Purpose To explore the diagnostic efficiency of ultrasound(US)combined with contrast-enhanced ultrasound(CEUS)in breast lesions and to analyze the related factors affecting the diagnostic accuracy.Materials and Methods From January 2022 to February 2023,the clinical data and ultrasound images of 784 patients who underwent breast US and CEUS examination with definite pathological results were retrospectively collected in the First Medical Center of the PLA General Hospital.The diagnostic efficacy of US combined with CEUS in benign and malignant breast lesions was analyzed,respectively.The independent risk factors for diagnostic errors were analyzed via Logistic regression.Results The sensitivity,specificity and accuracy of US combined with CEUS in the diagnosis of benign and malignant breast lesions were 89.2%,84.4%and 88.7%,respectively.The area under the receiver operating characteristic curve was 0.932.The results of multivariate Logistic regression analysis showed that the diagnosis error rate increased when the lesions were non-mass type(odds ratio,OR=1.927,P=0.047),complex cystic and solid(OR=3.729,P=0.000),and high-enhanced CEUS(OR=1.937,P=0.023),while the diagnosis error rate decreased when the lesions were large(OR=0.688,P=0.004)and with US-detect suspicious lymph node(OR=0.143,P=0.011).Conclusion When the breast lesions are non-mass type,complex cystic and solid lesions and hyper-enhancement,the diagnosis error rate of US combined with CEUS increased.It is necessary to further explore the enhancement patterns of different lesions.

Purpose To explore the correlation between phosphatidylinositol 3 kinase/serine-threonine kinase(PI3K/AKT)signaling pathway and elastic characteristics of breast lesions.Materials and Methods A total of 115 breast lesions were prospectively analyzed in 114 patients who underwent surgery from May 2021 to May 2022 at Chinese PLA General Hospital.Ultrasound and shear wave elastography were performed preoperatively.Immunohistochemical staining was used to detect the expression of PI3K/AKT protein levels in the tissue specimens,and the correlation between the staining results and the elastic parameters of shear wave elastography was analyzed.Results Surgical pathology revealed benign breast lesions in 50 cases and malignant lesions in 65 cases(25 cases with axillary lymph node metastasis).The maximum modulus of elasticity(F=40.47),the average modulus of elasticity(F=45.11),the ratio of elasticity of the lesion to that of the surrounding tissue(F=48.98),the detection rate of"hard ring sign"(χ2=62.25),the expression level of PI3K/p-PI3K(F=15.19,58.95)and AKT/p-AKT(F=46.94,74.21)were found in benign and malignant lesions without axillary lymph node metastasis,malignant lesions with axillary lymph node metastasis(all P<0.05).The expression levels of PI3K/p-PI3K and AKT/p-AKT were positively correlated with the maximum elastic modulus value,the mean elastic modulus value,and the ratio of elasticity of the lesion to the surrounding tissues(r=0.475,0.475,0.451;r=0.533,0.540,0.542;r=0.371,0.402,0.445;r=0.482,0.455,0.545,all P<0.05).Conclusion The expression level of PI3K/AKT in breast lesions is correlated with elastic characteristics,suggesting that it plays an important role in the regulation of elastic characteristics of breast lesions.

Objective:To investigate the value of dual-detector spectral CTA in distinguishing infarct core from penumbra in patients with acute ischemic stroke(AIS), and to further explore the risk factors associated with infarct core and their predictive value.Methods:The imaging and clinical data of 163 patients with AIS who met the inclusion criteria admitted to the Second Affiliated Hospital of Soochow University from March 2022 to May 2023 were retrospectively analyzed. Patients from March 2022 to December 2022 were used as the training group, and patients from January 2023 to May 2023 were used as the validation group for internal validation. The head and neck spectral CTA and brain CT perfusion imaging with dual-layer detector spectral CT were all carried out on all patients. Using CTP as reference, the patients were divided into infarct core group and non-infarct core group according to whether an infarct core occurred in the hypoperfusion regions of brain tissue. Multivariate logistic regression analysis was used to screen predictors related to the infarct core. The receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy.Results:A total of 163 patients were included in the study, including 112 in the training group and 51 in the validation group. There were significant differences in iodine density, effective atomic number, hypertension, triglyceride and neutrophils between the two groups ( P< 0.05). The cutoff values for iodine density values and effective atomic number values were 0.215 mg/mL and 7.405, respectively. Multivariate logistic regression analysis showed that iodine density and hypertension were independent risk factors for infarct core in AIS, and triglyceride was an independent protective factor. The area under the ROC curve (AUC) of iodine density value was the largest (0.859), with a sensitivity of 70.27%, and a specificity of 90.67%, which had a good predictive value. The ROC curve analysis results for the validation group were consistent with the training group. Conclusions:Spectral CT parameters iodine density values and effective atomic number values have the potential to distinguish the infarct core area from the penumbra area in patients with AIS. Iodine density and hypertension were independent risk factors of infarct core in AIS, triglyceride was an independent protective factor, and iodine density values obtained by dual-layer spectral detector CT had a high predictive value.

Objective:To investigate the predictive value of clinical factor model, deep learning model based on baseline plain CT images, and combination of both for predicting hematoma expansion in cerebral hemorrhage.Methods:The study was cross-sectional. Totally 471 cerebral hemorrhage patients who were firstly diagnosed in the Second Affiliated Hospital of Soochow University from January 2017 to December 2021 were collected retrospectively. These patients were randomly divided into a training dataset ( n=330) and a validation dataset ( n=141) at a ratio of 7∶3 by using the random function. All patients underwent two noncontrast CT examinations within 24 h and an increase in hematoma volume of >33% or an absolute increase in hematoma volume of >6 ml was considered hematoma enlargement. According to the presence or absence of hematoma enlargement, all patients were divided into hematoma enlargement group and hematoma non-enlargement group.Two-sample t test, Mann-Whitney U test or χ2 test were used for univariate analysis. The factors with statistically significant differences were included in multivariate logistic regression analysis, and independent influences related to hematoma enlargement were screened out to establish a clinical factor model. ITK-SNAP software was applied to manually label and segment the cerebral hemorrhage lesions on plain CT images to train and build a deep learning model based on ResNet50 architecture. A combination model for predicting hematoma expansion in cerebral hemorrhage was established by combining independent clinical influences with deep learning scores. The value of the clinical factor model, the deep learning model, and the combination model for predicting hematoma expansion in cerebral hemorrhage was evaluated using receiver operating characteristic (ROC) curves and decision curves in the training and validation datasets. Results:Among 471 cerebral hemorrhage patients, 136 cases were in the hematoma enlargement group and 335 cases were in the hematoma non-enlargement group. Regression analyses showed that male ( OR=1.790, 95% CI 1.136-2.819, P=0.012), time of occurrence ( OR=0.812, 95% CI 0.702-0.939, P=0.005), history of oral anticoagulants ( OR=2.157, 95% CI 1.100-4.229, P=0.025), admission Glasgow Coma Scale score ( OR=0.866, 95% CI 0.807-0.929, P<0.001) and red blood cell distribution width ( OR=1.045, 95% CI 1.010-1.081, P=0.011) were the independent factors for predicting hematoma expansion in cerebral hemorrhage. ROC curve analysis showed that in the training dataset, the area under the curve (AUC) of clinical factor model, deep learning model and combination model were 0.688 (95% CI 0.635-0.738), 0.695 (95% CI 0.642-0.744) and 0.747 (95% CI 0.697-0.793) respectively. The AUC of the combination model was better than that of the clinical model ( Z=0.54, P=0.011) and the deep learning model ( Z=2.44, P=0.015). In the validation dataset, the AUC of clinical factor model, deep learning model and combination model were 0.687 (95% CI 0.604-0.763), 0.683 (95% CI 0.599-0.759) and 0.736 (95% CI 0.655-0.806) respectively, with no statistical significance. Decision curves showed that the combination model had the highest net benefit rate and strong clinical practicability. Conclusions:Both the deep learning model and the clinical factor model established in this study have some predictive value for hematoma expansion in cerebral hemorrhage; the combination model established by the two together has the highest predictive value and can be applied to predict hematoma expansion.

Objective:To investigate the risk factors of postoperative recurrence of solid pseudopapillary neoplasms of the pancreas (SPN).Methods:Case-control studies on risk factors for postoperative recurrence in patients with SPN were conducted by searching in China National Knowledge Infrastructure, Wanfang Database, VIP Database, PubMed, Web of Science and Embase database from inception of these databases to November 2022. Two investigators screened the collected literatures independently according to the inclusion and exclusion criteria, extracted the data and evaluated the methodological quality, and then used Review Manager 5.4 for statistical analysis, odds ratio ( OR) was calculated with 95% confidence interval ( CI). Results:A total of 14 articles were included, including 1 409 patients with 67 cases in recurrence group and 1 342 cases in non-recurrence group. Twelve risk factors with predictive value for postoperative recurrence of SPN were extracted from the literatures. The analysis showed that the pooled OR and 95% CI of each risk factor were: gender ( OR=0.75, 95% CI: 0.35-1.59, P=0.450), age( OR=-2.08, 95% CI: -5.24-1.08, P=0.200), tumor diameter( OR=5.29, 95% CI: 4.71-5.87, P<0.001), tumor location( OR=0.56, 95% CI: 0.28-1.13, P=0.100), synchronous metastasis( OR=86.84, 95% CI: 22.64-333.05, P<0.001), lymph node metastasis ( OR=7.55, 95% CI: 2.58-22.06, P<0.001), perineural invasion ( OR=2.10, 95% CI: 0.98-4.48, P=0.060), positive margin( OR=7.00, 95% CI: 2.56-19.15, P<0.001), calcification( OR=0.49, 95% CI: 0.11-2.23, P=0.360), lymphovascular invasion( OR=11.22, 95% CI: 4.81-26.18, P<0.001), peripancreatic soft tissue invasion( OR=1.38, 95% CI: 0.48-4.00, P=0.550), capsular invasion( OR=1.72, 95% CI: 0.53-5.65, P=0.370). Conclusion:Large tumor diameter, synchronous metastasis, lymph node metastasis, positive margin, lymphovascular invasion increase the risk of recurrence of pancreatic SPN after resection, and patients with these characteristics should receive long-term follow-up.

Objective:To develop a grading prediction model of traumatic hemorrhage volume based on deep learning and assist in predicting traumatic hemorrhage volume.Methods:A retrospective observational study was conducted based on the experimental data of pig gunshot wounds in the time-effect assessment database for experiments on war-traumatized animals constructed by the General Hospital of the Chinese People's Liberation Army. The hemorrhage volume data of the study population were extracted, and the animals were divided into 0-300 mL, 301-600 mL, and > 600 mL groups according to the hemorrhage volume. Using vital signs indexes as the predictive variables and hemorrhage volume grading as the outcome variable, trauma hemorrhage volume grading prediction models were developed based on four traditional machine learning and ten deep learning methods. Using laboratory test indexes as predictive variables and hemorrhage volume grading as outcome variables, trauma hemorrhage volume grading prediction models were developed based on the above fourteen methods. The effect of the two groups of models was evaluated by accuracy and area under the receiver operator characteristic curve (AUC), and the optimal models in the two groups were mixed to obtain hybrid model 1. Feature selection was conducted according to the genetic algorithm, and hybrid model 2 was constructed according to the best feature combination. Finally, hybrid model 2 was deployed in the animal experiment database system.Results:Ninety-six traumatic animals in the database were enrolled, including 27 pigs in the 0-300 mL group, 40 in the 301-600 mL group, and 29 in the > 600 mL group. Among the fourteen models based on vital signs indexes, fully convolutional network (FCN) model was the best [accuracy: 60.0%, AUC and 95% confidence interval (95% CI) was 0.699 (0.671-0.727)]. Among the fourteen models based on laboratory test indexes, recurrent neural network (RNN) model was the best [accuracy: 68.9%, AUC (95% CI) was 0.845 (0.829-0.860)]. After mixing the FCN and RNN models, the hybrid model 1, namely RNN-FCN model was obtained, and the performance of the model was improved [accuracy: 74.2%, AUC (95% CI) was 0.847 (0.833-0.862)]. Feature selection was carried out by genetic algorithm, and the hybrid model 2, namely RNN-FCN* model, was constructed according to the selected feature combination, which further improved the model performance [accuracy: 80.5%, AUC (95% CI) was 0.880 (0.868-0.893)]. The hybrid model 2 contained ten indexes, including mean arterial pressure (MAP), hematocrit (HCT), platelet count (PLT), lactic acid, arterial partial pressure of carbon dioxide (PaCO 2), Total CO 2, blood sodium, anion gap (AG), fibrinogen (FIB), international normalized ratio (INR). Finally, the RNN-FCN* model was deployed in the database system, which realized automatic, continuous, efficient, intelligent, and grading prediction of hemorrhage volume in traumatic animals. Conclusion:Based on deep learning, a grading prediction model of traumatic hemorrhage volume was developed and deployed in the information system to realize the intelligent grading prediction of traumatic animal hemorrhage volume.

Objective:To observe the changes of arterial blood gas indexes in pigs with the free-field primary blast lung injury (PBLI) model, and to explore the value of arterial blood gas indexes in predicting moderate to severe PBLI.Methods:Nine adult healthy Landrace pigs were selected to construct the pig free-field PBLI model. Arterial blood samples were taken 15 minutes before the explosion (before injury) and 10, 30, 60, 120, and 180 minutes after the explosion (after injury). Arterial blood gas indexes and pulse oxygen saturation (SpO 2) were measured, compare the changes of blood gas analysis indexes and SpO 2 levels at different time points, and observe the changes of gross injury scores and pathological injury scores of lung tissue. Analyze the correlation between the blood gas indicators. Results:As time prolonged, at each time point, pH, arterial partial pressure of oxygen (PaO 2), and SpO 2 were lower than those before the injury, and blood lactic acid (Lac) and arterial partial pressure of carbon dioxide (PaCO 2) were higher than those before the injury. Compared with that before the injury, the pH value in the blood decreased significantly 10 minutes after the injury (7.39±0.06 vs. 7.46±0.02, P < 0.05), and the Lac increased significantly (mmol/L: 3.61±2.89 vs. 1.10±0.28, P < 0.05), and lasts until 180 minutes after injury (pH value: 7.37±0.07 vs. 7.46±0.02, Lac (mmol/L): 2.40±0.79 vs. 1.10±0.28, both P < 0.05); while PaO 2 and SpO 2 decreased significantly at 180 minutes after injury [PaO 2 (mmHg, 1 mmHg = 0.133 kPa): 59.40±10.94 vs. 74.81±9.39, P < 0.05; SpO 2: 0.75±0.11 vs. 0.89±0.08, P < 0.05], PaCO 2 increased significantly (mmHg: 56.17±5.38 vs. 48.42±4.93, P < 0.05). Correlation analysis showed that the gross injury score of lung blast injury animals was positively correlated with the pathological injury score ( r = 0.866, P = 0.005); PaO 2 and SpO 2 were positively correlated ( r = 0.703, P = 0.000); pH value and Lac were negative Correlation ( r = -0.400, P = 0.006); pH value is negatively correlated with PaCO 2 ( r = -0.844, P = 0.000). Conclusion:This study successfully established a large mammalian free-field PBLI model, arterial blood gas analysis is helpful for the early diagnosis of PBLI, whether SpO 2 can be used to evaluate the severity of lung injury remains to be further verified.

Objective: To investigate the expression and clinicopathological significance of high mobility group box protein B1 (HMGB1) protein in breast cancer. Methods: The expression of HMGB1 protein in 26 normal breast tissues and 417 invasive breast cancer tissues diagnosed at Dongyang People′s Hospital, Zhejiang Province from 2016 to 2018 were detected by immunohistochemical EnVision method. The relationship between nuclear and cytoplasmic HMGB1 protein expression and clinicopathologic features of breast cancer patients were analyzed. Results: The nuclear and cytoplasmic expression of HMGB1 protein was 80.8% (337/417) and 16.8% (70/417) respectively in breast cancer, and was 46.2%(12/26) and 0(0/26) respectively in normal breast tissue. Both nuclear and cytoplasmic expression of HMGB1 protein in breast cancer were significantly higher than normal breast tissue (P<0.001, P=0.046, respectively). The nuclear expression of HMGB1 protein was also higher in high grade, estrogen receptor (ER) negative, progesterone receptor (PR) negative (P=0.006, P=0.004, P<0.001, respectively); whereas the cytoplasmic expression of HMGB1 protein was also higher in high grade, estrogen receptor (ER) negative, progesterone receptor (PR) negative (P<0.001 in all) breast cancers. Multivariate logistic regression model showed that nuclear HMGB1 expression correlated with histologic grade (OR=2.188, 95%CI=1.078-4.443, P=0.030), while cytoplasmic HMGB1 expression correlated with histologic grade (OR=3.031, 95%CI=1.600-5.742, P=0.001), ER (OR=0.129, 95%CI=0.034-0.494, P=0.003) and TNM staging (OR=3.820, 95%CI=1.042-14.001, P=0.043). Multivariate analysis of Cox proportional hazard model showed that nuclear HMGB1 expression was an independent risk factor for the overall survival of breast cancer patients (HR=0.366, 95%CI=0.138-0.972, P=0.044). Conclusion Nuclear and cytoplasmic HMGB1 proteins are related to multiple poor prognostic factors in breast cancer, and may be a potential biomarker for breast cancer treatment.

Objective To compare the predictive values of swirl sign and black hole sign on CT scanning in early hematoma expansion in spontaneous intracerebral hemorrhage (SICH) patients.Methods Two hundred and ten firstly diagnosed SICH patients,admitted to our hospital from January 2012 to December 2018,were enrolled in the study.All patients were divided into hematoma expansion and non-hematoma expansion group according to whether early hematoma expansion appeared;and they were also divided into positive imaging sign group and negative imaging sign group according to whether imaging signs appeared;the clinical and imaging data were compared between these groups,respectively.The accuracies of swirl sign and black hole sign in predicting early hematoma expansion were analyzed using receiver operator characteristic (ROC) curve.Multivariate Logistic regression analysis was performed to determine the independent risk factors for early hematoma expansion.Results (1) In the 57 patients with early hematoma expansion,21 (36.8％) had swirl sign,and 17 (29.8％) had black hole sign;in the 153 patients without hematoma expansion,12 (7.8％) had swirl sign and 22 (14.4％) had black hole sign;the differences between the two groups were statistically significant (P＜0.05).As compared with those in the non-hematoma expansion group,the admission systolic blood pressure increased significantly and number of patients with intraventricular hemorrhage was significantly larger in the hematoma expansion group (P＜0.05).(2) There were no statistical differences in clinical and imaging data between the patients with swirl sign (n=33) and patients without swirl sign (n=177,P＞0.05);the hematoma volume in patients with black hole sign (n=39) was significantly increased as compared with that in patients without black hole sign (n=171,P＜0.05),and there were no statistical differences in other clinical and imaging data between patients with and without black hole sign (P＞0.05).(3) The areas under ROC curve of swirl sign,black hole sign,and "swirl sign combined with black hole sign" were 0.645,0.577,and 0.570,respectively.(4) Multivariate Logistic regression analysis showed that admission systolic blood pressure,swirl sign and black hole sign were independent risk factors for early hematoma expansion (P＜0.05).Conclusion In comparison to black hole sign and "swirl sign combined with black hole sign",the swirl sign has higher predictive value in early hematoma expansion in ICH patients.

Objective To evaluate the imaging features of nigrosome-1 in Parkinson's disease (PD) with a 3 T scanner by susceptibility weighted imaging (SWI),and to explore its clinical relevance.Methods Thirty-two patients with primary PD diagnosed by neurologists were collected.Healthy controls matched to their age and gender were recruited during the same period (n=20).All subjects underwent routine brain magnetic resonance imaging (MRI) and sensitive weighted imaging (SWI).The SWI images of the subjects were evaluated to evaluate nigrosome-1 by blinded investigators.Then,the correlation between imaging features and clinical data was analyzed.Results In the PD group,21 cases of bilateral "absent swallow-tail sign",five cases of bilateral "indecisive swallow-tail sign",five cases of "absent swallow-tail sign" on one side and "indecisive swallow-tail sign" on the other side,and one case of bilateral "clear swallow-tail sign" were found.The course of the "absent swallow-tail sign" group (56 (54) months) was significantly longer than the "non-absent swallow-tail sign" group (18 (18) months;U=-2.47,P=0.01).The Hoehn-Yahr stage was significantly higher in the "absent swallow-tail sign" group (2.0 (0.5)) than in the "non-absent swallow-tail sign" group (1.5 (0.5),U=-2.21,P=0.03).There was also a statistically significant difference in the Unified Parkinson's Disease Rating Scale score (24 (8),13 (14)) between the two groups (U=-2.91,P=0.01).However,there were no statistically significant differences between the two groups in the Hamilton Depression Scale score (5 (2) vs 5 (7),U=-0.10,P=0.94) and the Hamilton Anxiety Scale score (3.0 (2.5) vs 3.0 (3.0),U=-0.02,P=1.00).Conclusion The images of nigrosome-1 by SWI are closely related to the severity of the condition and motor symptoms of patients with PD,which can reflect the severity of the disease.