Objective To explore the preoperative diagnostic value of a radiomics nomogram based on intratumoral and peritumoral apparent diffusion coefficient(ADC)maps for prostate cancer.Methods A retrospective collection was conducted on MRI images of 503 patients with prostate lesions confirmed by pathology.The region of interest(ROI)was delineated on the ADC maps and extended 1-5 mm outward to form the peritumoral region.Radiomics features were extracted from both intratumoral and peritumoral regions,and radiomics models were established.A combined model integrating clinical model was constructed and a nomogram was drawn.The performance of each model and nomogram were evaluated.Results The combined model achieved the highest area under the curve(AUC)in the test set(AUC=0.823)at a peritumoral distance of 3 mm.The nomogram based on the combined model showed good predictive performance and clinical utility on both decision curve analysis(DCA)and calibration curve.Conclusion The radiomics nomogram based on intratumoral and peritumoral ADC maps has the greatest diagnostic value in distinguishing benign and malignant prostate cancer at a peritumoral distance of 3 mm before surgery.

Objective To explore the preoperative diagnostic value of a radiomics nomogram based on intratumoral and peritumoral apparent diffusion coefficient(ADC)maps for prostate cancer.Methods A retrospective collection was conducted on MRI images of 503 patients with prostate lesions confirmed by pathology.The region of interest(ROI)was delineated on the ADC maps and extended 1-5 mm outward to form the peritumoral region.Radiomics features were extracted from both intratumoral and peritumoral regions,and radiomics models were established.A combined model integrating clinical model was constructed and a nomogram was drawn.The performance of each model and nomogram were evaluated.Results The combined model achieved the highest area under the curve(AUC)in the test set(AUC=0.823)at a peritumoral distance of 3 mm.The nomogram based on the combined model showed good predictive performance and clinical utility on both decision curve analysis(DCA)and calibration curve.Conclusion The radiomics nomogram based on intratumoral and peritumoral ADC maps has the greatest diagnostic value in distinguishing benign and malignant prostate cancer at a peritumoral distance of 3 mm before surgery.

Objective To explore the applicability,safety and effectiveness of hepatic arterial infusion chemotherapy(HAIC)combined with sintilimab and bevacizumab in the patients with intermediate and ad-vanced hepatocellular carcinoma(HCC)failed by lenvatinib treatment.Methods A total of 62 patients with intermediate and advanced liver cancer failed by lenvatinib treatment in this hospital from February 2023 to February 2024 were selected as the study subjects and divided into the HAIC group(n=13),HAIC+targeted therapy group(n=18)and HAIC+combined therapy group(n=31)according to different treatment regi-mens.The HAIC group only received the HAIC treatment,the HAIC+targeted therapy group adopted the HAIC+lenvatinib combined treatment and the HAIC+combined treatment group received the HAIC+sintil-imab and bevacizumab combined treatment.The objective remission rate(ORR),disease control rate(DCR),median progression-free survival time(mPFS),median overall survival time(mOS),carcinoembryonic antigen(CEA),vascular endothelial growth factor(VEGF),carbohydrate antigen 125(CA125)levels after treatment were observed in 3 groups,and the treatment related adverse reactions were recorded.The Kaplan-Meier method was used for conducting the survival analysis.Results According to RECIST1.1 criteria,ORR of the HAIC+combined treatment group was higher than that of the HAIC group and HAIC+targeted therapy group(35.5％vs.23.1％vs.22.2％),DCR was lower than that of the other two groups(83.8％vs.92.3％vs.88.9％),but the differences were not statistically significant and the differences were not statistically sig-nificant(P＞0.05).mOS of the HAIC+combined treatment group was longer than that of the HAIC group and HAIC+targeted therapy group[18.1(95％CI:13.3-22.9)months vs.12.6(95％CI:9.0-16.2)months vs.15.9(95％CI:11.5-20.3)months].mPFS in the HAIC+combined treatment group was longer than that in the the HAIC group and HAIC+targeted therapy group[12.0(95％CI:9.4-14.6)months vs.9.7(95％CI:2.2-17.2)months vs.10.1(95％CI:8.3-11.9)months],and the differences were not statistically sig-nificant(P＞0.05).The levels of CEA,VEGF and CA125 after treatment in 3 groups were decreased com-pared with before treatment,moreover the levels in the HAIC+combined treatment group was the lowest(P＜0.05).There was no statistically significant difference in the incidence rate of adverse reactions among the three groups(P＞0.05).Conclusion The HAIC combined with immunotherapy regimen is effective in the pa-tients with intermediate and advanced HCC failed by lenvatinib treatment,moreover which has high safety.

Purpose To explore the value of deep learning feature prediction based on the ResNet50 deep residual network model for predicting early hematoma expansion in spontaneous intracerebral hemorrhage without traditional imaging manifestations. Materials and Methods A retrospective study was performed on 235 patients with spontaneous intracerebral hemorrhage in Jiangdu People's Hospital Affiliated to Yangzhou University from January 2019 and December 2022. These patients had undergone their initial plain cranial CT scan within 6 hours of symptom onset and a subsequent follow-up scan within 24 hours of admission. They were randomly assigned to a training set consisting of 188 cases and a test set of 47 cases,using an 8︰2 ratio. The region of interest (ROI) of hematoma was traced layer by layer on the first plain head CT,and image genomics features were extracted. The maximum two-dimensional cross-sectional ROI of the hematoma 3D-ROI,as well as ROI images at 1 mm and 2 mm above and below the maximum two-dimensional cross-sectional ROI,were then cut and input into the pre-trained ResNet50 model for feature extraction. The image genomics features were then fused with the extracted deep learning features using a least absolute shrinkage and selection operator regression model. A support vector machine classifier was used to construct a prediction model,which was evaluated using receiver operating characteristic curves and decision curve analysis. Results In the training set,the area under curve (AUC) of the deep learning feature model was 0.972,which was higher than that of the image genomics feature model (0.951) and the fused feature model (0.968),but this difference was not statistically significant (P>0.05). In the testing set,the AUCs of the deep learning feature model and the fused feature model were 0.867 and 0.895,respectively,which were significantly higher than that of the image genomics feature model (0.833),with statistically significant differences (Z=-1.794,-2.191,both P<0.05). The AUC of the fused feature model showed an improvement compared to the deep learning feature model,but the difference was not statistically significant (P>0.05). In the test set,decision curve analysis revealed that the fused feature model yielded greater benefits compared to both the deep learning feature model and the radiomic feature model. Conclusion The deep learning feature model based on ResNet50 deep residual network shows better performance in predicting early hematoma expansion than the image genomics feature model,and the fused feature model has a beneficial effect on predicting hematoma expansion. This deep learning approach provides a prediction tool with supervisory capability for clinical decision-making.

Purpose To explore the value of deep learning feature prediction based on the ResNet50 deep residual network model for predicting early hematoma expansion in spontaneous intracerebral hemorrhage without traditional imaging manifestations. Materials and Methods A retrospective study was performed on 235 patients with spontaneous intracerebral hemorrhage in Jiangdu People's Hospital Affiliated to Yangzhou University from January 2019 and December 2022. These patients had undergone their initial plain cranial CT scan within 6 hours of symptom onset and a subsequent follow-up scan within 24 hours of admission. They were randomly assigned to a training set consisting of 188 cases and a test set of 47 cases,using an 8︰2 ratio. The region of interest (ROI) of hematoma was traced layer by layer on the first plain head CT,and image genomics features were extracted. The maximum two-dimensional cross-sectional ROI of the hematoma 3D-ROI,as well as ROI images at 1 mm and 2 mm above and below the maximum two-dimensional cross-sectional ROI,were then cut and input into the pre-trained ResNet50 model for feature extraction. The image genomics features were then fused with the extracted deep learning features using a least absolute shrinkage and selection operator regression model. A support vector machine classifier was used to construct a prediction model,which was evaluated using receiver operating characteristic curves and decision curve analysis. Results In the training set,the area under curve (AUC) of the deep learning feature model was 0.972,which was higher than that of the image genomics feature model (0.951) and the fused feature model (0.968),but this difference was not statistically significant (P>0.05). In the testing set,the AUCs of the deep learning feature model and the fused feature model were 0.867 and 0.895,respectively,which were significantly higher than that of the image genomics feature model (0.833),with statistically significant differences (Z=-1.794,-2.191,both P<0.05). The AUC of the fused feature model showed an improvement compared to the deep learning feature model,but the difference was not statistically significant (P>0.05). In the test set,decision curve analysis revealed that the fused feature model yielded greater benefits compared to both the deep learning feature model and the radiomic feature model. Conclusion The deep learning feature model based on ResNet50 deep residual network shows better performance in predicting early hematoma expansion than the image genomics feature model,and the fused feature model has a beneficial effect on predicting hematoma expansion. This deep learning approach provides a prediction tool with supervisory capability for clinical decision-making.

Objective To observe the value of subregional non-contrast CT(NCCT)radiomics features based on habitat imaging technology for predicting hematoma expansion(HE)in patients with spontaneous intracranial hemorrhage(sICH).Methods Data of 228 sICH patients with negative conventional imaging signs were retrospectively analyzed and divided into HE group(n=99)or non HE(NHE)group(n=129)based on the occurrence of HE nor not.also divided into training set(n=182)or test set(n=46)at a ratio of 8:2.Clinical data,NCCT data and laboratory examination results were compared between groups.Logistic regressive analysis was performed to screen the impact factors of HE.ROI of whole hematoma(ROIwhole)was sketched and clustered into 3 sub-regions(ROIsub1,ROIsub2 and ROIsub3,the latter located in the critical area between hematoma and brain tissue)with habitat imaging technology,and radiomics features of ROI were extracted and screened.Then 4 prediction models were constructed based on the above 4 ROI,and the efficacy of each model for predicting HE was analyzed.Results The fasting blood glucose in HE group was higher than that in NHE group(t=2.047,P=0.041),which was not independent impact factor for predicting HE in sICH patients(P=0.070)according to logistic regression analysis.The area under the curve of ROIsub3 radiomics model for predicting sICH HE in training and test set was 0.945 and 0.863,respectively,not significantly different with that of ROIwhole(0.921,0.813),ROIsub1(0.925,0.807)nor ROIsub2(0.909,0.720)(all P＞0.05).Decision curve analysis showed that ROIsub3 radiomics model could bring greater benefits than the other 3 models.Conclusion NCCT radiomics features of the critical area between hematoma and brain tissue based on habitat imaging technology had high value for predicting HE in sICH patients.

Ankle Injuries ; Fractures, Compression ; Humans ; Knee Injuries

Objective　To investigate the effect of fluid attenuated inversion recovery (FLAIR) on hyperintense vascular sign in patients with unilateral middle cerebral artery chronic occlusion.Changes of HVS and prediction of cerebral infarction were also analyzed.Methods　Patients with unilateral middle cerebral artery chronic occlusion who were hospitalized in Jiangdu People’s Neurology Department of Yangzhou City from July 2016 to August 2021 were enrolled.According to the presence or absence of cerebral infarction,they were divided into non-cerebral infarction group and cerebral infarction group.According to whether the cerebral infarction recurred during the follow-up,the cerebral infarction group was divided into recurrence group and non-recurrence group.Multivariate logistic regression model and ROC curve were used to analyze the risk and predictive value of FVHs and cerebral infarction in patients with unilateral middle cerebral artery chronic occlusion.Results　(1)Univariate analysis showed that the fibrinogen level,HVS signs and FVHs score in cerebral infarction group were significantly higher than those in non-cerebral infarction group (P<0.05).The baseline FVHs score of recurrent cerebral infarction patients were significantly higher than that of non-recurrent cerebral infarction patients (P<0.05).(2)Logistic regression analysis showed that FVHs score had a significant independent positive correlation with the first occurrence of cerebral infarction (OR=2.499; 95%CI 1.481~4.218;P=0.001),and FVHs score was not independently associated with cerebral infarction recurrence (OR=1.356;95%CI 0.922~1.994;P=0.112);(3)ROC curve analysis showed that FVHs score ≥4 had certain predictive value for cerebral infarction in patients with unilateral middle cerebral artery chronic occlusion,with sensitivity of 73.3%,specificity of 66.2%,area under curve (AUC) of 0.669 (95%CI 0.476~0.861;P=0.041).Conclusion　For patients with unilateral middle cerebral artery chronic occlusion,HVS may change in a process from scratch,and once the occurrence of HVS signs suggests that cerebral blood flow is decompensated,which is prone to cerebral infarction.FHVs score has certain predictive value for cerebral infarction.

Objective:To investigate the clinical significance of fluid-attenuated inversion recovery (FLAIR) vascular hyperintensities (FVHs) in patients with chronic atherosclerotic middle cerebral artery occlusion.Methods:From July 2016 to November 2020, patients admitted to the Department of Neurology, Jiangdu People's Hospital of Yangzhou and first found chronic atherosclerotic middle cerebral artery occlusion were enrolled. The demographic, clinical and MRI imaging data were collected. According to the presence or absence of acute cerebral infarction, they were divided into the non-acute cerebral infarction group and the acute cerebral infarction group. According to the modified Rankin Scale score at 3 months after the onset, patients with acute cerebral infarction were further divided into the good outcome group (≤2) and the poor outcome group (>2). A multivariate logistic regression model was used to analyze the independent correlation between FVHs and the risk of cerebral infarction in patients with chronic atherosclerotic middle cerebral artery occlusion and the outcome in patients with cerebral infarction. Results:A total of 94 patients with chronic atherosclerotic middle cerebral artery occlusion were enrolled, including 38 with non-acute cerebral infarction (40.4%) and 56 with acute cerebral infarction (59.6%). In patients with acute cerebral infarction, 13 (23.2%) had a poor outcome, and 43 (76.8%) had a good outcome. The fibrinogen level, the proportion of patients with FVHs and the FVHs score in the cerebral infarction group were significantly higher than those in the non-cerebral infarction group (all P<0.05). Multivariate logistic regression analysis showed that the FVHs score was significantly, independently, and positively correlated with the risk of acute cerebral infarction (odds ratio 2.524, 95% confidence interval 1.400-4.552; P=0.002). The National Institutes of Health Stroke Scale score at admission, the modified Rankin Scale score at admission, and FVHs score in the poor outcome group were significantly higher than those in the good outcome group (all P<0.05). Multivariate logistic regression analysis showed that there was a significant independent negative correlation between the FVHs score and the outcome of patients with cerebral infarction (odds ratio 0.144, 95% confidence interval 0.045-0.459; P=0.001). Conclusions:FVHs suggest that the blood supply is in a state of decompensation. When FVHs are present, the risk of cerebral infarction in patients with chronic middle cerebral artery occlusion is significantly increased; the wider the range of FVHs, the higher the risk of poor outcome after cerebral infarction.

Objective To observe the application of multiple fluorescent PCR (Polymerase Chain Reaction) in the diagnosis and clinical detection of bloodstream infection. Methods 256 blood cultures were collected by the Laboratory Department of Yinzhou People′s Hospital from January 2018 to May 2018, and were detected by multiplex fluorescent PCR. The results of the PCR were compared with the traditional blood culture bacteria identification instrument (traditional blood culture method). The number of positive and negative samples and the number of corresponding samples of the two methods were counted. Then, they analyzed the specificity and sensitivity of multiplex fluorescence PCR in the diagnosis of bloodstream flow infections. Results A total of 18 pathogenic microbes are detected through blood culture and PCR. Multiple fluorescent PCR detects 142 positive samples and 114 negative samples. Among them, 132 samples also show positive through blood culture, and 111 samples show negative. The consistency rate between multiple PCR and traditional blood cultures is 91.8％ (235/256). The negative prediction rate of PCR is 97.4％ (111/114), sensitivity rate 97.8％ (132/135), specificity rate 91.7％ (111/121). 10 samples show positive through multiple fluorescence PCR but negative for blood culture, 3 samples show positive through blood culture but negative for PCR. Besides, there are 3 types of pathogens that exceed the detection range of PCR. Conclusions Multiplex PCR method can detect 17 pathogens in blood culture specimens of patients, which can not only optimize the traditional blood culture process, but also greatly shorten the reporting time and improve the detection rate of blood culture methods. Especially for patients treated with antibiotics, it can reduce missed detection and improve the diagnostic rate of bloodstream infections.