To investigate the ability of an auxiliary diagnostic model based on the YOLO-v7-based model in the classification of cervical lymphadenopathy images and compare its performance against qualitative visual evaluation by experienced radiologists. Three types of lymph nodes were sampled randomly but not uniformly. The dataset was randomly divided into for training, validation, and testing. The model was constructed with PyTorch. It was trained and weighting parameters were tuned on the validation set. Diagnostic performance was compared with that of the radiologists on the testing set. The mAP of the model was 96.4% at the 50% intersection-over-union threshold. The accuracy values of it were 0.962 for benign lymph nodes, 0.982 for lymphomas, and 0.960 for metastatic lymph nodes. The precision values of it were 0.928 for benign lymph nodes, 0.975 for lymphomas, and 0.927 for metastatic lymph nodes. The accuracy values of radiologists were 0.659 for benign lymph nodes, 0.836 for lymphomas, and 0.580 for metastatic lymph nodes. The precision values of radiologists were 0.478 for benign lymph nodes, 0.329 for lymphomas, and 0.596 for metastatic lymph nodes. The model effectively classifies lymphadenopathies from ultrasound images and outperforms qualitative visual evaluation by experienced radiologists in differential diagnosis.

Objective: Improvement in regional blood flow has been shown to ameliorate diabetic gastroparesis. We compared the gastric blood supply in patients with diabetes with gastroparesis with that in healthy subjects, by using contrast-enhanced ultrasound (CEUS). Methods: 30 healthy subjects and 40 patients with diabetic gastroparesis were enrolled. The CEUS parameters of greater curvatures of the antrum (GCOA) and lesser curvatures of the antrum (LCOA), including peak intensity (PI) and the area under the curve (AUC), were compared between the two groups. Results: Intraclass correlation coefficient (ICC) for PI in healthy subjects measured on CEUS were 0.831–0.857 and 0.803–0.823, respectively. Intra-ICC and inter-ICC values for AUC were 0.805–0.823 and 0.813–0.815, respectively. In both groups, no significant difference was observed in PI and AUC values of GCOA and LCOA (p > 0.05). The PI and AUC of GCOA and LCOA in the diabetes group were less than those in the normal group (p < 0.05). Conclusion: CEUS can assess stomach wall vascularity with a high reproducibility. Microcirculation in the antrum of patients with diabetic gastroparesis is poorer than that of normal group, which is consistent with the mechanisms of diabetic neuropathy. CEUS can be used for evaluation of microvascular perfusion in patients with stomach wall disease. Advances in knowledge: This was the first study to use CEUS for assessment of blood supply of the gastric wall and to compare microvascular perfusion between healthy individuals and patients with diabetes with gastroparesis.

To establish and validate a nomogram for predicting lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) in the cervical central region. This retrospective study included 287 PTC patients with 309 nodules treated from December 2018 to May 2020 at our hospital. The cohort was divided randomly into a training set and a testing set according to a 7:3 ratio. The training set contained 216 nodules, and the testing set contained 93 nodules. The nomogram was developed using the training set, and the data of the testing set were used to validate the performance of nomogram. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index) and calibration curve. The study showed multifocality, thyroid lesion size, and American College of Radiology Thyroid Imaging, Reporting and Data System (TI-RADS) score were significantly independently associated with LNM in the cervical central region. In the testing set, the calibration curve showed that the nomogram had good discrimination with a C-index of 0.775 (95% confidence interval, 0.680–0.869) and adequate calibration (P = 0.808). By decision curve analysis and clinical impact curve analysis, the nomogram was shown to have a satisfactory net benefit between thresholds of 0.40 and 0.75. The nomogram can be used for predicting LNM of PTC in the cervical central region and may provide valuable guidance for planning the surgical treatment of PTC patients.

The aim of this study was to develop a prognostic model for radioactive iodine (RAI) therapy outcome in patients with Graves‘ disease. We enrolled 127 patients. Information on RAI therapy, ultrasound indexes of thyroid, and other lifestyle factors was collected. The competing risk model was used to estimate the multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for nonhealing or recurrence of hyperthyroidism (NHRH). The performance of the model was assessed by receiver operator characteristic analysis and the Brier score and internally validated by bootstrap resampling. Then, a nomogram was developed. Forty-one cases (32.2%) of NHRH were documented. Positive Ki-67 expression, a higher dose of per-unit thyroid volume, and females showed lower risks of NHRH (all P < 0.05). The HR values (95% CI) were 0.42 (0.23, 0.79), 0.01 (0.00, 0.02), and 0.47 (0.25, 0.89), respectively. The bootstrap validation showed that the model had the highest accuracy and good calibration for predicting cumulative risk of NHRH at 180 days after RAI therapy (AUC = 0.772; 95% CI: 0.640–0.889, Brier score = 0.153). By decision curve analysis, the nomogram was shown to have a satisfactory net benefit between thresholds of 0.20 and 0.40. Ki-67, ultrasound volumetry, and scintigraphy techniques can play important roles in evaluating RAI therapy outcome in Graves‘ disease patients. The prediction nomogram shows reasonable accuracy in predicting NHRH.

Background: Early detection of breast cancer and accurate assessment of lesions are key goals of imaging evaluation. Ultrasound is widely used, but its diagnostic performance is influenced by complex image features, noise, and operator experience. Reducing operator dependence and improving accuracy are critical clinical issues. Methods: In this retrospective study, 7,025 breast ultrasound images from our center were annotated based on pathology and split into training, validation, and internal test sets (8:1:1). The Dataset of Breast Ultrasound Images was used as the external test set. YOLO-v7 and YOLO-v8 models were trained through transfer learning after data augmentation and balancing the classes. Performance was compared on internal and external test sets and was evaluated against a reader study. Results: YOLO-v7 and YOLO-v8 reached optimal performance at epochs 294 and 135, respectively. YOLO-v7 slightly outperformed YOLO-v8 on the internal test set, while YOLO-v8 achieved higher accuracy, recall, specificity, precision, and F1 score on the external test set. Both models showed significantly higher accuracy, specificity, and precision than the senior radiologist, with YOLO-v8 achieving a significantly higher F1 score. Discussion: YOLO-v8 demonstrated better generalization due to its anchor-free mechanism and deeper architecture, while YOLO-v7 showed signs of overfitting. Both models outperformed the junior radiologist and approached or exceeded the diagnostic performance of the senior radiologist, indicating potential to assist less experienced readers. Conclusion: YOLO-v7 and YOLO-v8 effectively classified breast lesions. YOLO-v8 showed faster convergence and higher diagnostic efficiency, suggesting strong potential for clinical application.

Amid rising diabetes prevalence, the burden of diabetes-related chronic kidney disease (DKD) is escalating, particularly in China. This study aimed to describe the epidemiological trends of DKD in China and develop a Bayesian age–period–cohort (BAPC) model to predict its incidence from 2022 to 2036. Data from the Global Burden of Disease Study 2021 (GBD 2021) were used to assess DKD’s burden in China between 1990 and 2021, including age-standardized incidence (ASIR), prevalence (ASPR), mortality (ASMR), and disability-adjusted life years (DALYs). BAPC models were applied to evaluate age, period, and cohort effects and to project future trends. Between 1990 and 2021, the ASIR for chronic kidney disease due to diabetes mellitus type 1 (DKD1) decreased (annual percent change (APC) = -0.57, P < 0.001), while ASPR increased (APC = 1.25, P < 0.001). ASMR and DALYs both declined (APC = -1.98, P < 0.001; APC = -1.99, P < 0.001). For chronic kidney disease due to diabetes mellitus type 2 (DKD2), ASIR slightly increased (APC = 0.44, P < 0.001), but both ASPR and ASMR decreased (APC = -0.23, P = 0.004; APC = -0.56, P < 0.001). Projections suggest an increase in DKD2 incidence and declining incidence for DKD1 by 2036. This study provides insights into the trends of DKD burden in China over the past three decades. The BAPC offers a reliable forecast for the next 15 years, aiding future research to reduce DKD’s impact in China.