Objective To evaluate MRI characteristics of solid papillary carcinomas (SPCs) in situ of the breast.Methods A retrospective study included 5 patients with pathologically confirmed SPC in situ was performed.MRI data before operation including conventional MRI,dynamic contrast enhanced MRI (DCE-MRI) and DWI were analyzed.Results All the lesions showed iso/hypointensity on T1 FSPGR sequence,iso/hyperintensity on FSE T2WI sequence and STIR sequence.Mass enhancements were observed for all lesions with oval or irregular shapes on DCE-MRI.The margin of lesions were circumscribed,and internal enhancements were homogeneous or heterogeneous.Time intensity curve appeared a rapid increase in initial contrast phases and platform or outflow types in delayed phases.All the lesions on DWI showed slightly hyperintensity with the ADC value range from 1.34 × 10-3 mm2/s to 1.96)× 10-3 mm2/s.Conclusion MRI manifestations of SPC are characteristics,which may provide valuable information to distinguish SPC in situ from other invasive breast carcinomas.

Objective To evaluate MR diffusion-weighted imaging (DWI)and MR perfusion-weighted imaging(PWI) in differentiating benign from malignant soft tissue tumors by comparing the related parameters. Methods Fifty patients with soft tissue tumors verified by pathology( benign 24, malignant 26) underwent DWI and dynamic contrast-enhanced T2 * -weighted PWI. DWI and PWI data of benign and malignant soft tissue tumors were acquired at the workstation and their difference was analyzed with t-test. The diagnostic accordance rate was verified with x2-test. Subjective overall performance of two techniques were evaluated with receiver operating characteristic (ROC) analysis. Results ADC values of benign and malignant tumors were (2. 03±0. 36) × 10-3 mm2/s, ( 1.52±0. 39) × 10-3 mm2/s,respectively. The signal intensity decrease of them during the first-pass perfusion (SIdecrease ) were ( 13.54 ± 3.37 )%, (47. 57 ± 5. 21 ) % ,respectively. The maximum linearity slope rate of TIC ( SSmax ) of them were ( 5.51 ± 2. 54 ) %, (7.94 ± 3. 33) %, respectively. There were significant differences between benign and malignant tumors of ADC value and SIdecrease ( t = 2. 515,2. 938 ;P < 0. 05 ), while there was no significant difference in SSmax (t = 1. 272,P >0. 05). When the threshold of ADC value was 1. 866 × mm2/s, sensitivity and specificity for determining malignant tumors were 84. 6% (22/26)and 83.3% (20/24). When the threshold of SIdecrease was 40. 33% ,sensitivity and specificity for determining malignant tumors were 88. 5% (23/26)and 75.0% (18/24). In type Ⅰa of TIC,the proportion of benign soft tissue tumor was 3/24 and malignant tumor was 20/26. In type Ⅰb , benign tumor was 14/24 and malignant tumor was 3/26. In type Ⅰc, malignant tumor was 3/26. In type Ⅱ ,benign tumor was 7/24. The diagnostic accordance rate of DWI and PWI were 84. 0% (42/50) and 82. 0% (41/50), respectively. There was no significant difference between them ( x2 = 0. 8, P >0. 05). The accuracies of them were 81.7% , 83. 6% respectively by the area under the ROC curve (AUC). The sensitivity of PWI in diagnosing malignant soft tissue tumors was higher. Conclusions ADC value and SIdecrease are Valllable diagnostic parameters in differentiating benign and malignant soft tissue tumors. The threshold of these parameters for diagnosing malignant soft tissue tumors are 1. 866 × 10-3 mm2/s and 40. 33%, respectively. The type of TIC can help to distinguish malignant tumors from benign tumors, while the SSmax can not. The accuracies of DWI and PWI in the diagnosis of malignant soft tissue tumors are moderate. Compared with DWI, PWI should be selected firstly because of its higher sensitivity in diagnosing malignant tumors.

Objective To evaluate multiple magnetic resonance (MR) imaging techniques in the differentiation of benign and malignant soft tissue tumors by comparing different information from MR perfusion-weighted Imaging (MR-PWI) and 1 HMR spectroscopy (1 H-MRS).Methods Forty patients with soft tissue tumors underwent conventional MR imaging,dynamic contrast-enhanced T2*-weighted MR-PWI and 1 H proton MR spectroscopy.The differences of perfusion and 1 H-MRS parameters of benign and malignant tumors were analyzed with t test.Results There was significant difference between benign and malignant tumoral tissues of BF value and Cho/Cr ratio(t=2.531,2.927,P < 0.05),while BV,MTT,Cho,Cr or Lip peak value were not.TIC was different between benign group (Ib) and malignant group (Ia).When the threshold value of BF was 4.35 ml ·100 mg-1·min-1,sensitivity and specificity for determining malignant tumors were 81.8%(18/22),72.2%(13/18),respectively.When the threshold value of Cho/Cr ratio was 3.22,Sensitivity and specificity for determining malignant tumors were 86.4% (19/22),88.9% (16/18),respectively.The abnormal wave crest is detected at 2.0-2.1ppm in 5 malignant tumors (2 malignant schwannoma and 3 malignant fibrous histiocytoma),while the other 35 cases were not.Conclusion The BF value and Cho/Cr ratio were both valuable diagnostic parameters in differentiating benign and malignant soft tissue tumors.TIC was helpful to distinguish malignant tumors from benign tumors,while the sensitivity and specificity of 1 H-MRS in diagnosing malignant soft tissue tumors were both higher.

With constant development and application of new generation information technology such as big data, cloud computing and Internet of Things, traditional management style and thought patterns of TCM are being changed. It is particularly important to introduce information security into budget management of TCM projects. This article discussed security factors in TCM budget monitoring platform, organized key contents of information security construction, built information security model for monitoring platform, and analyzed security strategies for the construction of TCM budget monitoring platform, with a purpose to guarantee effective implementation of budget information management measures of TCM projects.

Objective To investigate the value of kinetic features measured by computer-aided diagnosis (CAD)for breast MRI.Methods One hundred and sixty four lesions diagnosed pathologically by operation or biopsy comprised the analysis set.Automated lesion kinetic information from CADStream programs for breast MRI was identified.Three CAD variables were compared for benign and malignant lesions: initial phase peak enhancement (greatest percentage of signal intensity increase on first contrast enhanced sequence),delayed phase enhancement categorized by a single type of kinetics comprising the largest percentage of enhancement (washout,plateau,or persistent),and delayed phase enhancement categorized by single most suspicious type of kinetics (any washout ＞ any plateau ＞ any persistent).Morphological characteristics of breast lesions were described according to breast imaging and reporting data system (BI-RADS).Initial phase peak enhancement mean values between benign and malignant breast lesions were compared by using Wilcoxon rank-sum test,delayed phase enhancement categorized by a single type of kinetics comprising the largest percentage of enhancement or by single most suspicious type of kinetics between benign and malignant breast lesions were compared by using Chi-square test.Results There were 72 benign and 92 malignant breast lesions.A total of 123 (75.0％) mass lesions were identified,and the other 41 (25.0％) lesions showed no mass.Thirty lesions were BI-RADS-MRI 2,68 lesions were BI-RADS-MRI 3,43 lesions were BI-RADS-MRI 4,23 lesions were BI-RADS-MRI 5.Initial phase peak enhancement mean values of benign and malignant lesions were 237％ (69％ to 629％)and 336％ (86％ to 793％),respectively.There was no significant difference between benign and malignant lesions in initial peak enhancement mean value (Z =-1.626,P =0.104).Delayed phase enhancement categorized by single most suspicious type of kinetics (any washout ＞ any plateau ＞ any persistent) for benign and malignant lesions were 15,10,47 and 2,3,87 respectively.There was a significant difference between benign and malignant lesions (x2 =23.562,P =0.000).Initial peak enhancement value ＜ 100％ or ≥100％ were 5 and 67 for benign lesions,3 and 89 for malignant lesions,respectively.There was no significant difference between benign and malignant lesions at 100％ threshold (x2 =1.181,P =0.277).Delayed phase enhancement categorized by a single type of kinetics comprising the largest percentage of enhancement (washout,plateau,or persistent) for benign and malignant lesions were 48,6,18 and 47,15,30 respectively.There was no significant difference between benign and malignant lesions (x2 =4.496,P =0.106).Conclusions Of CAD kinetics analyzed,only delayed enhancement categorized by most suspicious type is helpful for the differentiation between benign and malignant lesions.However,there is significant overlap between initial peak enhancement at 100％ threshold or delayed kinetics categorized by largest percentage enhancement types of benign and malignant lesions,so lesion morphologic features should be considered.

One of the important approaches to structure analysis is protein fold recognition, which is oftenapplied when there is no significant sequence similarity between structurally similar proteins. A framework with athree-layer support vector machines fusion network (SFN) is presented. The framework is applied to 27-classprotein fold recognition from primary structure of proteins. SFN uses support vector machines as memberclassifiers, and adopts All-Versus-All as multi-class categorization. Six groups of features are divided into majorand minor ones by SFN, and several diversity fusion schemes are correspondingly built. The final decision is madeby dynamic selection of the results of all fusion schemes. When it is still difficult to know what kind of fusion offeature groups can achieve good prediction,SFN is a dependable solution by selecting the optimal fusion offeature groups automatically, which can ensure the best recognition. Overall recognition system achieves 61.04%fold prediction accuracy on the independent test dataset. The results and the comparison with other approachesdemonstrate the effectiveness of SFN, and thus encourage its further exploration.

Objective To study the recovery mechanism of dysphagic patients after stroke using functional magnetic resonanee imaging(fMRI). Methods Thirteen patients with dysphagia caused by unilateral cortical or subcortical lesions were recruited into a dysphagia group,and eight age-matched healthy volunteers were recruited as controls.Both grouDs performed experimental volitional swallowing tasks during fMRI studies.All patients of the dys-phagia group received rehabilitation treatment targeting dysphagia.Of the 13 dysphagia patients,7 reached almost complete recovery and were identified as recovered in follow-up fMRI studies.A 3.0 T MR scanner and echo planar imaging(EPI)T_2 WI sequence were employed to obtain the fMRI data.SPM2 software was used for post-processing of the fMRI data and displaying activated brain maps.Lateral index(LI)was calculated as LI:(C-1)/(C+I).Paired t tests were used to compare activated brain volume before and after complete recovery. Results Consistent activation of the bilateral primary sensorimotor cortex,anterior cingulated gyrus and the bilateral insular cortex were observed in the control group. Activation of the pons,medulla,left cerebellum,left prefrontal area,right occipital area and the left insular cortex were observed in the dysphagia group.Activation was observed in the bilateral primary sensorimotor cortex.bilateral prefrontal area,bilateral superior temporal gyrus,left insular cortex,bilateral frontal o-pereulum and anterior cingulated gyrus in the recovered patients.The total activated volume before recovery in the ip-silesional hemisDhere was significantly less compared with the contralesional hemisphere in the dysphagia group.In the recovered patients,both the activated brain volume of the ipsilesional hemisphere and value of LI were significant-ly larger than those at the initial examination.Conclusions Decreased activation in the sensorimotor cortex,the in-sular lobe and the cingulate gyms might be causes.of dysphagia.Compensation by the contralesional hemisphere in the early stages and then the restoration of the ipsilesional hemisphere after recovery may be mechanisms of dysphagia recovery in stroke patients.

A novel method of feature extraction from protein primary structure has been proposed and applied to classify the protein homodimer, homotrimer, homotetramer and homohexamer, i. e. one protein sequence can be represented by a feature vector composed of amino acid compositions and a set of weighted auto-correlation function factors of amino acid residue index. As a result, high classification accuracies are obtained. For example, with the same support vector machine (SVM), the total accuracies of QIANA, AIANB, MEEJ, ROBB and SNEP sets based on this novel feature extraction method are 77.63, 77.16, 76.46, 76.70 and 75.06% respectively in Jackknife test, which are 6.39, 5.92, 5.22, 5.46 and 3.82 percent points respectively higher than that of COMP set based on the conventional method composed of amino acid compositions. With the same QIANA set, the total accuracy of SVM is 77.63%, which is 16.29 percent points higher than that of covariant discriminant algorithm. These results show: (1) The novel feature extraction method is effective and feasible, and the feature vectors based on this method may contain more protein quaternary structure information and appear to capture essential information about the composition and hydrophobicity of residues in the surface patches buried in the interfaces of associated subunits; (2) SVM can be referred as a powerful computational tool for classifying the homo-oligomers of proteins.
Algorithms ; Amino Acid Sequence ; Artificial Intelligence ; Cluster Analysis ; Humans ; Models, Molecular ; Molecular Sequence Data ; Protein Conformation ; Proteins ; chemistry ; classification ; Sequence Analysis, Protein ; methods

Objective To analyze the patterns and distribution of lymph node metastasis in patients with adenocarcinoma of the esophagogastric junction ( AEG). Methods The pathological data of 393 patients with AEG from 2006 to 2009 were analyzed. The patterns and distribution of lymph node metastasis were analyzed in patients with different Siewert subtypes, depths of tumor invasion, and maximum diameters of the tumor, and the high?risk lymphatic drainage areas were investigated. Between?group comparison was performed by χ2 test. Results The metastatic rate and ratio of abdominal lymph nodes in AEG were 69?? 2%and 31?? 31%, respectively. The incidence rates of lymph node metastasis in the cardia, lesser curvature, left gastric artery, splenic artery, splenic hilum, mesenteric root, and abdominal aorta were the highest. The metastatic rate and ratio of mediastinal lymph nodes were 16?? 4% and 8?? 3%, respectively. The incidence rates of lymph node metastasis in the lower paraesophageal, esophageal hiatus, and superior diaphragmatic areas were the highest. Compared with Siewert type II and type III AEG, Siewert type I AEG had a significantly higher mediastinal lymph node metastatic rate (P= 0?? 003) and a significantly lower abdominal lymph node metastatic ratio (P= 0?? 002).The metastatic ratios of lymph nodes in multiple abdominal regions were higher in patients with stage T3+T4 AEG and a maximum tumor diameter of ≥6 cm than in the control group, while the metastatic ratios of mediastinal lymph nodes in groups with different maximum tumor diameters were similar. The metastatic ratios of lymph nodes in the greater curvature, hepatoduodenal ligament, and inferior diaphragmatic areas were lower than 10% in all groups. Conclusions In radiotherapy for AEG, the abdominal high?risk lymphatic drainage areas involve the cardia, lesser curvature, left gastric artery, splenic artery, splenic hilum, mesenteric root, and abdominal aorta, while the mediastinal high?risk lymphatic drainage areas involve the lower paraesophageal, esophageal hiatus, and superior diaphragmatic areas. In addition, the personalized target volume design should be based on the patterns of lymph node metastasis with different Siewert subtypes and clinical pathological characteristics.

Objective:To evaluate the value of 18F-FDG PET metabolic parameters in predicting histopathological grade of soft tissue sarcoma (STS). Methods:From December 2012 to December 2021, 51 patients (26 males, 25 females, age range: 32-84 years) who underwent 18F-FDG PET/CT imaging before treatment and confirmed STS pathologically in the First Affiliated Hospital of Dalian Medical University were retrospectively collected. 18F-FDG PET metabolic parameters SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG) and intertumoral FDG uptake heterogeneity (IFH) were measured. Kruskal-Wallis rank sum test was used to analyze the differences in metabolic parameters among different groups and Spearman rank correlation analysis was used to analyze the correlation of each metabolic parameter and histological grade. Logistic regression was used to screen and construct the prediction model for high-grade STS. ROC curve was plotted and Delong test was used to analyze the differences among AUCs. Results:The metabolic parameters SUV max, MTV, TLG and IFH were significantly different among French Federation of Cancer Centers Sarcoma Group (FNCLCC)Ⅰ( n=8), Ⅱ( n=10) and Ⅲ ( n=33) grade groups ( H values: 16.24, 10.52, 19.29 and 16.99, all P<0.05), and each metabolic parameter was positively correlated with histological grade ( rs values: 0.58, 0.45, 0.52, and 0.62, all P<0.05). Multivariate logistic regression analysis showed that SUV max(odds ratio ( OR)=1.27, 95% CI: 1.06-1.51, P=0.009) and IFH ( OR=6.83, 95% CI: 1.44-32.27, P=0.015) were independent risk indicators for high-grade STS. The prediction model constructed by combining SUV max and IFH had better diagnostic efficacy for differentiating high-grade STS with the AUC of 0.93, and the sensitivity of 93.9%(31/33) and the specificity of 16/18, respectively. The AUC of prediction model was significant different from SUV max, MTV, TLG and IFH (AUCs: 0.81, 0.78, 0.86 and 0.85; z values: 2.69, 2.53, 1.94 and 1.97, all P<0.05). Conclusions:The metabolic parameters SUV max, MTV, TLG and IFH are valuable predictors for histological grade of STS. The combination of SUV max and IFH may be a more meaningful method than using each of the above metabolic parameters alone.