With the extension of life expectancy, population aging and the increasing healthcare demands of elderly patients have become pressing realities. Aged patients often experience diminished physiological reserve and progressive deterioration across multiple systems including physiological, metabolic, and sensory functions resulting in reduced tolerance to intravenous anesthetics. Ciprofol is a novel non-barbiturate intravenous anesthetic independently developed and recently approved in China. Existing evidence indicates that ciprofol exhibits characteristics such as rapid onset, faster recovery, reduced injection pain, higher potency, and a wider safety window. However, most studies to date are small, single-center investigations lacking rigorous evidence grading, particularly in the context of clinical application in elderly patients. Based on current research, this review summarizes the clinical use of ciprofol in the elderly population, with the aim of supporting its rational administration in this demographic and proposing directions for future research.

Lung cancer, characterized by its complex and diverse etiology and subtle early clinical symptoms, has led to a consistently high incidence rate and a continuous increase year after year.CT imaging technology, due to its extremely high detection rate and ability to non-invasively and conveniently identify suspected lung lesions, is currently the best method for early lung cancer screening. This review will systematically elaborate on the advantages and disadvantages of two-dimensional reconstruction technology and three-dimensional reconstruction technology in CT images, and focus on the analysis of the clinical application value of three-dimensional reconstruction technology in the diagnosis and treatment of pulmonary nodules in chest CT images.The purpose is to select the most suitable reconstruction method for the diagnosis of clinical lung diseases, and to provide a reference for the innovation of three-dimensional reconstruction technology of lung nodules in chest CT in the future.

Objective To evaluate the relationship between ultrasound-derived fat fraction (UDFF) and non-contrast multislice helieal computed tomography (MSCT) CT values, aiming to estimate hepatic fat content using UDFF. Methods A total of 140 patients from Beijing Tsinghua Changgung Hospital were prospectively enrolled between February 2024 AND June 2024. Based on the order of admission, the first 110 patients were assigned to the training set and the subsequent 30 patients to the validation set. All patients underwent both ultrasonography and upper abdominal MSCT within 7 days. The training set was categorized into a normal group (hepatic fat content<5.0%, n=68), a mild fatty liver group (hepatic fat content≥5.0% and <10.0%, n=30), and a moderate-to-severe fatty liver group (hepatic fat content≥10.0%, n=12). UDFF and CT values were measured in the right hepatic lobe using regions of interest (ROI) analysis. Correlation between UDFF and CT values was assessed, and comparisons were made among different fatty liver groups. Diagnostic performance of UDFF was evaluated using ROC curve analysis. Results In the training set, UDFF showed a strong negative correlation with CT values (r=-0.738, P<0.001), with statistically significant differences among groups (P<0.001). The UDFF values in the moderate-to-severe fatty liver group were significantly higher than those in the mild fatty liver and normal groups and normal groups (P<0.001). For diagnosing mild fatty liver, the area under the curve (AUC) was 0.661 (P=0.011、95% CI: 0.536~0.785), with a sensitivity of 43.3%, specificity of 85.3%, and a cutoff value of 6.1%. For moderate-to-severe fatty liver, the AUC was 0.921 (P<0.001、95% CI: 0.809~1.032), with a sensitivity of 91.7%, specificity of 93.3%, and a cutoff value of 10.4%. In the validation set, UDFF demonstrated an accuracy of 71.4%, sensitivity of 57.1%, specificity of 76.2% for mild fatty liver diagnosis, and an accuracy of 88.9%, sensitivity of 100%, specificity of 85.7% for moderate-to-severe fatty liver diagnosis. Conclusion UDFF exhibits a strong negative correlation with CT values in the quantitative diagnosis of fatty liver, and demonstrates high accuracy in diagnosing moderate-to-severe fatty liver.

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Neoadjuvant chemotherapy (NAC) has become an essential component of contemporary breast cancer treatment. However, conventional methods for assessing NAC efficacy are often limited by subjectivity and suboptimal accuracy, underscoring the urgent need for more objective and reliable evaluation strategies. In recent years, AI, particularly radiomics and deep learning, has driven significant advances in medical imaging analysis. MRI-based radiomics combined with DL has demonstrated the ability to extract high-dimensional features from imaging data that are invisible to the human eye. These features can capture subtle microstructural alterations within tumors, characterize biological phenotypes, and quantify intratumoral heterogeneity, thereby substantially improving the precision of treatment response evaluation. This review highlights recent progress in the application of MRI-based AI technologies for predicting NAC response in breast cancer, aiming to facilitate the translation of these techniques from theory to clinical practice and to provide a scientific foundation for advancing precision and personalized oncology care.

Objective To explore the value of a deep learning-based ultrasound radiomics nomogram in predicting Ki-67 expression levels in invasive breast cancer. Methods A retrospective single-center study was conducted, collecting complete preoperative clinical data and ultrasound images from 465 patients with pathologically confirmed invasive breast cancer at the First Affiliated Hospital of Bengbu Medical University from January to December 2024. Image acquisition was performed using Mindray Resona 7 and Samsung HS60 color Doppler ultrasound systems. Based on immunohistochemical results, patients were divided into high and low Ki-67 expression groups and randomly assigned to training (n=326) and validation (n=139) cohorts at a 7:3 ratio. ITK-SNAP software was used to segment tumors from the largest 2D ultrasound cross-sectional images, with interobserver consistency of ROI delineation assessed by ICC. Pyradiomics was employed to extract radiomics features from tumor tissues, and four deep learning networks were pretrained to construct clinical, ultrasound radiomics, fusion, and combined nomogram models. Diagnostic performance and clinical utility were evaluated using ROC curves, calibration curves, and decision curve analysis. Results Nineteen optimal ultrasound radiomics features and the DenseNet121 deep learning model showed the best performance (P<0.05). In the training cohort, the AUCs for the clinical model, ultrasound radiomics model, deep learning model, fusion model, and nomogram were 0.79 (95% CI: 0.74-0.84), 0.85 (95% CI: 0.81-0.90), 0.87(95% CI: 0.83-0.91), 0.94(95% CI: 0.91-0.97), and 0.95(95% CI: 0.93-0.98), respectively. In the validation cohort, the corresponding AUCs were 0.76(95% CI: 0.68-0.84), 0.78 (95% CI: 0.70-0.85), 0.81(95% CI: 0.74-0.88), 0.91(95% CI: 0.86-0.96), and 0.93(95% CI: 0.89-0.98). Conclusion The deep learning-based ultrasound radiomics nomogram can effectively predict Ki-67 expression in invasive breast cancer.

Breast cancer is the malignant tumor with the highest incidence in women worldwide. Therefore, early detection and early diagnosis are the keys to prolong survival, but there are limitations in traditional imaging diagnostic methods. In recent years, artificial intelligence (AI) technology has significantly improved the accuracy and efficiency of breast cancer imaging diagnosis through deep learning and image processing. In breast ultrasound, mammography, Breast MRI and emerging imaging technologies, AI can promote the development of medical imaging through lesion detection, classification, image enhancement, risk prediction and clinical decision support. However, the clinical translation of AI still faces challenges such as data standardization, algorithm generalization, and ethical compliance. In the future, it is necessary to strengthen multi-center cooperation, promote technological innovation, and improve ethical regulations, to ensure that it can truly meet clinical needs, so as to promote the intelligence, precision and universality of breast cancer diagnosis and treatment. This paper provides a systematic review of the comparative advantages and limitations of AI versus conventional imaging methods in the diagnosis, treatment, and prognosis prediction of breast diseases. It explores feasible pathways for clinical translation and future development directions, while also offering insights into the prospective applications of AI in breast disease diagnosis and treatment.

Deep vein thrombosis (DVT) is a condition caused by abnormal blood clotting within deep veins. As a common peripheral vascular disease, its annual incidence rate is approximately 0.1%. DVT fragments can easily lead to pulmonary embolism. Additionally, approximately 20%-50% of patients develop post-thrombotic syndrome later on, severely impacting their quality of life. Therefore, early recognition and management of DVT are crucial for preventing complications such as pulmonary embolism. This review summarizes various imaging diagnostic techniques for DVT, including DSA, US, CT, and MRI, alongside deep learning-based medical image analysis methods for thrombus detection. The aim is to provide evidence-based guidance for DVT prevention and treatment.

Objective To investigate the diagnostic value of 3.0T MRI T2 mapping combined with diffusion tensor imaging (DTI) in peripheral neuropathy associated with type 2 diabetes mellitus (DPN). Methods A prospective cohort of 42 patients with DPN hospitalized from January 2021 to April 2024 (DPN group), along with 29 healthy controls (HC group), was enrolled. All participants underwent lower limb nerve T2 mapping, DTI, and whole-body diffusion-weighted imaging with background suppression on a 3.0T MRI system. Quantitative parameters were measured, including T2 values of the sciatic and tibial nerves, DTI metrics, quadriceps T2 values, the T2 ratio of sciatic nerve to quadriceps at the same level, and cross-sectional areas of the sciatic and tibial nerves. Group differences were assessed, and diagnostic performance was evaluated using ROC curve analysis. Results Compared with the HC group, patients with DPN exhibited significantly increased T2, axial diffusivity (AD), and radial diffusivity (RD) values of the sciatic and tibial nerves (P<0.001), while fractional anisotropy (FA) and relative anisotropy (RA) values were decreased (P<0.001). Within the DPN group, significant differences were observed between the sciatic and tibial nerves in T2, FA, RD, and RA values (P<0.05), whereas AD values showed no significant difference (P>0.05). No significant intra-group differences were found in the HC group (P>0.05). The quadriceps T2 values and sciatic nerve-to-muscle T2 ratios were higher in the DPN group compared to HC (P<0.05). The cross-sectional areas of both sciatic and tibial nerves also differed significantly between groups (P<0.05). ROC curve analysis revealed that the AUC of T2 values for the sciatic and tibial nerves was 0.864 and 0.726, respectively; for FA values, the AUC was 0.825 and 0.800; and for combined T2 and FA values, the AUC was 0.895 and 0.822, respectively. Conclusion 3.0T MRI T2 mapping combined with DTI enables effective quantitative assessment of diabetic peripheral neuropathy. A diagnostic model integrating T2 and FA values provides superior diagnostic accuracy. This approach not only reveals fat infiltration and water content changes in the quadriceps, but also evaluates morphological alterations of lower limb nerves in DPN patients, offering reliable imaging evidence for early diagnosis and disease assessment.

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related deaths globally. Occult lymph node metastasis poses a significant challenge to the diagnosis, treatment planning, and prognosis of NSCLC patients. Early detection of occult lymph node metastasis through imaging can help select the optimal treatment method, thereby improving patient outcomes and ultimately contributing to reducing NSCLC-related mortality. This review discusses recent advancements in imaging features and radiomics for the early identification of occult lymph node metastasis in NSCLC, as well as the interpretability methods of radiomics.

Breast cancer ranks among the most prevalent malignancies in women globally, with consistently high incidence and mortality rates. Accurate assessment of axillary lymph node (ALN) status critically informs clinical decision-making and prognosis prediction. Although ALN dissection and sentinel lymph node biopsy remain the diagnostic gold standard, both procedures carry inherent limitations including surgical invasiveness and false-negative rates. Consequently, precise preoperative prediction of ALN status remains an unmet clinical need. Techniques such as mammography, CT, and breast MRI have advanced non-invasive evaluation. Radiomics and deep learning methodologies are now integral to ALN metastasis research in breast cancer. Notably, radiomics and deep learning models based on CT and MRI demonstrate robust performance in detecting ALN metastasis, achieving significant AUC values. This article systematically reviews recent advances in CT and MRI radiomics for predicting axillary lymph node metastasis in breast cancer.

Objective To investigate the value of a radiomics model integrating multimodal MRI and mammography (MG) for predicting axillary lymph node (ALN) metastasis in breast cancer. Methods A retrospective study was conducted, enrolling 575 breast cancer patients from The Xuzhou Central Hospital and The Huai'an First People's Hospital from January 2021 to May 2024. By comparing clinical indicators between the ALN metastasis-positive and negative groups, two clinical factors significantly associated with ALN metastasis (P<0.05) were identified: clinical T stage and lymph node palpation findings. Based on lesion segmentation from dynamic contrast-enhanced MRI (DCE-MRI) and MG images, radiomic features were extracted and screened, retaining 24 features with non-zero coefficients. Three Random Forest models were constructed based on features from the MRI tumor region (MRI_Tumor), the MRI lymph node region (MRI_LN), and the MG tumor region (MG_Tumor). These three models were then combined to form a clinical factor-free radiomics model (All_Imaging_Fusion). Finally, by integrating clinical T stage and lymph node palpation, a comprehensive multimodal radiomics model (All_Imaging+Clinical_Fusion) was developed. Results The multimodal radiomics model (All_Imaging+Clinical_Fusion) demonstrated excellent predictive performance, with AUC values of 0.943, 0.931, and 0.911 in the training, internal validation, and external validation cohorts, respectively. It significantly outperformed models based on any single imaging modality (P<0.05). Other predictive efficacy metrics also confirmed its superiority. Decision curve analysis indicated that this model provides a high clinical net benefit. Conclusion The multimodal radiomics model, which integrates radiomic features from DCE-MRI and MG with key clinical factors, can non-invasively and effectively predict ALN metastasis in breast cancer, offering a novel approach for guiding individualized treatment strategies.

Objective To assess whether ⁶⁸Ga-fibroblast activation protein inhibitor (⁶⁸Ga-FAPI)-04 PET/CT and its uptake parameters can serve as an effective supplement to magnetic resonance imaging (MRI) for preoperative T?staging of rectal cancer. Methods We retrospectively enrolled 124 patients with pathologically confirmed rectal cancer (verified by preoperative biopsy or postoperative histopathology) treated at our hospital from October 2023 to October 2025. Among them, 83 underwent preoperative MRI, 66 underwent preoperative ⁶⁸Ga?FAPI?04 PET/CT, and 25 completed both imaging examinations. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of the two modalities for each T stage were compared. The 66 patients who underwent PET/CT were categorized into three groups (T1/T2, T3, and T4) according to pathological T stage. Tumor uptake parameters, including SUV_max, SUV_mean, MTV, TLU, and TBR, were measured, and the intergroup differences were compared. Multiple regression analysis, Spearman correlation, and ROC curves were used to evaluate the correlations between uptake parameters and T stage, as well as their diagnostic performance in distinguishing T stages. Results MRI showed higher specificity for T2 staging and higher sensitivity for T4 staging than ⁶⁸Ga?FAPI?04 PET/CT (specificity: 88.30% vs 68.60%, P=0.011; sensitivity: 87.50% vs 28.60%, P=0.041). All PET/CT uptake parameters differed significantly across T stages (P<0.05) and correlated with T stage. TLU was identified as an independent predictor for differentiating T1/T2 lesions (OR=0.912, P=0.022). Each uptake parameter could distinguish T1/T2 from T3 tumors with an AUC greater than 0.70. Combined multi?parameter analysis achieved superior diagnostic performance, with a maximum AUC of 0.922. Conclusion MRI remains the primary imaging modality for preoperative T?staging of rectal cancer. ??Ga?FAPI?04 PET/CT serves as an effective supplement to MRI. Its uptake parameters aid in distinguishing T1/T2 from T3 lesions, and combined multi?parameter evaluation yields optimal diagnostic performance.

With the advancement of artificial intelligence (AI) technology, its applications in ultrasound medicine have gradually expanded from image analysis to multiple clinical stages, including image acquisition, quality control, lesion detection, and interventional navigation, demonstrating potential value in improving diagnostic efficiency and reducing variability among operators. At the same time, ethical concerns are becoming increasingly prominent, such as opaque algorithmic decision-making processes, data privacy risks, health equity issues arising from model biases, and unclear accountability. These challenges are particularly evident in ultrasound medicine, a field highly dependent on operator experience and dynamic image interpretation. Based on an overview of typical AI applications and current research developments, this article provides a comprehensive analysis of these ethical challenges and explores possible solutions from perspectives including model optimization, clinical application standards, medical education, and governance mechanisms. Overall, AI is currently better suited as an auxiliary tool in clinical decision-making, and its effective implementation depends on improved model interpretability, refined multi-party responsibility frameworks, and continuous enhancement of physicians' professional competence. Moving forward, it is essential to advance technological innovation and ethical governance in tandem to further enhance the safety, transparency, and applicability of AI systems, thereby promoting their standardized use in ultrasound medicine.

Objective To systematically evaluate the efficacy and safety of ultrasound-guided sphenopalatine ganglion block for the treatment of allergic rhinitis (AR), with a focus on its short-term, cumulative, and long-term clinical improvements in young AR patients with high disease prevalence and frequent healthcare-seeking behavior, and elucidate the underlying neuromodulatory mechanisms. Methods A retrospective analysis was conducted on 50 AR patients who underwent ultrasound-guided bilateral sphenopalatine ganglion block at the Department of Ultrasound Medicine, Affiliated Hospital of Shaanxi University of Chinese Medicine, from November 2024 to September 2025. Each patient received four treatment sessions (one course). Efficacy and safety were assessed using the Visual Analog Scale (VAS) and the Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ). Symptom and quality-of-life scores were recorded at baseline and at 10 min, 30 min, 24 h, and 7 days post-treatment, as well as at 6 months after completion of the full course. Individual efficacy was evaluated based on the improvement rate of symptom scores. Results Twenty-seven patients completed the full treatment course; the remaining patients were evaluated based on data from individual treatment sessions. A total of 256 block procedures were performed, with 6 failures, yielding a puncture success rate of 97.7%. At each time point following the first, third, and fourth treatments, as well as at the 6 month follow-up, both the VAS scores and the RQLQ domain scores (including nasal congestion, rhinorrhea, nasal itching, sleep disturbance, and emotional problems) were significantly decreased compared with baseline (P<0.05), with both scales showing a similar downward trajectory. At 30 min after the first treatment, the effective rates for nasal congestion and rhinorrhea were 34% and 62%, respectively, rising to 81% and 89% after the full course. At the 6-month follow-up after the full course, 55% of patients achieved sustained symptom relief, with scores for nasal congestion, rhinorrhea, sleep, and emotions decreasing to 3.2 (2.2, 4.3), 4.3 (2.9, 5.3), 4.0 (3.0, 5.0), and 5.0 (4.0, 7.0), respectively, all significantly below the baseline values of 5.3 (3.7, 6.9), 6.8 (5.3, 8.0), 6.0 (4.0, 7.5), and 7.0 (4.0, 12.0). Four adverse events were reported during the treatment period, with an incidence of 3.1%, and no serious adverse events occurred. Conclusion Ultrasound-guided sphenopalatine ganglion block demonstrates good short-term and cumulative clinical benefit in the treatment of allergic rhinitis in young patients with elevated disease prevalence and frequent clinic visits. It provides rapid relief of nasal congestion and rhinorrhea, leads to sustained improvement in quality of life after the full course.

Objective To analyze the MRI features and risk factors of recurrent anal fistula. Methods The clinical data of 120 patients with anal fistula who underwent surgical treatment at the First Affiliated Hospital of Anhui University of Chinese Medicine from May 2016 to May 2025 were retrospectively analyzed. According to the occurrence of anal fistula, the patients were divided into the recurrent group (n=40) and the newly diagnosed group (n=80). MRI features and general data of the two groups were comparatively analyzed. Multivariate logistic regression was used to analyze the influencing factors of recurrent anal fistula. Results The proportions of patients with high anal fistula, number of fistulas>1, course of disease>1 year, and history of anorectal surgery in the recurrent group were higher than those in the newly diagnosed group (P<0.05). Multivariate logistic regression analysis showed that high anal fistula (OR=1.471, 95% CI: 1.177-1.839), number of fistulas>1 (OR=1.527, 95% CI: 1.209-1.928), course of disease>1 year (OR=1.508, 95% CI: 1.221-1.864), and history of anorectal surgery (OR=1.496, 95% CI: 1.201-1.864) were risk factors for recurrent anal fistula (P<0.05). Conclusion Recurrent anal fistula exhibits certain features on MRI. The location of anal fistula, number of fistulas, course of disease, and history of anorectal surgery are factors influencing recurrent anal fistula. Targeted prevention and control measures should be developed based on above factors to reduce postoperative recurrence risk.

The incidence of thyroid cancer has been steadily increasing in recent years. Fine?needle aspiration cytology (FNAC) remains the gold standard for preoperative diagnosis, yet conventional manual interpretation suffers from low efficiency, high subjectivity, and an increased workload for pathologists. The rapid advances in deep learning over the past few years have provided a new avenue to overcome these limitations. This review systematically summarizes the current status and future perspectives of artificial intelligence (AI)?based image analysis in thyroid FNAC. It highlights the technical principles of mainstream models such as U?Net and Transformer, outlines the supportive role of publicly available datasets (e.g., the PTC Dataset), and synthesizes the progress made by both domestic and international research teams in cell classification, benign?malignant differentiation, and computer?aided diagnosis. Existing studies demonstrate that AI-based models attain a diagnostic accuracy of over 96% in differentiating benign cells from TBSRTC V+ categories (comprising suspicious for papillary thyroid carcinoma and malignant). Moreover, the diagnostic performance of these models has been shown to be comparable to that of senior pathologists, with a marked improvement in inter-observer agreement among less experienced cytopathologists. Nevertheless, current research still faces challenges including lightweight model deployment, multi?center generalizability, and the determination of legal liability. With ongoing model optimization and the refinement of regulatory frameworks, AI is expected to become an indispensable adjunctive tool in thyroid FNAC diagnosis, although its complete replacement of human cytopathologists will require cautious further exploration.

Chronic wounds are characterized by prolonged healing duration, high recurrence rates, and substantial treatment costs. The healing process is influenced by multiple factors, including tissue perfusion, inflammatory responses, and the local microenvironment. Accurate and timely wound assessment is essential for optimizing therapeutic strategies and improving patient outcomes. Traditional evaluation methods primarily rely on scoring systems and clinical experience, which often fall short in detecting deep tissue changes, microcirculatory abnormalities, and early risk signals. This review summarizes the applications of three-dimensional photogrammetry, near-infrared imaging, infrared thermography, fluorescence imaging, and laser speckle contrast imaging in chronic wound assessment. It compares their assessment dimensions, clinical scenarios, and implementation limitations, and examines technological complementarity and the roles of artificial intelligence in data standardization, longitudinal prediction, and smart nursing, providing a basis for objective assessment, risk identification, and continuous management.

Objective To develop and evaluate a nomogram model integrating multimodal imaging radiomics features and clinical factors for distinguishing molecular subtypes of human epidermal growth factor receptor 2 (HER-2) negative breast cancer. Methods A retrospective analysis was conducted on 141 patients with pathologically confirmed breast cancer who underwent preoperative MRI, mammography, and ultrasound examinations at the First Affiliated Hospital of Bengbu Medical University from January 2020 to May 2025. The patients were randomly divided into a training cohort (n=98) and a test cohort (n=43) at a ratio of 7:3. Radiomics features were extracted from craniocaudal (CC) and mediolateral oblique (MLO) views of mammography, the largest cross-sectional images of ultrasound masses, DWI, FS-T2WI, and the third phase of DCE-MRI. The optimal radiomics features were selected and incorporated into three machine learning classifiers to establish radiomics models. The best-performing algorithm was selected to generate the radiomics score (Rad-score). Univariate and multivariate logistic regression analyses were performed to identify independent clinical predictors, which were combined with the Rad-score to construct an integrated nomogram model. The predictive performance of each model was evaluated using ROC curves. Results Thirteen optimal radiomics features and two independent clinical predictors were finally selected. Among the three machine learning classifiers, the random forest algorithm demonstrated the best performance, with AUC values of 0.883 (95% CI: 0.824-0.941) and 0.877 (95% CI: 0.788-0.966) in the training and test cohorts, respectively. The integrated nomogram model combining the Rad-score and clinical predictors achieved superior diagnostic performance compared with the clinical model and multimodal radiomics model, with AUC values of 0.958 (95% CI: 0.919-0.997) in the training cohort and 0.891 (95% CI: 0.813-0.970) in the test cohort. Conclusion The nomogram model based on clinical factors combined with multimodal imaging radiomics features can accurately distinguish low-expression and zero-expression subtypes of HER-2 negative breast cancer. This model may provide valuable guidance for individualized clinical decision-making and treatment planning.

Objective To investigate the prognostic value of chest CT combined with serological indexes [hypersensitive C-reactive protein (hs-CRP), D-dimer (D-D), procalcitonin (PCT)] in patients with severe pneumonia. Methods A total of 80 patients with severe pneumonia and 60 patients with common pneumonia in the Sixth People's Hospital of Nantong were enrolled as severe group and common group from November 2020 to August 2024, respectively. All patients underwent chest CT plain scan, and levels of hs-CRP, D-D and PCT were detected. According to 28d survival, patients in severe group were divided into death group (n=29) and survival group (n=51). CT scores, hs-CRP, D-D and PCT in different groups were compared, and their prognostic value in severe patients was evaluated. Results CT score, levels of hs-CRP, D-D and PCT in severe group were higher than those in common group (P<0.05). APACHE II score at admission, proportions of infection strains≥2 kinds, disorder of consciousness, organ dysfunction≥3, septic shock and mechanical ventilation, CT score, levels of hs-CRP, D-D and PCT in death group were higher than those in survival group (P<0.05). High APACHE II score at admission, organ dysfunction ≥3 and high CT score were independent risk factors of 28 d prognosis (death) in patients with severe pneumonia (P<0.05). The area under the curve (AUC) values of CT score, hs-CRP, D-D and PCT for predicting prognosis of patients with severe pneumonia were 0.714 (95% CI: 0.592-0.836), 0.686 (95% CI: 0.564-0.808), 0.727 (95% CI: 0.607-0.847) and 0.734 (95% CI: 0.619-0.850), respectively. AUC of combined detection with the above indexes was 0.886 (95% CI: 0.809-0.964), greater than that of single index (P<0.05). Conclusion Chest CT combined with hs-CRP, D-D and PCT can effectively predict prognosis and guide the formulation of clinical protocols in patients with severe pneumonia.

Objective To investigate the correlation between subnuclear DNA distribution and breast cancer metastasis via stochastic optical reconstruction microscopy (STORM). Methods Low metastatic breast cancer MCF7 cells and high metastatic breast cancer MDA-MB-231 cells were selected as the research subjects. On this basis, TGFβ was supplemented to establish a breast cancer metastasis model, whereas breast cancer differentiation models were constructed via treatment with two distinct drug combinations: Tazemetostat/Ipatasertib and Rosiglitazone/PD98059, respectively. The efficacy of model establishment was evaluated by MMP9 expression and wound healing assay. The subnuclear distribution of DNA was analyzed by STORM with fluorescence labeling of histone H3, DNA repair protein OGG1 and dsDNA staining. Results In low metastatic breast cancer MCF7 cells, approximately 60% of DNA was distributed in peripheral region of the cell nucleus, whereas only 35% of DNA was localized in the corresponding area in high metastatic breast cancer MDA-MB-231 cells (P=0.0025). In addition, the distribution of DNA in the breast cancer metastasis model aggregates the center of nucleus (P=0.0102), while the distribution of DNA in the breast cancer differentiation model aggregates towards peripheral region of the cell nucleus (P=0.0005, 0.0013). Conclusion This study visualized the subnuclear DNA distribution in breast cancer cells with varying metastatic potentials via STORM, uncovered the intrinsic correlation between subnuclear DNA distribution and breast cancer metastasis, and indicated that this distribution could act as a promising diagnostic indicator for breast cancer metastasis.

Objective To analyze middle cerebral artery atherosclerotic plaque characteristics using magnetic resonance vessel wall imaging (MR-VWI) and to explore their association with pre-infarction hemodynamic status. Methods A total of 82 patients with middle cerebral artery atherosclerotic stenosis treated at The First Affiliated Hospital, Fujian Medical University, were retrospectively enrolled. Based on cerebral MR perfusion imaging, patients were divided into normal group (n=44) and pre-infarction group (n=38). Clinical data and plaque characteristics were collected and compared between the two groups. Logistic regression analysis was performed to investigate the relationship between plaque characteristics and pre-infarction perfusion abnormalities. Results No significant differences were observed in clinical data, plaque area, or plaque thickness between the two groups (P>0.05). Compared with the normal group, the pre-infarction group showed higher plaque enhancement grade (P=0.003), lower collateral vessel status score (P=0.002), higher prevalence of negative remodeling (P=0.031), a greater proportion of concentric plaques (P=0.001), and larger plaque burden (P<0.001). Multivariate logistic regression analysis identified plaque morphology and plaque burden as independent risk factors for pre-infarction perfusion abnormalities (P<0.05). The area under the curve (AUC) for plaque morphology, plaque burden, and their combination was 0.675 (95% CI: 0.555-0795), 0.874 (95% CI: 0.797-0.952), and 0.904 (95% CI: 0.832-0.976), respectively. The combined model demonstrated superior predictive performance compared with either parameter alone. Conclusion Plaque morphology and plaque burden are independent risk factors for cerebral perfusion abnormalities in the pre-infarction stage. The combination of these two plaque characteristics provides improved prediction of pre-infarction perfusion abnormalities, which may facilitate early identification of high-risk patients and provide imaging-based evidence for clinical decision-making.

Pancreatic solid pseudopapillary neoplasm (pSPN) is a rare low-grade malignant pancreatic tumor. Surgical resection is currently the preferred treatment method. Although the overall prognosis is good, 2.6%-15% of patients will still experience recurrence or metastasis. Imaging is not only used for early diagnosis of pSPN, but also plays an important role in assessing tumor biological behavior, predicting prognosis, and guiding treatment decisions and follow-up. This article systematically explains the application progress of commonly used imaging technologies in the prognosis diagnosis and treatment evaluation of pSPN, and explores the correlation between multi-modal imaging features and clinical prognosis, providing directions for future research.

Objective To develop a deep learning-based super-resolution reconstruction framework for small hepatocellular carcinoma (sHCC) in magnetic resonance imaging,designed to synergistically enhance lesion conspicuity and diagnostic utility by jointly optimizing high-frequency detail preservation and anatomical fidelity in thin-slice diffusion-weighted imaging (DWI). Methods A retrospective study was conducted on 3-mm DWI data from 300 patients with sHCC admitted from December 2022 to June 2024. The dataset was randomly divided into training and test sets at an 8:2 ratio. A dual-branch super-resolution model was constructed, comprising a content branch for extracting global features through cascaded gradient Transformer blocks and a gradient branch for enhancing structural information via gradient Transformer blocks. The model incorporated two novel components: (1) a cross local-enhanced self-attention module to optimize interactions between pixel-level features and global context, and (2) a channel-spatial joint attention layer that dynamically allocates weights to improve the visibility of key anatomical structures. Three senior radiologists blindly evaluated both original (OR-DWI) and super-resolved (SR-DWI) images using a 5-point Likert scale. Objective metrics, including the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), were also calculated. Results SRDWI demonstrated significantly superior subjective ratings compared to ORDWI across multiple evaluation metrics? (P<0.001), including hepatic nodule signal specificity (3.41±0.53 vs 2.47±0.50), normal liver parenchyma homogeneity (3.29±0.47 vs 2.78±0.42), artifact interference severity (2.56±0.52 vs 2.47±0.48), and overall image quality (3.15±0.49 vs 2.67±0.48). Although post-super-resolution processing resulted in a marginally increased image noise level (3.98±0.61 vs 2.87±0.46, P<0.05), the values remained well within clinically acceptable limits without compromising diagnostic interpretation of key features. Quantitative analysis further confirmed SRDWI's excellent performance, achieving a peak signal-to-noise ratio of 34.65489 dB and structural similarity index of 0.90365. Conclusion The proposed deep learning framework effectively bridges the diagnostic performance gap between thin-slice (3 mm) and conventional thick-slice DWI protocols, achieving comparable lesion characterization accuracy while mitigating motion artifacts. This advancement establishes a robust technical foundation for precision diagnosis of early-stage sHCC, where subtle anatomical details critically influence therapeutic decision-making.

Objective To investigate the apllication value of deep learning image reconstruction (DLIR) algorithms in optimising the images quality of low-kV head and neck CT angiography (CTA). Methods Sixty patients who underwent head and neck CTA in the Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine Hospital from October 2024 to February 2025 were analyzed. The scanning was performed with 80 kV and smart mA (50-400 mA). The contrast agent dosage was 40 mL, the flow rate was 4 mL/s, and the injection time of contrast agent was 10 s. After scanning, 40% adaptive iterative reconstruction algorithm-V (ASIR-V40%), low intensity DLIR (DLIR-L), medium intensity DLIR (DLIR-M) and high intensity DLIR (DLIR-H) were reconstructed respectively. The CT and SD values of the aortic arch, 2 cm above the opening of the common carotid arteries bilaterally, segment V1 of the vertebral arteries, proximal basilar artery, segment M1 of the middle cerebral artery, and temporalis muscle were measured, and signal-to-noise ratios (SNR) and contrast-to-noise ratios (CNR) were calculated. The images were subjectively scored by two radiologists using a five-point scale in a double-blind method. The SD value, CT value, CNR value, SNR value and subjective score of the four reconstruction algorithms were compared. Results There was no statistically significant difference in CT values for each vessel between the four reconstruction algorithms (P>0.05). As the reconstruction levels of ASIR-V40%, DLIR-L, DLIR-M and DLIR-H gradually increased, the images showed a decreasing trend in SD, and an increasing trend in CNR and SNR.Among them, DLIR-H has the lowest SD value and the highest CNR and SNR. Moreover, there are statistically significant differences in the SD, SNR, and CNR values between DLIR-L, DLIR-M, DLIR-H and ASIR-V40% (P<0.05).Compared with ASIR-V40%, DLIR-H Reconstruction the SNR and CNR of the aortic arch, 2 cm above the common carotid artery opening, V1 segment of the vertebral artery, proximal basilar artery, and M1 segment of the middle cerebral artery in DLIR-H increased by approximately 69%, 48.3%, 48.3%, 51.6%, 56.4% and 68%, 55.9%, 48.2%, 51.6%, 56.6% respectively, and the SD values decreased by approximately 40.2%, 35.8%, 30.6%, 33.2%, 35.3% respectively.The subjective scores of DLIR-H, DLIR-M and DLIR-L were all higher than those of ASIR-V40%, and the differences were statistically significant (P<0.05). Conclusion Compared with ASIR-V at 40%, DLIR reconstruction can further reduce image noise and improve image quality in head and neck CTA. Therefore, the DLIR algorithm can be used to enhance the image quality of low-kV head and neck CTA.

Objective To explore the correlation between lateralized motor manifestations and multimodal neuroimaging alterations of deep cerebral nuclei in patients with Parkinson's disease (PD). Methods A total of 40 patients with Parkinson's disease and 36 gender- and age-matched healthy control (HC) subjects were retrospectively enrolled from the Second Affiliated Hospital of Xinjiang Medical University from December 2024 to December 2025. Clinical data of all enrolled patients (demographic characteristics, motor and non-motor symptoms, and medication regimens) were collected. PD were divided into the left-onset Parkinson's disease (LPD) group and the right-onset Parkinson's disease (RPD) group according to the affected side of motor symptom onset. Gray matter volume and magnetic susceptibility value (MSV) values in different nuclei were compared among groups, and the correlations between nuclear parameters and clinical scales were analyzed. Results No significant differences were observed in age, gender or education level among the three groups (P>0.05). Disease duration and MoCA scores were similar between LPD and RPD groups (P>0.05). Compared with the HC group, the PD group showed significantly increased MSV in the substantia nigra, globus pallidus, and putamen (P<0.05). The left substantia nigra MSV was significantly higher in the RPD group than in the LPD group, and the right substantia nigra MSV was significantly higher in the LPD group than in the RPD group (P<0.05). Compared with the HC group, the PD group exhibited significantly reduced gray matter volume in the substantia nigra, globus pallidus, and putamen (P<0.05), and the putamen volume reduction was more prominent in the RPD group than in the LPD group (P<0.05). Correlation analysis showed that bilateral MSV in the globus pallidus was significantly positively correlated with UPDRS-III scores (left:r=0.833,right:r=0.859,P< 0.01). MSV in the substantia nigra was also significantly positively correlated with UPDRS-III scores, with a stronger correlation for the right substantia nigra (right: r=0.761, P<0.01; left: r=0.317, P<0.05). Conclusion Asymmetric iron accumulation in the substantia nigra contralateral to symptomatic limb serves as a core pathological marker of motor lateralization in PD, and multi-modal MRI enables noninvasive evaluation of pathological injury and motor impairment.

Objective To assess the predictive efficacy of the plasma atherogenic index (AIP) in conjunction with the Gensini score for identifying coronary functional ischemia, characterized by a computed tomography fractional flow reserve (CT-FFR) of ≤0.80. Methods A total of 215 consecutive patients with suspected coronary artery disease were recruited for this study, all of whom underwent both coronary angiography and CT-FFR evaluation at the First Affiliated Hospital of Bengbu Medical University from January 2025 to January 2026. Clinical data and lipid profiles were collected, and the AIP was determined using the formula log₁₀ (TG/HDL-C). The extent of coronary stenosis was measured using the Gensini score, while functional ischemia was defined as the lowest CT-FFR value of ≤0.80 among the three principal coronary arteries. Patients were divided into an ischemic group (CT-FFR≤0.80, n=156) and a non?ischemic group (CT-FFR>0.80, n=59). Between?group differences in clinical characteristics, AIP and Gensini scores were compared. Univariate and multivariate logistic regression analyses were performed to assess the associations of AIP and Gensini score with coronary functional ischemia. The predictive performance of AIP, Gensini score, and their combination was evaluated using ROC curves and the DeLong test; Net reclassification improvement and integrated discrimination improvement were used to assess improvements in clinical risk stratification and diagnostic accuracy. Results Compared with the non?ischemic group, the ischemic group showed significantly higher AIP and Gensini scores (P<0.05). Multivariate logistic regression identified AIP, Gensini score, and age as independent predictors of coronary functional ischemia. ROC curve analysis revealed that the AUC for AIP alone, Gensini score alone, and their combined model were 0.703, 0.705, and 0.790, respectively. The DeLong test indicated that the combined model exhibited a significantly higher AUC compared to the AIP alone (P=0.009) and the Gensini score alone (P=0.006). Additionally, analyses of the net reclassification improvement and integrated discrimination improvement further substantiated that the combined model surpassed each individual indicator in terms of risk stratification and diagnostic accuracy. Conclusion Both the AIP and Gensini score are strongly correlated with coronary functional ischemia and act as independent predictors. Their integration markedly enhances the predictive performance for coronary functional ischemia.

Objective To explore the changes of white matter and hippocampus structure and their relationship with cognitive function in elderly patients with type 2 diabetes mellitus (T2DM) based on MRI. Methods A total of 136 elderly patients with T2DM admitted to First People's Hospital of Tianshui were enrolled as T2DM group from March 2023 to December 2025, while 68 healthy elderly volunteers with matched age and genders during the same period were enrolled as control group. All underwent 3.0T cranial MRI scan to measure the volume of white matter hyperintensities (WMH), bilateral hippocampi and hippocampal subfields. The cognitive function was evaluated by mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA). The differences in brain structure indexes and cognition scores between the two groups were compared. The correlation between brain structure indexes and cognitive function was analyzed by Pearsoncorrelation analysis. The influencing factors of cognitive impairment were analyzed by multiple linear regression analysis. Results WMH volume in T2DM group was larger than that in control group, while volumes of bilateral hippocampi and hippocampal subfields (CA1, CA2-3, dentate gyrus) were smaller than those in control group (P<0.05). The total scores and scores of MMSE and MoCA in T2DM group were lower than those in control group (P<0.05). In elderly patients with T2DM, volume of WMH was negatively correlated with scores of MMSE and MoCA (r=-0.582, -0.613, P<0.05), while volume of bilateral hippocampi was positively correlated with them (r=0.624, 0.657, P<0.05). Increased WMH volume, decreased hippocampal volume and increased glycosylated hemoglobin were independent risk factors of cognitive impairment in elderly patients with T2DM (P<0.05). Conclusion There is significant microstructural injury of white matter and hippocampal atrophy in elderly patients with T2DM, and these structural changes are closely related to decline of cognitive function. The brain structure parameters under MRI can be applied as important evaluation indexes for the risk of cognitive impairment in elderly patients with T2DM.