Objective To develop a novel PET tracer, ¹⁸F-JR-1002, targeting the cannabinoid type 2 receptor (CB2R), to enhance molecular imaging capabilities for CB2R-related diseases. Methods The synthesis of a PET tracer targeting cannabinoid type 2 receptor (N-(4-(diethylamino)benzyl)-4-(2-(fluoro-¹⁸F) ethoxy)-N-(p-tolyl) benzenesulfonamide) was designed and prepared stably and efficiently on the automatic synthesizer, denoted as ¹⁸F-JR-1002. Cell affinity and specificity experiments, in vitro stability experiments, whole-body dynamic and static scanning of mice, and spleen autoradiography were carried out. Results The decay-corrected radiochemical yield was 34.7%±8.1% (n=12), corresponding to a molar activity Am of 264.5±41.2 GBq/μmol. Stability tests showed that ¹⁸F-JR-1002 had excellent in vitro stability. The cellular uptake assays and spleen tissue autoradiography showed that ¹⁸F-JR-1002 exhibits high binding affinity and specific imaging capability for CB2R. In mice, most of the ¹⁸F-JR-1002 probes were concentrated in the small intestine, followed by obvious uptake in the liver, kidneys, and bladder. The spleen and lungs had relatively low uptake, and the bones basically did not take up the tracer. According to dynamic scanning data, the PET tracer accumulated in the liver and kidneys in the early stage. Over time, the uptake of the tracer in the small intestine and bladder increased significantly and then stabilized. Conclusion This study developed a high-affinity, specific and metabolically stable CB2R probe ¹⁸F-JR-1002, which improved the accuracy and reliability of CB2R imaging and provided more accurate imaging support for clinical practice.

Objective To evaluate the diagnostic performance of individual parameters and combined models utilizing ¹⁸F-FDG PET, routine blood tests, biochemistry, and MRI for differentiating atypical primary central nervous system lymphoma (PCNSL) from high-grade glioma (HGG). Methods A retrospective analysis included 49 patients (25 HGGs, 24 PCNSLs) admitted to the Department of Nuclear Medicine, Second Hospital of Lanzhou University from January 2016 to September 2024, and they were stratified into HGG and PCNSL groups. Lesions were delineated using 3D Slicer to derive PET semi-quantitative parameters. Intergroup differences in age, gender, lesion number/location, MRI features, routine blood/ biochemical parameters, and PET metabolic parameters were compared. Parameters showing statistically significant differences were identified. ROC curves were generated for both individual parameters and combined models to assess diagnostic performance. Results No statistically significant differences were observed between groups for age, gender, lesion number, lymphocyte count, albumin, or metabolic tumor volume (P>0.05). Significant differences (P<0.05) were found for lesion location, enhancement features, neutrophil count, mean standardized uptake value (SUV_mean), minimum standardized uptake value (SUV_min), and maximum standardized uptake value (SUV_max). The corresponding AUC were 0.608, 0.854, 0.613, 0.885, 0.833, 0.923, respectively. Combining lesion location, enhancement features, and neutrophil count with either SUV_max, SUV_min, SUV_mean yielded significantly higher AUCs of 0.993, 0.985, 0.993, respectively, outperforming all single-parameter models. Conclusion The combined diagnostic model integrating PET, routine blood tests, and MRI demonstrate outstanding performance in distinguishing atypical PCNSL from HGG, highlighting its significant potential for clinical translation and application.

Objective To develop a fusion model that integrates multiparametric magnetic resonance imaging (mpMRI)-based radiomics features with clinical and pathological variables for predicting lymph-vascular space invasion (LVSI) in patients with endometrial cancer. Methods This retrospective study included 96 patients with pathologically confirmed endometrial cancer treated at Northwest Women's and Children's Hospital from January 2015 to June 2024. Axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were used to manually delineate both tumor lesions and corresponding uterine body regions. radiomics features were extracted from the delineated regions. clinical and pathological variables were screened using univariate analysis and significant predictors were integrated with imaging features to construct a fusion model. model performance was evaluated using leave-one-out cross-validation. Results The AUC for DWI-based radiomics models reached 0.84 for tumor lesions and 0.87 for the uterine body, while the T2WI-based models yielded AUCs of 0.82 and 0.84, respectively. multivariate logistic regression identified age, CA199 and Ki67 expression as independent predictors of LVSI (P<0.05), with the combined clinical-pathological model achieving an AUC of 0.834. The final fusion model, incorporating both radiomics and clinical-pathological features, achieved an AUC of 0.920, demonstrating superior predictive performance compared to single-modality models. Conclusion The integration of mpMRI-derived radiomics with key clinical and pathological factors significantly enhances the predictive accuracy for LVSI in endometrial cancer. this fusion approach may provide valuable support for accurate preoperative staging and the development of individualized treatment strategies.

Objective To investigate the comparative efficacy of T2 and T3 staging of rectal cancer based on conventional MRI signs and imaging histology. Methods A total of 272 patients with pathologically confirmed T2 and T3 stage rectal cancer were enrolled at the Lujiang Branch of the Tongji University Affiliated Oriental Hospital from December 2021 to December 2024, including 104 patients in the T2 stage and 168 patients in the T3 stage.First, we conducted a comparative analysis of the diagnostic efficacy of preoperative assessment for T2 versus T3 staging of rectal cancer based on multiparametric conventional MRI signs; Second, the enrolled patients were randomly divided into a training group (n=190) and a validation group (n=82) in a 7∶3 ratio; Imaging histological features associated with T2 and T3 staging of rectal cancer were extracted from non-lipid-suppressed T2-weighted sequences, diffusion-weighted imaging, and MRI-enhanced scanning images, respectively, to construct a joint imaging histology model;Finally, receiver operating characteristic curves were plotted for the multiparameter-based conventional MRI signs and the imaging histology model. The area under the curve, specificity, and sensitivity of the corresponding models were calculated to compare and analyze the efficacy of preoperative assessment for T2 and T3 staging of rectal cancer based on the two diagnostic modalities. Results Based on the diagnostic efficacy of conventional multiparametric MRI signs, the AUC was 0.905, the specificity and sensitivity were 0.917 and 0.894; while the highest efficacy was observed in the MRI imaging histology-based joint model training group,the AUC was 0.981,The specificity and sensitivity were 0.944 and 0.929, respectively;In the validation group, the AUC was 0.953, The specificity and sensitivity were 0.926 and 0.898, respectively; and the results were well-calibrated and statistically significant (P<0.05). Conclusion The efficacy of the preoperative assessment for T2 and T3 staging of rectal cancer using the MRI histology model is higher than that of traditional MRI signs, making it worthy of further promotion and application in clinical practice.

Objective To explore the clinical application of MRI radiomics in the diagnosis of triangular fibrocartilage complex (TFCC) injuries. Methods This retrospective study enrolled 96 (injury/non-injury: 62/34) patients who received arthroscopy in Binzhou Medical University Hospital from January 2019 to October 2024. Regions of Interest were delineated on multi-sequence MRI, followed by radiomic feature extraction. The dataset was randomly split into training and test sets at a ratio of 7:3.The best radiomics features were selected by LASSO algorithm, and C-support vector classification (CSVC), Nu-support vector classification (NuSVC), random forest (RF), logistic regression (LR), adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost) were used to establish the omics model respectively. The radiomics model was constructed using the optimal algorithm selected by AUC evaluation, while the same algorithm was employed to build the clinical model based on clinical data. Subsequently, both models were combined to develop a Nomogram prediction model. The diagnostic performance was assessed using ROC curve and decision curve analysis, followed by quantitative comparison of core indicators. Results The AUC values for the training and test sets of radiomics models constructed using six algorithms (CSVC, NuSVC, LR, RF, AdaBoost, and XGBoost) were as follows: 0.7568 and 0.8355; 0.8570 and 0.8421; 0.8052 and 0.8092; 0.8848 and 0.8026; 0.8202 and 0.8224; and 0.7926 and 0.7796, respectively. The clinical model achieved AUCs of 0.6890 (training) and 0.7417 (testing), compared to 0.9738 and 0.9250 respectively for the Nomogram model. The difference between Nomogram model and clinical model was statistically significant (P<0.05). The AUC of the Nomogram was higher than that of the radiomics model, but the difference was not statistically significant in the test set (P>0.05). Conclusion The radiomics model based on RF algorithm is superior to other algorithms in TFCC damage diagnosis. Compared with the clinical model and the omics model, the Nomogram model showed a more superior diagnostic efficiency in the diagnosis of TFCC injury.

Objective To investigate the application value of dynamic contrast-enhanced MRI with 3.0T magnetic resonance CAIPIRINHA-Dixon-TWIST-VIBE technique in the diagnosis of hepatic space-occupying lesions. Methods A total of 98 patients with liver space-occupying lesions confirmed by pathology in the hospital were collected from December 2022 to December 2024.After admission,all patients received MRI plain scan and CDT-VIBE dynamic enhanced scan to obtain multi-phase images (water phase,fat phase,same/opposite phase) combined with qualitative enhancement mode scan images (arterial phase,portal phase,delayed phase characteristics).The quantitative parameters of dynamic enhanced MRI [peak time (T_peak), transport constant (K^trans), rate constant (K_ep), signal-to-noise ratio (SNR),contrast-to-noise ratio (CNR)]were recorded.With pathology as the gold standard, the characteristics of MRI images and quantitative parameters of patients with different properties of liver space-occupying lesions were analyzed.Receiver operating characteristic curve was used to analyze the differential efficiency of dynamic enhanced MRI parameters on the properties of liver space-occupying lesions. Results Among the 98 patients with liver space-occupying lesions,45 cases were pathologically confirmed as benign lesions and 53 cases as malignant lesions. The proportion of high enhancement in the arterial phase, heterogeneous enhancement, capsular enhancement in the delayed phase and "fast-in and fast-out" model in malignant group were higher than those in benign group (P<0.05), and the marginal nodular enhancement, continuous enhancement and "progressive enhancement" model were more common in benign group (P<0.05). K^trans, K_ep, SNR and CNR in malignant group were higher than those in benign group (P<0.05), and Tpeak was shorter than that in benign group (P<0.05). ROC curve showed that the areas under the curves of K^trans, K_ep, T_peak, SNR, CNR for differentiating benign and malignant liver space-occupying lesions were 0.842 (95% CI: 0.754-0.908, P<0.001), 0.811 (95% CI: 0.720-0.883, P<0.001), 0.809 (95% CI: 0.717-0.882, P<0.001), 0.791 (95% CI: 0.697-0.866, P<0.001) and 0.768(95% CI: 0.671-0.847, P<0.001). K^trans had the highest efficiency, and when the cut-off value of K^trans was>0.28 min^-1, the Youden index, sensitivity and specificity were 0.570, 79.25%, 77.78%, respectively. Conclusion CDT-VIBE technique combined with qualitative enhancement model and quantitative perfusion parameters can help to identify benign and malignant liver space-occupying lesions. Malignant lesions show high enhancement in the arterial phase, heterogeneous enhancement, "fast in and fast out" and capsule enhancement, and increased K^trans and K_ep and shortened T_peak. K^trans value more than 0.28 min^-1 can be used as a reference value for the differential diagnosis of benign and malignant liver space-occupying lesions.

Objective To investigate alterations in thalamic functional connectivity and their clinical implications in healthy adults following 36-hour sleep deprivation (SD) using whole-brain resting-state functional magnetic resonance imaging (rs-fMRI). Methods Thirty healthy participants were recruited from Xuzhou Central Hospital from October 2023 to April 2024 for a 36-hour SD protocol with two rs-fMRI scans: baseline assessment during rested wakefulness and post-SD evaluation. Bilateral thalamic regions were selected as seed points for whole-brain functional connectivity analysis. Concurrent psychomotor vigilance tests (PVT) were administered to assess sustained attention performance. Comparative analyses of thalamocortical connectivity and behavioral data were conducted between pre- and post-SD conditions. Results Post-SD PVT performance demonstrated significantly increased error rates compared to baseline (P<0.001). rs-fMRI analysis revealed diminished functional connectivity between bilateral thalamic seeds and bilateral lingual gyri following SD, specifically: left thalamus-left lingual gyrus, left thalamus-right lingual gyrus, and right thalamus-left lingual gyrus connections (P<0.05). Notably, altered connectivity between the left thalamus and right lingual gyrus showed significant negative correlation with PVT lapse changes (post-SD vs pre-SD, P<0.05). Conclusion Thirty-six-hour SD significantly impairs thalamic functional connectivity, particularly manifesting as reduced thalamo-lingual gyrus connectivity. This neural mechanism may underlie observed deficits in visuospatial attention and memory maintenance following prolonged wakefulness.

Objective To construct and validate a machine learning model based on enhanced CT radiomics features to predict the expression status of immunohistochemical index P504S in renal cancer. Methods Clinical, pathological and imaging data of 117 patients with pathologically confirmed renal carcinoma and defined P504S expression status in the First Affiliated Hospital of Bengbu Medical University from January 2019 to September 2024 were collected and retrospectively analyzed; Three-dimensional radiomics features from contrast-enhanced CT of the lesions were extracted to establish a predictive model for distinguishing between P504S-negative and P504S-positive cases. All cases were randomly divided into a training set and a test set at a ratio of 7:3. 5-fold cross-validation was performed on the training set to select the optimal hyperparameters for establishing the predictive model, and the model using was validated the test set and the diagnostic performance of the model was analyzed using the ROC curve, calibration curve, and decision curve analysis. The region of interest was delineated based on the arterial and venous phases of CT scans. Data were normalized using Min-max normalization, and dimensionality reduction was performed through principal component analysis and Pearson similarity. The Relief algorithm was used for feature selection, and support vector machine and Naive Bayes were used as classifiers to construct the radiomics models for the arterial and venous phases, respectively. Results The radiomics model for the arterial phase achieved an AUC and accuracy of 0.801 and 0.805 on the training set and 0.833 and 0.743 on the test set, respectively. The radiomics model for the venous phase achieved an AUC and accuracy of 0.791 and 0.683 on the training set and 0.808 and 0.714 on the test set, respectively. The combined model for the arterial and venous phases achieved an AUC of 0.846 for all cases (95% CI: 0.768-0.906), slightly higher than the radiomics model for the arterial phase (0.804, 95% CI: 0.720-0.871) and the radiomics model for the venous phase (0.823, 95% CI: 0.742-0.887), but there was no statistically significant difference (P>0.05). Conclusion A machine learning model based on enhanced CT radiomics features of renal cancer can predict the expression status of immunohistochemical indicator P504S.

Objective To explore the value of a nomogram based on ultrasound features combined with clinical and pathological indicators in predicting sentinel lymph node metastasis (SLNM) risk in T1 breast cancer. Methods A retrospective analysis was conducted on 306 breast cancer patients pathologically confirmed at The First Affiliated Hospital of Xinjiang Medical University from January 2021 to December 2023. Patients were randomly divided into training and validation sets in a 7:3 ratio. Multivariate logistic regression was used to identify independent predictors of SLNM. A predictive model was established and visualized as a Nomogram. The model was validated using the validation set, calibration curves, ROC curves and decision curve analysis. Results Multivariate Logistic regression identified four independent predictors of SLNM: tumor aspect ratio, margin characteristics, axillary lymph node status, and Ki-67 expression status. The nomogram incorporating these indicators demonstrated good predictive performance, with AUC of 0.79 (95% CI: 0.72-0.86) in the training set and 0.83 (95% CI: 0.74-0.93) in the validation set. Calibration curves confirmed the model's accuracy. Conclusion The developed nomogram effectively predicts SLNM risk in T1 breast cancer patients. It may serve as a tool to identify patients who do not require sentinel lymph node biopsy and guide decisions on axillary lymph node dissection and adjuvant therapy.

Objective To investigate the clinical value of combined model based on clinical and double phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics features in predicting the expression level of Ki-67 in breast cancer. high predictive efficacy for Ki-67 expression in breast cancer. Methods A total of 155 patients with confirmed breast cancer at the First Affiliated Hospital of Anhui Medical University from January 2021 to August 2023 were retrospectively collected and categorized into high expression group (Ki-67≥30%, n=84) and low expression group (Ki-67<30%, n=71) according to postoperative immunohistoche-misty results. All cases were then randomly assigned to either a training set (n=108) or a test set (n=47) at a ratio of 7:3. The clinical model, the radiomics model(enhanced early and later phase), and the combined model were constructed using the selected clinico-radiological and radiomics features(enhanced early and later phase). ROC curves were used to evaluate the diagnostic efficacy of the four models. Calibration curves and decision curves were subsequently employed to assess the clinical utility of the predictive models. Results Ultimately, three clinico-radiological features and seven radiomics features were selected.The model constructed by combining the radiomics features with the clinico-radiological features showed various improvements in the effectiveness of Ki-67 expression prediction.The AUC of the combined model were 0.924 and 0.909 in the training and test sets, respectively. Calibration and decision curves showed that the combined model had promising clinical application. Conclusion The model based on double phase DCE-MRI radiomics features combined with clinico-radiological features has

Objective To compare the diagnostic value of Lung RADS and C Lung RADS in characterizing pulmonary nodules, and to evaluate the predictive performance of C Lung RADS in combination with 3D reconstruction features. Methods A retrospective analysis was conducted on clinical and 3D imaging data from 153 patients who underwent lobectomy for pulmonary nodules at our institution from January 2023 to January 2025. Independent predictive factors were identified using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. The diagnostic performance of Lung RADS and C Lung RADS was compared using Venn diagrams and confusion matrices. A combined predictive model was constructed and evaluated using ROC curves, calibration curves, Hosmer-Lemeshow goodness-of-fit tests, decision curve analysis, and clinical impact curves. Results Both systems showed high concordance in the high-risk and very-high-risk categories. Three-dimensional reconstruction features significantly differed between benign and malignant nodules (P<0.05), with average CT value and intranodular vascularity emerging as independent predictors. The diagnostic performance of three-dimensional features combined with either Lung RADS or C Lung RADS did not differ significantly (P_Delong=0.341), while the combined model significantly outperformed the three-dimensional features alone (P_Delong=0.020). Ultimately, the model integrating three-dimensional features with C Lung RADS was selected, yielding an AUC of 0.875 (95% CI: 0.786-0.963), a goodness-of-fit test (χ2=9.825, P=0.278), and demonstrated clinical net benefit within a risk threshold range of 0.02-0.58 based on decision curve analysis and clinical impact curves. Conclusion Lung RADS and C Lung RADS performed similarly in assessing pulmonary nodule malignancy risk, but C Lung RADS offered a more concise classification scheme. The combined model incorporating 3D reconstruction features and C Lung RADS demonstrated favorable predictive performance and holds promise for aiding in the preoperative differentiation of benign and malignant pulmonary nodules.

Objective To analyze the two-dimensional sonographic characteristics and blood flow distribution patterns of endometrial cancer (EC) using the consensus terminology of the International Endometrial Tumor Analysis (IETA), and explore the diagnostic value of combining IETA ultrasound features with tumor markers in predicting the pathological grading of EC. Methods A retrospective analysis was conducted on the ultrasound images and serum tumor marker levels of 147 EC patients at Affiliated Hospital of Qiaodao University from January 2020 to August 2024. According to the 2023 staging system of the International Federation of Gynecology and Obstetrics, the enrolled cases were divided into a low-grade group (n=104) and a high-grade group (n=43) based on histological types. Univariate and multivariate logistic regression analyses were performed to assess the diagnostic efficacy of individual indicators and combined multi-factor indicators. Diagnostic performance was evaluated using ROC curves, and the AUC was calculated. Results Univariate analysis showed statistically significant differences between the low-grade and high-grade groups in CA125, HE4, endometrium-myometrium junction morphology, endometrial echogenicity, blood flow scores, and lesion diameter (P<0.05). Multivariate logistic regression analysis identified CA125, endometrium-myometrium junction morphology, blood flow scores, and lesion diameter as independent risk factors for predicting high-grade EC (P<0.05). The combined multi-factor indicators showed a sensitivity of 69.80%, specificity of 82.70%, accuracy of 81.13%, and an AUC of 0.81, which was significantly higher than that of individual indicators. Conclusion The combination of IETA ultrasound features and CA125 demonstrates high diagnostic value in predicting the pathological grades of EC. It provides critical evidence for the early identification of high-grade EC patients.

Objective To investigate the clinical value of high-resolution magnetic resonance vessel wall imaging (HRMR-VWI) in the short-term prognosis and recurrence of patients with intracranial atherosclerotic stenosis (ICAS) type cerebral infarction. Methods A total of 175 hospitalized patients who were treated at the First Affiliated Hospital of Xinxiang Medical College from October 2023 to September 2024, met the diagnostic criteria for ICAS type cerebral infarction, and underwent HRMR-VWI were included. Patients were followed up for 90 days. According to their neurological functional recovery and the presence of stroke recurrence, they were divided into good prognosis group (mRS≤2) vs poor prognosis group (mRS>2) and stroke recurrence group vs non-recurrence group. Binary logistic regression, COX regression, and other methods were used to analyze the correlation between plaque parameters and adverse outcomes. Results Univariate analysis results showed that severe vascular stenosis, plaque enhancement, and moderate-to-severe stroke were more common in patients with poor prognosis, and their serum HbA1c levels were higher. In the stroke recurrence group, a history of diabetes, a history of stroke, plaque enhancement, and moderate-to-severe stroke were more common. Multivariate analysis found that severe vascular stenosis (P=0.027, OR=5.041, 95% CI: 1.199-21.191) and moderate-to-severe stroke (P<0.001, OR=104.048, 95% CI: 21.773-497.226) were independent risk factors for poor prognosis, while a history of stroke (P=0.023, HR=2.620, 95% CI: 1.140-6.023) and plaque enhancement (P=0.025, HR=5.381, 95% CI: 1.230-23.546) were independent risk factors for stroke recurrence. Conclusion Severe vascular stenosis and moderate-to-severe stroke were independent risk factors for 90-day poor prognosis in ICAS patients, while plaque enhancement and a history of stroke were independent risk factors for 90-day stroke recurrence in ICAS type stroke patients.

Objective To explore the dynamic changes in B-line counts at the posterolateral alveolar and/or pleural syndrome point (PLAPS point ) during the early stage of acute cerebral hemorrhage, to assess their diagnostic value in the early identification of neurogenic pulmonary edema (NPE). Methods This retrospective study analyzed 40 patients with acute cerebral hemorrhage who were admitted to Chongqing Seventh People's Hospital from January to October 2024, and they were categorized into the NPE group and the non-pulmonary edema group based on the presence or absence of NPE. All patients underwent bedside lung ultrasound scans at the PLAPS point, and B-line counts were recorded. Results The B-line count was significantly higher in the NPE group than in the non-pulmonary edema group (5.13±1.22 vs 2.32±1.41, P<0.01). ROC curve analysis showed that B-line counts at the PLAPS point had a sensitivity of 92.50% and a specificity of 86.42% for diagnosing NPE, with an optimal cutoff value of 3.5 lines. Additionally, in some patients with NPE, B-line counts at the left PLAPS point were generally higher than those at the right side. Conclusion B-line quantification at the PLAPS point offers high sensitivity and specificity for the diagnosis of NPE, making it particularly valuable for early detection in patients with acute cerebral hemorrhage. Left-sided B-line predominance may indicate greater fluid accumulation in the left lung, which could inform and refine ultrasound assessment strategies. Wider clinical adoption is recommended.

Objective To investigate and analyze the risk factors for recurrence of elderly colon polyps treated with endoscopic mucosal resection (EMR). Methods A total of 115 elderly patients with colon polyps were included in the study for analysis. They were divided into two groups based on whether they had postoperative recurrence, namely the non-recurrence group and the recurrence group. Clinical characteristics, general information, and surgical related information of the patients were collected based on an electronic medical record system. Logistic multivariate regression and univariate analysis were used to identify the factors affecting colon polyp recurrence after EMR surgery. Results Among the 115 patients, 29 cases relapsed, with a recurrence rate of 25.21%, and 86 cases (74.79%) did not relapse. Univariate analysis showed that there were statistically significant differences between the two groups in the types of colonic polyps, the number of polyps, BMI, gender, high-density lipoprotein, the proportion of adenomas, multiple sites, triglycerides, uric acid, type 2 diabetes, hypertension, non-alcoholic fatty liver disease, and age (P<0.05). There was no statistically significant difference in the location of polyps, drinking history, educational level, smoking history and family history between the two groups (P>0.05). Logistic multiple regression analysis showed that after EMR treatment for large colon polyps, the polyp type, polyp diameter BMI, Polyp location, hypertension, non-alcoholic fatty liver disease, and age are risk factors for recurrence (P<0.05). Conclusion For elderly patients with large colon polyps, although endoscopic mucosal resection is effective, there is a risk of recurrence, which includes risk factors such as polyp diameter, polyp type, and age. It is necessary to assess the patient's risk of recurrence, develop intervention measures, reduce the risk of recurrence, and ensure treatment effectiveness.

Alzheimer's disease (AD) is a neurodegenerative disorder that severely threatens human health, with a highly complex pathogenesis. It is generally believed that extracellular deposition of Aβ and intracellular aggregation of tau protein, which form NFTs, are key factors influencing the onset of AD. Medical imaging techniques, especially MRI, are considered essential tools in the study of brain diseases, particularly AD. However, conventional MRI techniques have certain limitations in the early diagnosis of AD, such as low sensitivity and difficulty in specifically targeting biomarkers associated with AD and other related diseases.Nanomaterials, when used as MRI contrast agents, can enhance the imaging signals of affected brain regions, enabling accurate diagnosis and imaging of AD. In addition, nanomaterials have demonstrated controlled-release properties, targeting capabilities, and the ability to help drugs cross the blood-brain barrier in MRI-guided drug delivery systems. These features offer new possibilities for AD treatment, even enabling synergistic effects between the nanomaterials, thus achieving both diagnostic and therapeutic benefits.This paper systematically reviews recent advancements in the combination of MRI and nanomaterials for the early diagnosis and treatment of AD. It also provides an in-depth outlook on the future development of MRI-nanomaterial integration in AD diagnosis and therapy.

Acute pulmonary embolism (APE) is a serious health-threatening syndrome characterized by an extremely high mortality and disability rate, therefore, early diagnosis, treatment and prognosis assessment of patients with APE are critical. Inflammatory response and coagulation processes are important links in the formation of embolism and are involved in this pathophysiological process. Pulmonary vascular remodeling is often correlated with the extent of pulmonary artery involvement and the degree of right heart dysfunction. Key imaging parameters, including the longest diameter of the right ventricular short axis to the longest diameter of the left ventricle short axis ratio, the Qanadli index, pulmonary artery diameter, and pulmonary artery dilation, are crucial for predicting poor prognosis in APE. This article aims to review recent advancements in clinical features, imaging parameters, serum biomarkers and their combinations for predicting the prognosis of patients with APE. The goal is to enhance the prognostic accuracy of APE, assist clinicians in better risk stratification and personalized treatment planning and ultimately improve patient outcomes.

The metal artifacts generated by the metal implants in the body will cover the anatomical structure and reduce the diagnostic accuracy in the postoperative review.Dual-energy CT virtual monoenergetic imaging combined with MAR technology can effectively reduce the influence of metal artifacts on CT image quality, but the optimal monoenergetic level for suppressing metal artifacts in different parts is different. Therefore, this article reviews the current research status of the optimal monochromatic energy levels for reducing metal artifacts in different locations, including intracranial aneurysm embolization, oral metal implants, spinal internal fixation, hip replacement, and other implants. This review aims to provide a scientific basis for clinical imaging practice, thereby optimizing imaging parameters and improving the accuracy of postoperative evaluation.

The incidence of papillary thyroid cancer (PTC) is increasing year by year, and accurate preoperative diagnosis plays a crucial role in clinical management and prognosis. Contrast-enhanced ultrasound is a non-invasive, real-time imaging technique that enhances tissue contrast through intravenous injection of ultrasound contrast agents, dynamically displaying microcirculation perfusion information of lesions and enabling qualitative and quantitative analysis. The integration of Contrast-enhanced ultrasound with novel technologies such as radiomics and deep learning holds promising potential in early disease diagnosis and treatment evaluation. This article reviews the principles of thyroid contrast-enhanced ultrasound, the application of contrast-enhanced ultrasound in the diagnosis of PTC, the evaluation of PTC treatment effects, and the research on related new technologies in contrast-enhanced ultrasound. It aims to provide guidance for the precise diagnosis and treatment practice of PTC.

Pulmonary thromboembolism is an umbrella term for a group of diseases or clinical syndromes caused by thrombus blockage of the pulmonary artery and its branches, which can lead to circulatory instability or sudden death without timely treatment, with a mortality rate as high as 30%. Energy-spectrum CT can make qualitative and quantitative judgment on tissues through its attenuation characteristics under different X-ray energies, and through post-processing technology, it can generate images such as base matter map, single-energy image, effective atomic number, and energy-spectrum curve at one time, which overcomes the limitations of traditional CT in characterizing tissues and allows for a more comprehensive analysis of the disease. In recent years, energy spectrum CT has been widely used in the diagnosis and risk stratification assessment of pulmonary embolism disease. Its multiparameter quantitative analysis has shown significant advantages in improving the accuracy of PE diagnosis and optimizing image quality. The purpose of this paper is to review the imaging principle of energy spectrum CT and its application value in pulmonary embolism disease.