This paper proposes a two-stage method integrating visual foundation models （VFM） and diffusion models. The segment anything model （SAM） as VFM is combined with the SegRefiner diffusion model to construct the SAM-SegRefiner framework for automated segmentation of edema， inflammation， and thrombus regions in histopathological images of hemorrhoidal tissue， providing a reproducible technical tool for the objective quantification of pathological morphology and its application in traditional Chinese medicine （TCM） syndrome research. Trained and validated on multi-center retrospective data， the SAM-SegRefiner model achieved an average pixel accuracy of 95.32% and a mean intersection over union （mIoU） of 66.81% on an independent test set， significantly outperfor-ming comparative models such as U-Net， MixU-Net， and SAM-Med2D， and also demonstrating robust cross-center generalization capability. Furthermore， by correlating the quantitatively segmented results from the model with the patients' TCM syndrome types， the potential associations between pathomorphological features and TCM syndrome differentiation have been explored. The analysis revealed no statistically significant differences in the degree of inflammatory infiltration and thrombus formation among different syndrome types， suggesting a complex relationship between local pathological changes and systemic syndrome manifestations.

Objective: To evaluate the diagnostic potential of fluid suppression (FS) techniques combined with amide proton transferweighted imaging (APTw) for assessment of isocitrate dehydrogenase (IDH) mutation status, glioma subtypes, and tumor proliferation. Materials and Methods: This retrospective study included 117 patients with adult-type diffuse gliomas. Conventional APTw, FSAPTw, and spillover-corrected FS-APTw (SCFS-APTw) metrics were calculated from 3T MRI data in tumor parenchyma, necrotic or cystic regions, and contralateral normal-appearing white matter. APTw signals were compared across the three techniques within each region, between IDH-mutant and IDH-wildtype gliomas, and among astrocytoma, oligodendroglioma, and glioblastoma subtypes. Diagnostic performance for distinguishing IDH mutation status and glioma subtypes was assessed using receiver operating characteristic (ROC) analysis. Correlations between APTw metrics and Ki-67 expression were also analyzed. Results: Both FS-APTw and SCFS-APTw significantly reduced signal intensity in tumor parenchyma and necrotic or cystic regions, with SCFS-APTw demonstrating a stronger suppression effect. IDH-wildtype gliomas showed significantly higher APTw metrics than IDH-mutant gliomas (all P < 0.001). Glioblastomas exhibited significantly higher metrics than oligodendrogliomas (all corrected P ≤ 0.015) and astrocytomas (all corrected P < 0.001). SCFS-APTw achieved the highest area under the ROC curve (AUC) of 0.846 for identification of IDH mutation status. For differentiation between astrocytomas and glioblastomas, the 90th percentile of APTw yielded the highest AUC (0.860), followed by SCFS-APTw (0.849). For discrimination between oligodendrogliomas and glioblastomas, the highest AUC (0.832) was obtained using the 90th percentile of SCFS-APTw. None of these metrics successfully differentiated oligodendrogliomas from astrocytomas. SCFS-APTw demonstrated the strongest correlation with Ki-67 expression (r = 0.451). Conclusion FS techniques effectively reduce elevated APTw signals originating from fluid compartments. Their combination with APTw imaging enables evaluation of IDH mutation status, glioma subtypes, and tumor proliferative activity in adulttype diffuse gliomas.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objective:To investigate the spatial distribution pattern of local tumor progression (LTP) for hepatocellular carcinoma (HCC) ≤5 cm after microwave ablation.Methods:A retrospective analysis was performed on 169 HCCs with matched MRI before and after ablation from December 2009 to December 2019. A tumor MRI was reconstructed using three-dimensional visualization technology. LTP was classified as contact or non-contact, early or late stage, according to whether LTP was in contact with the edge of the ablation zone and the occurrence time (24 months). The tumor-surrounded area was divided into eight quadrants by using the eight-quadrant map method. An analysis was conducted on the spatial correlation between the quadrant where the ablative margin (AM) safety boundary was located and the quadrant where different types of LTP occurred. The t-test, or rank-sum test, was used for the measurement data. 2-test for count data was used to compare the difference between the two groups.Results:The AM quadrant had a distribution of 54.4% LTP, 64.2% early LTP stage, and 69.1% contact LTP, suggesting this quadrant was much more concentrated than the other quadrants ( P ?

Objective:To evaluate the therapeutic effect of ultrasound-guided microwave ablation (MWA) in the treatment of breast cancer liver metastases (BCLM), and to select the beneficiaries.Methods:A retrospective study was performed in 63 patients with BCLM who underwent ultrasound-guided microwave ablation in Department of Interventional Ultrasound, the Fifth Medical Center of Chinese PLA General Hospital, and had detailed follow-up data from February 2010 to October 2022. General patient data and laboratory results were collected. Univariate and multivariate Cox regression analyses were performed to determine the independent influencing factors for prognosis in patients with BCLM. A nomogram model was established to predict the survival rates of patients after operation.Calibration curve and decision curve were plotted to evaluate the calibration degree and the clinical benefit of the model, respectively. Time-dependent ROC curve was plotted to evaluate the predicting ability of the model. All patients were divided into high-risk group and low-risk group according to prognostic index. Survival curves were plotted to compare differences in survival between the two groups.Results:Multivariate Cox regression analysis showed that tumor burden score(TBS) and the presence of extrahepatic metastasis before MWA were independent influencing factors for prognosis of BCLM(all P<0.001). The combined prediction model was established based on TBS, extrahepatic metastasis and axillary nodal metastases. The areas under the 1-year, 2-year and 3-year ROC curves were 0.849, 0.855 and 0.878, respectively, suggesting that the model had good predicting ability. The calibration curve showed that the model had good calibration degree, while the decision curve showed that the model had good clinical practicability. The median survival time between the two groups was statistically significant[13.5 (9, 20) months vs 63.0 (39, 140) months, P<0.001]. Conclusions:Low-risk patients with BCLM gain significant clinical benefits after microwave ablation treatment, while high-risk patients do not exhibit a noticeable survival advantage.

Objective:To evaluate the therapeutic effect of ultrasound-guided microwave ablation (MWA) in the treatment of breast cancer liver metastases (BCLM), and to select the beneficiaries.Methods:A retrospective study was performed in 63 patients with BCLM who underwent ultrasound-guided microwave ablation in Department of Interventional Ultrasound, the Fifth Medical Center of Chinese PLA General Hospital, and had detailed follow-up data from February 2010 to October 2022. General patient data and laboratory results were collected. Univariate and multivariate Cox regression analyses were performed to determine the independent influencing factors for prognosis in patients with BCLM. A nomogram model was established to predict the survival rates of patients after operation.Calibration curve and decision curve were plotted to evaluate the calibration degree and the clinical benefit of the model, respectively. Time-dependent ROC curve was plotted to evaluate the predicting ability of the model. All patients were divided into high-risk group and low-risk group according to prognostic index. Survival curves were plotted to compare differences in survival between the two groups.Results:Multivariate Cox regression analysis showed that tumor burden score(TBS) and the presence of extrahepatic metastasis before MWA were independent influencing factors for prognosis of BCLM(all P<0.001). The combined prediction model was established based on TBS, extrahepatic metastasis and axillary nodal metastases. The areas under the 1-year, 2-year and 3-year ROC curves were 0.849, 0.855 and 0.878, respectively, suggesting that the model had good predicting ability. The calibration curve showed that the model had good calibration degree, while the decision curve showed that the model had good clinical practicability. The median survival time between the two groups was statistically significant[13.5 (9, 20) months vs 63.0 (39, 140) months, P<0.001]. Conclusions:Low-risk patients with BCLM gain significant clinical benefits after microwave ablation treatment, while high-risk patients do not exhibit a noticeable survival advantage.

Objective To investigate the QCC activities to reduce the effect of noise on the ward. Methods Formulate and adopt the measures by setting up QQC team to analyze the factors of the ward noise. Results Ward noise reduction rate was more than 80%, including all types of wheel noise reduction rate was up to 92.31%, the family talking loudly and the eachinstrument noise were respectively 88.24% and 88.10%. Conclusion The application of QQC reduces the ward’s noise, improves patient’s satisfaction and nursing quality.

Objective To explore the effect of nursing intervention on compliance of Budesonide/Formoterol inhalation in patients with COPD.Methods 135 patients with COPD were randomly divided into observation group and control group.68 cases in the observation group inhaled Budesonide /Formoterol and were additional given comprehensive nursing interventions;67 cases in the control group inhaled Budesonide /Formoterol alone.The compliance and effect were detected before and after treatment.Results After intervention,the Budesonide /Formoterol in-halation rate in the observation group was significantly improved.Conclusion The nursing inter-vention can raise the compliance of inhalation treatment of Budesonide /Formoterol in COPD pa-tients and improve the lung function of the patients.

Objective To explore the effect of nursing intervention on compliance of Budesonide/Formoterol inhalation in patients with COPD.Methods 135 patients with COPD were randomly divided into observation group and control group.68 cases in the observation group inhaled Budesonide /Formoterol and were additional given comprehensive nursing interventions;67 cases in the control group inhaled Budesonide /Formoterol alone.The compliance and effect were detected before and after treatment.Results After intervention,the Budesonide /Formoterol in-halation rate in the observation group was significantly improved.Conclusion The nursing inter-vention can raise the compliance of inhalation treatment of Budesonide /Formoterol in COPD pa-tients and improve the lung function of the patients.