min. Standardized uptake value (SUV)-related parameters and dual-time-point change parameters were measured and analyzed. Robustness analyses were also performed. Results All SUV-related parameters, the change in maximum standardized uptake value (ΔSUV_max), and the retention index (RI) were significantly higher in the CA group than in the benign group (P<0.001). ROC analysis showed that RI (AUC=0.966) and ΔSUV_max (AUC=0.961) had better diagnostic performance than SUV_max from a single time point (P<0.05). These dual-time-point parameters increased the diagnostic accuracy for gray-zone cases from 59.3% to 96.3%. Bootstrap validation confirmed the robustness of the AUC values. Sensitivity analysis for gray-zone cases also indicated stable diagnostic advantages of dual-time-point parameters. Correlation analysis showed a weak positive correlation between serum prostate-specific antigen levels and PET quantitative parameters but without statistical significance(P>0.05). Strong positive correlations were observed between SUV_max at the standard and delayed phases, ΔSUV_max, and RI (P<0.001). Mild collinearity between ΔSUV_max and RI was observed in gray-zone diagnosis (variance inflation factor, VIF=4.13). Conclusion The dual-time-point parameters ΔSUV_max and RI from ¹⁸F-PSMA-1007 PET/CT imaging show good performance in differentiating prostate cancer from benign prostatic lesions. Their diagnostic value is superior to that of single-time-point parameters. These parameters may provide useful evidence for cases within the diagnostic gray zone.

Objective To develop a computed tomography (CT)-based radiomics score combined with clinical factors for predicting the response of patients with locally advanced gastric cancer to programmed cell death protein-1 (PD-1) inhibitors combined with the fluorouracil+leucovorin+oxaliplatin+docetaxel (FLOT) regimen. Methods A retrospective analysis was conducted on 97 patients with locally advanced gastric cancer who received PD-1 inhibitor (sintilimab) combined with the FLOT regimen at Henan Cancer Hospital from December 2019 to July 2024. Patients were randomly assigned to a training cohort (n=65) and an internal validation cohort (n=32) at a ratio of 7:3. Additionally, 125 patients with locally advanced gastric cancer treated at the First Affiliated Hospital of Zhengzhou University during the same period were enrolled as an external validation cohort. Treatment outcomes were classified as responder or non-responder according to the tumor regression grade (TRG) criteria. Radiological and clinical data were collected, and 3D Slicer software was used for image processing and feature extraction. Relevant features were selected using recursive feature elimination (RFE) and LASSO algorithms to construct a radiomics score (RS). A radiomic model and a combined clinical-radiomic model were developed based on statistically significant clinical features and the RS. The performance of the two models was compared, and SHAP analysis was used for model interpretation. A web-based prediction calculator was developed based on the optimal model. Results In the training and internal validation cohorts, 39 (60.0%) and 13 (40.6%) patients were classified as responders, respectively. Higher BMI (P=0.019) and age (P=0.015) showed statistically significant differences between responders and non-responders in the training cohort. After feature selection, 13 relevant radiomic features were integrated into the RS. The combined clinical-radiomic model demonstrated superior discriminative ability across the training, internal validation, and external validation cohorts, with area under the receiver operating characteristic curve (AUC) values of 0.90 (95% CI: 0.83-0.97), 0.92 (95% CI: 0.82-0.99), and 0.94 (95% CI: 0.91-0.97), respectively. SHAP analysis indicated that the RS was an important predictor. Conclusion The combined clinical-radiomic prediction model has potential value for predicting treatment outcomes in patients with locally advanced gastric cancer. The web-based calculator developed based on this model may serve as a personalized auxiliary tool to assist clinicians in treatment planning.

Objective To investigate the value of a fusion model based on enhanced CT deep learning radiomics combined with clinical and pathological features in the preoperative prediction of lymphovascular invasion (LVI) in colorectal cancer. Methods With postoperative pathology as the gold standard, a total of 219 patients with colorectal cancer were retrospectively enrolled from the First Affiliated Hospital of Bengbu Medical University from January 2022 to October 2024, including 82 LVI (+) and 137 LVI (-) cases. Patients were divided into training set (n=153) and validation set (n=66) at a ratio of 7:3. A clinical model was constructed based on collected clinicopathological data. Radiomic features of tumors were extracted using PyRadiomics, and seven classifiers (logistic regression, random forest, K-nearest neighbor, extreme gradient boosting, support vector machine, extremely randomized trees, and naive Bayes) were employed for training to select the optimal classifier for establishing a radiomics (Rad) model. A pre-trained DenseNet121 deep learning network was used for model training to construct a deep learning (DL_3D) model. The generated deep learning score (DL_score) was fused with radiomic features, and the optimal classifier K-nearest neighbor (KNN) was selected to build a fusion model (Fusion_DLR). Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), specificity, and other indicators. Calibration curves and decision curve analysis were applied to assess model calibration and clinical utility. Univariate logistic analysis was performed on preoperative clinicopathological data to screen for significant clinical features, which were then combined with the best-performing model among the four aforementioned models to construct and visualize a nomogram. Results Univariate logistic regression analysis showed that pathological grade was statistically significant (P<0.05). The AUC values of the fusion model were 0.927 (95% CI: 0.863-0.990) in the training set and 0.823 (95% CI: 0.684-0.962) in the validation set, respectively. The AUC values of the other three models in the training set were 0.847, 0.823, and 0.862, and those in the validation set were 0.677, 0.792, and 0.765, respectively, indicating that the fusion model was the optimal model. The nomogram constructed by combining the fusion model with pathological grade intuitively demonstrated that patients with higher pathological grade had a higher risk of being LVI (+). Conclusion The enhanced CT-based deep learning radiomics fusion model combined with clinical and pathological features has potential predictive value for LVI status in colorectal cancer.

Objective To investigate the value of time-dependent diffusion-weighted imaging (td-DWI) in the identification of clinically significant prostate cancer (csPCa) and the analysis of its microstructural characteristics. Methods This retrospective study included 128 patients with prostate diseases who underwent MRI at Ma'anshan People's Hospital from March 2023 to May 2025. Among them, 59 patients were diagnosed with csPCa (csPCa group), and 69 patients had non-csPCa prostate diseases (non-csPCa group). Clinical indicators, various ADC values, and td-DWI microstructural parameters-including intracellular volume fraction (Vin), extracellular diffusion coefficient (Dex), diameter (d), and cellularity—were compared between the two groups. Univariate and multivariate logistic regression analyses were used to construct a predictive model, and its diagnostic performance was evaluated. The SHAP method was employed to interpret the model output. Results Significant differences were observed between the csPCa and non-csPCa groups in tPSA, PI-RADS score, ZoomitADC, ADC25, ADC50, ADCPGSE, as well as Vin, Dex, and d values (P<0.05). Multivariate logistic regression analysis identified Vin as an independent predictor (OR=1.22, P<0.001). The diagnostic model achieved AUCs of 0.961 and 0.985 in the training and test sets, respectively. SHAP analysis indicated that Vin contributed the most to the model's predictions. Conclusion td-DWI microstructural parameters, particularly Vin, hold significant clinical value in distinguishing csPCa and can substantially enhance the quantitative diagnostic capability of prostate cancer imaging.

Objective To explore the diagnostic value of prostate imaging-reporting and data system version 2.1 (PI-RADS v2.1) score of multi-parameter magnetic resonance imaging (mp-MRI) combined with serum total prostate-specific antigen (tPSA) on transitional zone prostate cancer (TZPCa). Methods A total of 458 patients with prostate diseases confirmed by pathology in the First People's Hospital of Yibin from May 2022 to May 2025 were retrospectively selected. The PI-RADS v2.1 score and serum tPSA of all patients were recorded. Based on the pathological results of patients, they were classified into TZPCa group (n=209) and benign prostatic hyperplasia (BPH) group (n=249). The diagnostic value of PI-RADS v2.1 score, tPSA and their combination on TZPCa was analyzed by ROC curve, and the influencing factors for TZPCa were explored by Logistic stepwise regression analysis. Results The PI-RADS v2.1 score and tPSA in the TZPCa group were higher compared to the BPH group (P<0.05). ROC curve analysis results indicated that the areas under the curves (AUCs) of PI-RADS v2.1 score, tPSA and their combination were 0.718 (95% CI: 0.672-0.764), 0.698 (95% CI: 0.649-0.746) and 0.808 (95% CI: 0.769-0.847) in the diagnosis of TZPCa. Patients in the TZPCa group were older and had higher free prostate-specific antigen (fPSA), higher prostate-specific antigen density (PSAD) and higher proportion of abnormal digital rectal examination results compared to the BPH group, and the prostate volume and fPSA/tPSA were lower in the TZPCa group than those in the BPH group (P<0.05). After logistic stepwise regression analysis, PSAD (OR=1.112, 95% CI: 1.034-1.195), fPSA/tPSA (OR=0.890, 95% CI: 0.799-0.992), prostate volume (OR=0.899, 95% CI: 0.829-0.974) and PI-RADS v2.1 score (OR=1.108, 95% CI: 1.021-1.204) were found to be associated with TZPCa (P<0.05). Conclusion PI-RADS v2.1 score combined with serum tPSA exhibits important value in the diagnosis of patients with TZPCa, and can assist clinical diagnosis and treatment decisions.

Objective To compare the diagnostic performance of strain elastography (SE) and shear wave elastography (SWE) combined with the Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) in thyroid nodules, and to investigate the value of different correction strategies based on their combined use. Methods The clinical and imaging data of 176 thyroid nodules from 176 patients admitted to the Department of Ultrasound, The First Affiliated Hospital of Bengbu Medical University from May 2024 to August 2025 were retrospectively analyzed. All patients underwent conventional ultrasound, SE, and SWE examinations. Conventional ultrasound findings were classified according to C-TIRADS. The SE parameters included elasticity score and strain ratio (SR), whereas the SWE parameters including maximum Young's modulus (E_max), minimum Young's modulus (E_min), mean Young's modulus (E_mean), and the standard deviation of Young's modulus (E_sd). Pathological findings were used as the reference standard. ROC curves were constructed to evaluate and compare the diagnostic performance of C-TIRADS, SE, SWE, and their combinations under different classification correction strategies for differentiating benign from malignant thyroid nodules. Results Of the 176 thyroid nodules, 104 were malignant and 72 were benign. When used alone, conventional ultrasound showed relatively high diagnostic performance, with an AUC of 0.845. Among the SE parameters, the AUCs of elasticity score and SR were 0.714 and 0.811, respectively. Among the SWE parameters, the AUCs of E_max, E_min, E_mean, and E_sd were 0.817, 0.623, 0.797, and 0.696, respectively. When combined with conventional ultrasound, SWE showed better diagnostic performance than SE, with AUCs of 0.801 and 0.753, respectively. The overall diagnostic performance was further improved after dual-elastography-based correction. Conclusion Both SE and SWE combined with C-TIRADS can improve the differential diagnostic efficacy of thyroid nodules, among which dual-elastography-based correction provides better diagnostic performance. The two elastography techniques may complement each other in the diagnosis of thyroid nodules and provide more diagnostic information for sonographers.

Objective To evaluate the diagnostic performance of deep learning models on both representative and randomly selected clinical ultrasound images, and to compare their performance with that of radiologists with different levels of experience, in order to objectively assess the potential of artificial intelligence-assisted diagnosis in real-world clinical settings. Methods A total of 936 ultrasound images from 168 patients, retrospectively collected from December 2022 to July 2024 at the Quanzhou First Hospital Affiliated to Fujian Medical University (Quanzhou First Hospital, Fujian), were included in this study. The dataset was divided into training, validation, and test sets in a ratio of 7:1:2. Six convolutional neural network models (VGG19_bn, DenseNet121, Swin Transformer-Tiny, ConvNeXt-Tiny, MobileNetV2, and ResNet101) were employed to evaluate diagnostic performance on representative images selected by radiologists. To further assess model generalization, a fully random image sampling strategy was applied, and the experiments were repeated. The diagnostic performance of the best-performing model was quantitatively compared with that of three radiologists with senior, intermediate, and junior levels of experience. Results On representative images, the VGG19_bn model achieved the best performance (AUC=0.906). In the random image testing scenario, DenseNet121 demonstrated the strongest robustness (AUC=0.888), outperforming the senior ultrasound radiologist (AUC=0.738). Notably, DenseNet121 showed a superior balance between sensitivity (0.786) and specificity (0.857). In contrast, radiologist diagnosis tended to exhibit high sensitivity but relatively low specificity, with variability in sensitivity observed across different experience levels. Conclusion Well-trained deep learning models not only achieve strong diagnostic performance under controlled conditions but also demonstrate stable generalization ability in randomly sampled, clinically realistic scenarios. Their overall diagnostic performance is comparable to that of radiologists, highlighting their potential value in real-world clinical applications.

Objective To investigate the correlation between anatomical parameters of the hip joint and proximal femur measured on hip MR images and hip rotator cuff injury, and to analyze potential anatomical influencing factors. Methods A retrospective study was performed. 74 patients (86 hips) diagnosed with hip rotator cuff injury by bilateral hip MRI in the Department of Radiology, Xuzhou Central Hospital from February 2020 to August 2025 were enrolled as the experimental group. Another 80 age-, gender- and hip side-matched patients (96 hips) with negative bilateral hip MRI results during the same period were selected as the control group. Unilateral hip images were reconstructed from all raw images using the post-processing function of the Picture Archiving and Communication Systems (PACS). The following anatomical parameters were measured and recorded: femoral offset, neck-shaft angle, acetabular abduction angle, acetabular anteversion angle, center-edge (CE) angle, greater trochanter angle, greater trochanter height and proximal femoral length. The differences between each parameter in the experimental group and the control group were compared. Parameters with statistically significant inter-group differences were included in the logistic regression model for further analysis. Results There were statistically significant differences in femoral offset, greater trochanter height, neck-shaft angle, acetabular anteversion angle and CE angle between the experimental group and the control group (P<0.05). Univariate logistic regression analysis showed that all the above variables were statistically significant (P<0.05). Multivariate logistic regression analysis indicated that greater trochanter height (OR=1.268, P<0.001) and acetabular anteversion angle (OR=1.203, P=0.001) were positively correlated with hip rotator cuff injury, and were independent risk factors; CE angle (OR=0.885, P<0.001) was negatively correlated with hip rotator cuff injury, and was an independent protective factor. There were no statistically significant differences in proximal femoral length, acetabular abduction angle and greater trochanter angle between the two groups (P>0.05). Conclusion Hip rotator cuff injury is significantly correlated with femoral offset, greater trochanter height, neck-shaft angle, acetabular anteversion angle and CE angle. Among them, greater trochanter height, acetabular anteversion angle and CE angle are independent influencing factors for hip rotator cuff injury.

Objective To explore the predictive value of CT parameters of solid components in pulmonary mixed ground-glass nodules (mGGN) for infiltration degree of lung adenocarcinoma. Methods The clinical data of 120 patients with lung adenocarcinoma who presented with pulmonary mGGN and were confirmed by pathology from January 2022 to April 2024 were collected. Based on pathological results, patients with invasive adenocarcinoma (IAC) were included in the IAC group (n=73) and those with adenocarcinoma in situ and minimally invasive adenocarcinoma were included in the non-IAC group (n=47). Baseline data, CT signs and parameters of solid components in mGGN were compared between the two groups. ROC curves were used to evaluate the predictive value of CT parameters of solid components in pulmonary mGGN for IAC. Results Compared with the non-IAC group, the IAC group showed older age, higher proportions of lobulation, spiculation and air bronchogram signs, larger maximum diameter, average diameter, maximum CT value and average CT value of solid components (P<0.05). Multivariate logistic regression analysis showed that lobulation sign, air bronchogram sign, average diameter, maximum CT value, and average CT value were independent influencing factors for infiltration degree of lung adenocarcinoma (P<0.05). ROC curves showed that the optimal cutoff values of average diameter, maximum CT value, and average CT value of solid components in pulmonary mGGN for predicting infiltration degree of lung adenocarcinoma were 10.09 mm, -114.00 HU, and -394.07 HU, respectively. The AUC and sensitivity of combination of these three parameters for predicting infiltration degree of lung adenocarcinoma were 0.892 and 89.04%, respectively. Both were higher than those of any parameter alone (P<0.05). Conclusion CT parameters of solid components in pulmonary mGGN are closely related to infiltration degree of lung adenocarcinoma. Among them, the combination of average diameter, maximum CT value, and average CT value demonstrates high predictive value for infiltration degree of lung adenocarcinoma.

The prevalence rate of insomnia is as high as 15%, presents limitations in traditional diagnostic methods for both mechanistic research and clinical assessment. Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive neural monitoring technique. fNIRS possesses the capability for direct monitoring of cerebral cortical haemodynamics, featuring a sampling rate of approximately 10Hz or higher and excellent resistance to motion artefacts. fNIRS has become an important tool for investigating the neural mechanisms of insomnia and for clinical monitoring. fNIRS can effectively monitor dynamic changes in oxyhaemoglobin levels within superficial cortical regions such as the prefrontal cortex in insomnia patients, as well as alterations in the connectivity state of the default mode network between certain cortical regions. fNIRS provides novel data supporting the exploration of neural circuit mechanisms underlying sleep-wake regulation. The combined application of fNIRS and multimodal technologies demonstrates potential for guiding personalised treatment. This paper systematically reviews the technical advantages and research progress of fNIRS in assessing brain function and monitoring treatment efficacy for insomnia disorders, aiming to advance the application of fNIRS in clinical research and facilitate the development of more precise objective assessment and intervention strategies for insomnia.

Cardiovascular diseases represent a major global health burden, for which percutaneous intervention has become an essential therapeutic strategy in clinical practice. However, conventional cardiac interventional procedures still rely on two-dimensional imaging, which inherently suffers from limitations such as insufficient spatial information, restricted procedural precision, and radiation exposure. Previous studies have demonstrated that the augmented reality/mixed reality-based navigation technology can facilitate the visualization of complex cardiac anatomies, potentially enhancing the accuracy of preoperative measurements, improving intraoperative catheter positioning, and reducing contrast agent consumption. Nevertheless, the application and research progress of the augmented reality/mixed reality-based technology in cardiac interventional surgeries are still lacking systematic analysis. This review systematically summarizes the current applications and research progress of the augmented reality/mixed-based navigation technology in various cardiac procedures, including atrial septal defect closure, transcatheter aortic valve replacement, left atrial appendage occlusion, radiofrequency ablation, and mitral valve interventions, with a focus on in-depth discussions regarding three key stages: preoperative planning, intraoperative navigation, and postoperative assessment, aiming to provide a reference for the clinical application and future research of this technology in cardiac interventional procedures.

Diffuse large B-cell lymphoma is a highly heterogeneous hematologic malignancy with significant differences in prognosis. Current clinical prognostic scoring systems lack the precision for accurate risk stratification, especially in identifying high-risk patients. Positron emission tomography/computed tomography has been extensively used in the pretherapeutic evaluation of diffuse large B-cell lymphoma patients. Increasing researches show that PET/CT markers and radiomic features can predict survival outcomes thereby enhancing the precision of risk stratification in DLBCL. This review focuses on the use of ¹⁸F-FDG PET/CT markers and radiomics in DLBCL risk stratification, with an emphasis on their applications, risk stratification strategy and limitations.

Inflammatory bowel disease (IBD) is a group of chronic nonspecific intestinal inflammatory disorders with an undefined etiology. Early diagnosis remains challenging, and severe late-stage complications significantly impair patients' quality of life, making early diagnosis and intervention critical. Commonly used clinical diagnostic methods, such as CT, MRI, ultrasound, and digestive endoscopy, have certain limitations in the early detection of IBD, underscoring an urgent need for sensitive and specific early diagnostic strategies suitable for molecular imaging research. An increasing number of studies have investigated the application of various radionuclide molecular imaging agents, such as WBC, FDG, FAPI, CD45 and CXCR4, in diverse inflammatory diseases. These molecular imaging agents play a pivotal role in early disease diagnosis, inflammatory activity assessment, complication detection, as well as therapeutic response monitoring and prognosis evaluation, exhibiting high potential for clinical translation in molecular imaging practice. This review summarizes the latest research progress of radionuclide molecular imaging in the diagnosis and treatment of IBD, comprehensively analyzes its advantages, disadvantages, and application prospects in the field of molecular imaging, and aims to provide robust evidence to support the clinical application of molecular imaging techniques in IBD diagnosis.

Minimal hepatic encephalopathy (MHE), as the early occult stage of hepatic encephalopathy, often presents with unremarkable clinical manifestations and routine blood biochemical parameters. Its diagnosis primarily relies on neurophysiological or neuropsychological tests. Without timely intervention, MHE can progress to overt hepatic encephalopathy, significantly impairing patients' quality of life. In recent years, resting-state functional magnetic resonance imaging(rs-fMRI), with its non-invasive and precise advantages, has provided crucial neuroimaging tools for unraveling the neural mechanisms of MHE. However, existing reviews have predominantly focus on the conventional applications of rs-fMRI in MHE, lacking a systematic synthesis of multi-dimensional brain functional metrics and their integration with artificial intelligence techniques. This review systematically summarizes the latest advancements in utilizing rs-fMRI-based brain functional metrics to investigate the mechanisms of cognitive impairment in MHE. It first elucidates the neuropathological basis of MHE from the perspective of functional connectivity abnormalities, particularly within networks such as the default mode network and the executive control network. Building on this foundation, it further synthesizes the progress in model construction and validation for intelligent MHE diagnosis using machine learning and deep learning methods (e.g., support vector machines and graph neural networks). Furthermore, it explores the emerging trend of integrating multimodal imaging with artificial intelligence, addressing the gaps present in previous reviews. This article aims to provide a systematic imaging basis for elucidating the neural mechanisms of MHE and to offer new research directions for achieving quantitative and precise diagnosis of MHE. Future research endeavors should focus on multi-center, large-sample validation, multimodal data fusion, and in-depth exploration of pathways for clinical translation.

Premature ovarian insufficiency (POI) refers specifically to a clinical syndrome characterized by significant decline or even complete loss of ovarian physiological function in women under 40 years of age.POI not only markedly reduces female fertility potential,but also impairs patients' mental health and diminishes their quality of life if intervention is not administered in a timely manner. Therefore,early identification and standardized diagnosis and treatment are crucial for improving the prognosis of POI patients.With the continuous innovation of ultrasound technology,it plays an irreplaceable core role in the clinical diagnosis of POI.This paper systematically reviews the evaluation value of conventional ultrasound parameters (such as antral follicle count, ovarian volume and ovarian stromal arterial blood flow parameters),as well as the technical principles and clinical applications of ultrasound elastography techniques (including shear wave elastography and strain elastography) and their key parameters (such as strain ratio and Young's modulus). It focuses on sorting out the efficacy differences and synergistic potential of ultrasound technologies in the early screening and disease assessment of POI,aiming to provide a reference basis for clinically improving the accuracy of early POI diagnosis and optimizing diagnosis and treatment strategies.