To identify important MRI features to differentiate hepatic mucinous cystic neoplasms from septated hepatic cysts based on random forest.

OBJECTIVE: To identify important MRI features to differentiate hepatic mucinous cystic neoplasms (MCN) from septated hepatic cysts (HC) using random forest and compared with logistic regression algorithm. METHODS: Pathologically diagnosed hepatic cysts and hepatic MCNs with pre-operative contrast-enhanced MRI in our hospital from 2010 to 2023 were collected and only septated lesions on enhanced MRI were enrolled. A total of 21 septated HC and 18 MCNs were included in this study. Eighteen MRI features were analyzed and top important features were identified based on random forest (RF) algorithm. The results were evaluated by the prediction performance of a RF model combining the important features and compared with the performance of the logistic regression (LR) algorithm. Finally, for each identified feature, diagnostic probability, sensitivity, and specificity were calculated and compared. RESULTS: Four variables, i.e., the septation arising from wall without indentation, multiseptate, intracapsular cyst sign, and solitary lesion were extracted as top important features with significance for MCNs by the random forest algorithm. The RF model using these variables had an AUC of 0.982 (0.95CI, 0.950-1.000), compared with the LR model based on two identified features with AUC of 0.931 (0.95CI, 0.846-1.000), p = 0.202. Among the four important features, multiseptate had the highest specificity (95.2%) and good sensitivity (72.2%, lower than the septation from wall without indentation, 94.4%) to diagnose MCNs. CONCLUSION: Four out of 18 MRI features were extracted as reliably important factors to differ hepatic MCNs from septated HC. The combination of these four features in a RF model could achieve satisfactory diagnostic efficacy.

MRI quantitative assessment of the effects of low-carbohydrate therapy on Hashimoto's thyroiditis.

Objective: Hashimoto's thyroiditis is an inflammatory disease, and research suggests that a low-carbohydrate diet may have potential anti-inflammatory effects. This study aims to utilize Dixon-T2-weighted imaging (WI) sequence for a semi-quantitative assessment of the impact of a low-carbohydrate diet on the degree of thyroid inflammation in patients with Hashimoto's thyroiditis. Methods: Forty patients with Hashimoto's thyroiditis were recruited for this study and randomly divided into two groups: one with a normal diet and the other with a low-carbohydrate diet. Antibodies against thyroid peroxidase (TPOAb) and thyroglobulin (TgAb) were measured for all participants. Additionally, thyroid water content was semi-quantitatively measured using Dixon-T2WI. The same tests and measurements were repeated for all participants after 6 months. Results: After 6 months of a low-carbohydrate diet, patients with Hashimoto's thyroiditis showed a significant reduction in thyroid water content (94.84 ± 1.57% vs 93.07 ± 2.05%, P < 0.05). Concurrently, a decrease was observed in levels of TPOAb and TgAb (TPOAb: 211.30 (92.63-614.62) vs 89.45 (15.9-215.67); TgAb: 17.05 (1.47-81.64) vs 4.1 (0.51-19.42), P < 0.05). In contrast, there were no significant differences in thyroid water content or TPOAb and TgAb levels for patients with Hashimoto's thyroiditis following a normal diet after 6 months (P < 0.05). Conclusion: Dixon-T2WI can quantitatively assess the degree of thyroid inflammation in patients with Hashimoto's thyroiditis. Following a low-carbohydrate diet intervention, there is a significant reduction in thyroid water content and a decrease in levels of TPOAb and TgAb. These results suggest that a low-carbohydrate diet may help alleviate inflammation in patients with Hashimoto's thyroiditis.

A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model.

OBJECTIVE: To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). MATERIALS AND METHODS: This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. RESULTS: The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842-0.993) and 0.894 (95% CI: 0.766-0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p > 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. CONCLUSION: Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning.

Diagnostic Value of MRI Features in Dual-phenotype Hepatocellular Carcinoma: A Preliminary Study.

This study aimed to explore the magnetic resonance imaging (MRI) features of dual-phenotype hepatocellular carcinoma (DPHCC) and their diagnostic value.The data of 208 patients with primary liver cancer were retrospectively analysed between January 2016 and June 2021. Based on the pathological diagnostic criteria, 27 patients were classified into the DPHCC group, 113 patients into the noncholangiocyte-phenotype hepatocellular carcinoma (NCPHCC) group, and 68 patients with intrahepatic cholangiocarcinoma (ICC) were classified into the ICC group. Two abdominal radiologists reviewed the preoperative MRI features by a double-blind method. The MRI features and key laboratory and clinical indicators were compared between the groups. The potentially valuable MRI features and key laboratory and clinical characteristics for predicting DPHCC were identified by univariate and multivariate analyses, and the odds ratios (ORs) were recorded. In multivariate analysis, tumour without capsule (P = 0.046, OR = 9.777), dynamic persistent enhancement (P = 0.006, OR = 46.941), and targetoid appearance on diffusion-weighted imaging (DWI) (P = 0.021, OR = 30.566) were independently significant factors in the detection of DPHCC compared to NCPHCC. Serum alpha-fetoprotein (AFP) > 20 µg/L (P = 0.036, OR = 67.097) and prevalence of hepatitis B virus (HBV) infection (P = 0.020, OR = 153.633) were independent significant factors in predicting DPHCC compared to ICC. The differences in other tumour marker levels and imaging features between the groups were not significant. In MR enhanced and diffusion imaging, tumour without capsule, persistent enhancement and DWI targetoid findings, combined with AFP > 20 µg/L and HBV infection-positive laboratory results, can help to diagnose DPHCC and differentiate it from NCPHCC and ICC. These results suggest that clinical, laboratory and MRI features should be integrated to construct an AI diagnostic model for DPHCC.

Primary vaginal signet ring cell carcinoma.

Primary vaginal cancer is rare, accounting for only 2% of all gynecological malignant tumors. Primary vaginal cell carcinoma is mainly squamous cell carcinoma, accounting for about 90%, and adenocarcinoma only accounts for 8-10%. Primary signet ring cell carcinoma of vagina is rare and has not been reported in the literature. This paper reports a case of signet ring cell carcinoma in vagina.

A diagnostic scoring model of ENKTCL in the nose-Waldeyer's ring based on logistic regression: Differential diagnosis from DLBCL.

Objective: To establish a logistic regression model based on CT and MRI imaging features and Epstein-Barr (EB) virus nucleic acid to develop a diagnostic score model to differentiate extranodal NK/T nasal type (ENKTCL) from diffuse large B cell lymphoma (DLBCL). Methods: This study population was obtained from two independent hospitals. A total of 89 patients with ENKTCL (n = 36) or DLBCL (n = 53) from January 2013 to May 2021 were analyzed retrospectively as the training cohort, and 61 patients (ENKTCL=27; DLBCL=34) from Jun 2021 to Dec 2022 were enrolled as the validation cohort. All patients underwent CT/MR enhanced examination and EB virus nucleic acid test within 2 weeks before surgery. Clinical features, imaging features and EB virus nucleic acid results were analyzed. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors of ENKTCL and establish a predictive model. Independent predictors were weighted with scores based on regression coefficients. A receiver operating characteristic (ROC) curve was created to determine the diagnostic ability of the predictive model and score model. Results: We searched for significant clinical characteristics, imaging characteristics and EB virus nucleic acid and constructed the scoring system via multivariate logistic regression and converted regression coefficients to weighted scores. The independent predictors for ENKTCL diagnosis in multivariate logistic regression analysis, including site of disease (nose), edge of lesion (blurred), T2WI (high signal), gyrus like changes, EB virus nucleic acid (positive), and the weighted score of regression coefficient was 2, 3, 4, 3, 4 points. The ROC curves, AUCs and calibration tests were carried out to evaluate the scoring models in both the training cohort and the validation cohort. The AUC of the scoring model in the training cohort were 0.925 (95% CI, 0.906-0.990) and the cutoff point was 5 points. In the validation cohort, the AUC was 0.959 (95% CI, 0.915-1.000) and the cutoff value was 6 points. Four score ranges were as follows: 0-6 points for very low probability of ENKTCL, 7-9 points for low probability; 10-11 points for middle probability; 12-16 points for very high probability. Conclusion: The diagnostic score model of ENKTCL based on Logistic regression model which combined with imaging features and EB virus nucleic acid. The scoring system was convenient, practical and could significantly improve the diagnostic accuracy of ENKTCL and the differential diagnosis of ENKTCL from DLBCL.

Recent Advances in Well-Designed Therapeutic Nanosystems for the Pancreatic Ductal Adenocarcinoma Treatment Dilemma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant tumor with an extremely poor prognosis and low survival rate. Due to its inconspicuous symptoms, PDAC is difficult to diagnose early. Most patients are diagnosed in the middle and late stages, losing the opportunity for surgery. Chemotherapy is the main treatment in clinical practice and improves the survival of patients to some extent. However, the improved prognosis is associated with higher side effects, and the overall prognosis is far from satisfactory. In addition to resistance to chemotherapy, PDAC is significantly resistant to targeted therapy and immunotherapy. The failure of multiple treatment modalities indicates great dilemmas in treating PDAC, including high molecular heterogeneity, high drug resistance, an immunosuppressive microenvironment, and a dense matrix. Nanomedicine shows great potential to overcome the therapeutic barriers of PDAC. Through the careful design and rational modification of nanomaterials, multifunctional intelligent nanosystems can be obtained. These nanosystems can adapt to the environment's needs and compensate for conventional treatments' shortcomings. This review is focused on recent advances in the use of well-designed nanosystems in different therapeutic modalities to overcome the PDAC treatment dilemma, including a variety of novel therapeutic modalities. Finally, these nanosystems' bottlenecks in treating PDAC and the prospect of future clinical translation are briefly discussed.

MRI Extraprostatic Extension Grade: Accuracy and Clinical Incremental Value in the Assessment of Extraprostatic Cancer.

Objective: The purpose was to compare the accuracy of extraprostatic extension (EPE) grade on MRI predicting EPE with Partin tables, Memorial Sloan Kettering Cancer Center nomogram (MSKCCn), and combined models and to analyze the clinical incremental value of EPE grade. Materials and Methods: 105 prostate cancer patients confirmed by pathology after radical prostatectomy in our hospital from 2017 to 2021 were selected. The clinical stage, PSA, Gleason score, number of positive biopsy cores, and percentage of positive biopsy cores were recorded. Evaluate EPE grade according to EPE grade criteria, and calculate the probability of predicting EPE with Partin tables and MSKCCn. EPE grade is combined with Partin tables and MSKCCn to construct EPE grade+Partin tables and EPE grade+MSKCCn models. Calculate the area under the curve (AUC), sensitivity, and specificity of EPE grade, Partin tables, MSKCCn, EPE grade+Partin tables, and EPE grade+MSKCCn and compare their diagnostic efficacy. The clinical decision curve was used to analyze the clinical net income of each prediction scheme. Results: The AUC of EPE grade was 0.79, Partin tables was 0.50, MSKCCn was 0.78, the EPE grade+Partin table model was 0.79, and the EPE grade+MSKCCn model was 0.83. After EPE grade was combined with Partin tables and MSKCCn, the diagnostic efficiency of clinical model was significantly improved (P < 0.05). There was no significant difference in the diagnostic efficacy of the combined model compared with the single EPE grade (P > 0.05). The calibration curve of the combined model shows that it has a good calibration degree for EPE. In the analysis of the decision curve, the net income of the EPE grade is higher than that of Partin tables and MSKCCn and is equal to the EPE grade+Partin tables and is slightly lower than that of EPE grade+MSKCCn. The clinical net income of the combined model is obviously higher than that of individual clinical models. Conclusion: The accuracy of EPE classification in predicting prostate cancer EPE is high, and combined with the clinical model, it can significantly improve the diagnostic efficiency of the clinical model and increase the clinical benefit.

[Effect of Down-Regulation of ANRIL on Proliferation and Apoptosis of Kasumi-1 Cells and Its Potential Mechanism].

OBJECTIVE: To investigate the down-regulation of ANRIL (Antisense non-coding RNA in the INK4 Locus) effects on proliferation and apoptosis of Kasumi-1 cells and its related molecular mechanism. METHODS: Recombinant lentivirus was used to construct ANRIL down-regulation Kasumi-1 cells (sh-ANRIL group) and its control cells (sh-NC group). A fluorescence microscope was used to observe the transfection efficiency, RT-qPCR was used to detect knockdown efficiency and ANRIL expression in PBMCs and MBMCs of patients with acute myeloid leukemia (AML). Proliferation and apoptosis of Kasumi-1 cells were assayed by CCK-8 method and flow cytometry. Western blot was employed to detect the expression of PI3K, AKT, p-AKT, and relevant protein after down-regulation of ANRIL in Kasumi-1 cells. RESULTS: ANRIL overexpressed significantly in PBMCs and MBMCs of patients with AML, the transfection efficiency of recombinant lentivirus carrying sh-ANRIL and sh-NC on Kasumi-1 cells exceeded 90%, and the knockdown efficiency was 70%. When DNR was administrated for 24, 48, and 72 hours, the cell inhibition rate of the sh-ANRIL group was (47.40±1.49)%, (69.11±0.51)% and (91.82±1.10)%, which were significantly higher than those of the sh-NC group, respectively (P<0.05). The apoptotic rate in the sh-ANRIL group was (10.29±0.58)%, which was significantly higher than (5.42±0.67)% of the sh-NC group (P<0.01). After DNR treatment for 24 hours, the apoptotic rate of the sh-ANRIL group was (54.41±1.69)%, which was significantly higher than (38.28±1.42)% of sh-NC group (P<0.001). Western blot revealed that the protein levels of PI3K, p-AKT, PCNA, and BCL-2 in the sh-ANRIL group were reduced significantly than those in the sh-NC group, while the BAX protein expression increased. CONCLUSION: ANRIL may affect the proliferation and apoptosis of Kasumi-1 cells through PI3K/AKT signaling pathway. ANRIL is a potential therapeutic target for AML.

Machine Learning-Based Radiological Features and Diagnostic Predictive Model of Xanthogranulomatous Cholecystitis.

Background: Xanthogranulomatous cholecystitis (XGC) is a rare benign chronic inflammatory disease of the gallbladder that is sometimes indistinguishable from gallbladder cancer (GBC), thereby affecting the decision of the choice of treatment. Thus, this study aimed to analyse the radiological characteristics of XGC and GBC to establish a diagnostic prediction model for differential diagnosis and clinical decision-making. Methods: We investigated radiological characteristics confirmed by the RandomForest and Logistic regression to establish computed tomography (CT), magnetic resonance imaging (MRI), CT/MRI models and diagnostic prediction model, and performed receiver operating characteristic curve (ROC) analysis to prove the effectiveness of the diagnostic prediction model. Results: Based on the optimal features confirmed by the RandomForest method, the mean area under the curve (AUC) of the ROC of the CT and MRI models was 0.817 (mean accuracy = 0.837) and 0.839 (mean accuracy = 0.842), respectively, whereas the CT/MRI model had a considerable predictive performance with the mean AUC of 0.897 (mean accuracy = 0.906). The diagnostic prediction model established for the convenience of clinical application was similar to the CT/MRI model with the mean AUC and accuracy of 0.888 and 0.898, respectively, indicating a preferable diagnostic efficiency in distinguishing XGC from GBC. Conclusions: The diagnostic prediction model showed good diagnostic accuracy for the preoperative discrimination of XGC and GBC, which might aid in clinical decision-making.

A radiologic diagnostic scoring model based on CT features for differentiating gastric schwannoma from gastric gastrointestinal stromal tumors.

We aimed to further explore the CT features of gastric schwannoma (GS), propose and validate a convenient diagnostic scoring system to distinguish GS from gastric gastrointestinal stromal tumors (GISTs) preoperatively. 170 patients with submucosal tumors pathologically confirmed (GS n=35; gastric GISTs n=135) from Hospital 1 were analyzed retrospectively as the training cohort, and 72 patients (GS=11; gastric GISTs=61) from Hospital 2 were enrolled as the validation cohort. We searched for significant CT imaging characteristics and constructed the scoring system via binary logistic regression and converted regression coefficients to weighted scores. The ROC curves, AUCs and calibration tests were carried out to evaluate the scoring models in both the training cohort and the validation cohort. For convenient assessment, the system was further divided into four score ranges and their diagnostic probability of GS was calculated respectively. Four CT imaging characteristics were ultimately enrolled in this scoring system, including transverse position (2 points), location (5 points), perilesional lymph nodes (6 points) and pattern of enhancement (2 points). The AUC of the scoring model in the training cohort were 0.873 (95% CI, 0.816-0.929) and the cutoff point was 6 points. In the validation cohort, the AUC was 0.898 (95% CI, 0.804-0.957) and the cutoff value was 5 points. Four score ranges were as follows: 0-3 points for very low probability of GS, 4-7 points for low probability; 8-9 points for middle probability; 10-15 points for very high probability. A convenient scoring model to preoperatively discriminate GS from gastric GISTs was finally proposed.

Building contrast-enhanced CT-based models for preoperatively predicting malignant potential and Ki67 expression of small intestine gastrointestinal stromal tumors (GISTs).

PURPOSE: To assess contrast-enhanced computed tomography (CE-CT) features for predicting malignant potential and Ki67 in small intestinal gastrointestinal stromal tumors (GISTs) and the correlation between them. METHODS: We retrospectively analyzed the pathological and imaging data for 123 patients (55 male/68 female, mean age: 57.2 years) with a histopathological diagnosis of small intestine GISTs who received CE-CT followed by curative surgery from May 2009 to August 2019. According to postoperatively pathological and immunohistochemical results, patients were categorized by malignant potential and the Ki67 index, respectively. CT features were analyzed to be associated with malignant potential or the Ki67 index using univariate analysis, logistic regression and receiver operating curve analysis. Then, we explored the correlation between the Ki67 index and malignant potential by using the Spearman rank correlation. RESULTS: Based on univariate and multivariate analysis, a predictive model of malignant potential of small intestine GISTs, consisting of tumor size (p < 0.001) and presence of necrosis (p = 0.033), was developed with the area under the receiver operating curve (AUC) of 0.965 (95% CI, 0.915-0.990; p < 0.001), with 91.53% sensitivity, 96.87% specificity, 96.43% PPV, 92.54% NPV, 94.31% diagnostic accuracy. For high Ki67 expression, a model made up of tumor size (p = 0.051), presence of ulceration (p = 0.054) and metastasis (p = 0.001) may be the best predictive combination with an AUC of 0.785 (95% CI, 0.702-0.854; p < 0.001), 63.33% sensitivity, 76.34% specificity, 46.34% PPV, 86.59% NPV, 73.17% diagnostic accuracy. Ki67 index showed a moderate positive correlation with mitotic count (r = 0.578, p < 0.001), a weak positive correlation with tumor size (r = 0.339, p < 0.001) and with risk stratification (r = 0.364, p < 0.001). CONCLUSION: Features on CE-CT could preoperatively predict malignant potential and high Ki67 expression of small intestine GISTs, and Ki67 index may be a promising prognostic factor in predicting the prognosis of small intestine GISTs, independent of the risk stratification system.

A nomogram diagnostic prediction model of pancreatic metastases of small cell lung carcinoma based on clinical characteristics, radiological features and biomarkers.

Objective: To investigate clinical characteristics, radiological features and biomarkers of pancreatic metastases of small cell lung carcinoma (PM-SCLC), and establish a convenient nomogram diagnostic predictive model to differentiate PM-SCLC from pancreatic ductal adenocarcinomas (PDAC) preoperatively. Methods: A total of 299 patients with meeting the criteria (PM-SCLC n=93; PDAC n=206) from January 2016 to March 2022 were retrospectively analyzed, including 249 patients from hospital 1 (training/internal validation cohort) and 50 patients from hospital 2 (external validation cohort). We searched for meaningful clinical characteristics, radiological features and biomarkers and determined the predictors through multivariable logistic regression analysis. Three models: clinical model, CT imaging model, and combined model, were developed for the diagnosis and prediction of PM-SCLC. Nomogram was constructed based on independent predictors. The receiver operating curve was undertaken to estimate the discrimination. Results: Six independent predictors for PM-SCLC diagnosis in multivariate logistic regression analysis, including clinical symptoms, CA199, tumor size, parenchymal atrophy, vascular involvement and enhancement type. The nomogram diagnostic predictive model based on these six independent predictors showed the best performance, achieved the AUCs of the training cohort (n = 174), internal validation cohort (n = 75) and external validation cohort (n = 50) were 0.950 (95%CI, 0.917-0.976), 0.928 (95%CI, 0.873-0.971) and 0.976 (95%CI, 0.944-1.00) respectively. The model achieved 94.50% sensitivity, 83.20% specificity, 86.80% accuracy in the training cohort and 100.00% sensitivity, 80.40% specificity, 86.70% accuracy in the internal validation cohort and 100.00% sensitivity, 88.90% specificity, 87.50% accuracy in the external validation cohort. Conclusion: We proposed a noninvasive and convenient nomogram diagnostic predictive model based on clinical characteristics, radiological features and biomarkers to preoperatively differentiate PM-SCLC from PDAC.

A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features.

BACKGROUND: Renal angiomyolipoma without visible fat (RAML-wvf) and clear cell renal cell carcinoma (ccRCC) have many overlapping features on imaging, which poses a challenge to radiologists. This study aimed to create a scoring system to distinguish ccRCC from RAML-wvf using computed tomography imaging. METHODS: A total of 202 patients from 2011 to 2019 that were confirmed by pathology with ccRCC (n=123) or RAML (n=79) were retrospectively analyzed by dividing them randomly into a training cohort (n=142) and a validation cohort (n=60). A model was established using logistic regression and weighted to be a scoring system. ROC, AUC, cut-off point, and calibration analyses were performed. The scoring system was divided into three ranges for convenience in clinical evaluations, and the diagnostic probability of ccRCC was calculated. RESULTS: Four independent risk factors are included in the system: 1) presence of a pseudocapsule, 2) a heterogeneous tumor parenchyma in pre-enhancement scanning, 3) a non-high CT attenuation in pre-enhancement scanning, and 4) a heterogeneous enhancement in CMP. The prediction accuracy had an ROC of 0.978 (95% CI, 0.956-0.999; P=0.011), similar to the primary model (ROC, 0.977; 95% CI, 0.954-1.000; P=0.012). A sensitivity of 91.4% and a specificity of 93.9% were achieved using 4.5 points as the cutoff value. Validation showed a good result (ROC, 0.922; 95% CI, 0.854-0.991, P=0.035). The number of patients with ccRCC in the three ranges (0 to <2 points; 2-4 points; >4 to ≤11 points) significantly increased with increasing scores. CONCLUSION: This scoring system is convenient for distinguishing between ccRCC and RAML-wvf using four computed tomography features.

Radiographic Features and Clinical Factor for Preoperative Prediction in the Bulging Duodenal Papilla With Malignancy.

BACKGROUND: To investigate characteristic clinical and imaging features and establish a scoring system for preoperative prediction of malignancy in the bulging duodenal papilla. METHODS: A total of 147 patients with bulging duodenal papilla (Benign enlargement n = 67; malignant enlargement n = 80) from our hospital between 2010 and 2020 were retrospectively analyzed. We investigated meaningful clinical and CT imaging features and established the score model through logistic regression and weighted. The calibration test, the ROC, AUC, and cut-off points were performed in score model. The model was also divided into three score ranges for convenient clinical evaluation. RESULTS: Three clinical and CT imaging features were finally included in the score model including direct bilirubin (DBil) increase >7 umol/L (3 points), pancreatic duct (PD) dilation >5 mm (2 points), and irregular shape (2 points). The AUCs of the primary predictive model and score model were 0.896 (95% CI, 0.835-0.940) and 0.896 (95% CI, 0.835-0.940), respectively. This scoring system presented with a sensitivity of 78.8% and a specificity of 88.1% when using 2.5 points as cutoff value. Three score ranges were also proposed for convenient clinical use as follows: 0-2 points; 3-4 points; 5-7 points. The number of patients with malignant duodenal papillary enlargement increased with the increasing scores. CONCLUSIONS: We proposed a convenient scoring system to preoperative predict malignancy in the bulging duodenal papilla.

A scoring model for radiologic diagnosis of gastric leiomyomas (GLMs) with contrast-enhanced computed tomography (CE-CT): Differential diagnosis from gastrointestinal stromal tumors (GISTs).

OBJECTIVES: To investigate CT findings and develop a diagnostic score model to differentiate GLMs from GISTs. METHODS: This retrospective study included 109 patients with pathologically confirmed GLMs (n = 46) and GISTs (n = 63) from January 2013 to August 2018 who received CE-CT before surgery. Demographic and radiological features was collected, including lesion location, contour, presence or absence of intralesional necrosis and ulceration, growth pattern, whether the tumor involved EGJ, the long diameter (LD) /the short diameter (SD) ratio, pattern and degree of lesion enhancement. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors and establish a predictive model. Independent predictors for GLMs were weighted with scores based on regression coefficients. A receiver operating characteristic (ROC) curve was created to determine the diagnostic ability of the model. Overall score distribution was divided into four groups to show differentiating probability of GLMs from GISTs. RESULTS: Five CT features were the independent predictors for GLMs diagnosis in multivariate logistic regression analysis, including esophagogastric junction (EGJ) involvement (OR, 367.9; 95 % CI, 5.8-23302.8; P =  0.005), absence of necrosis (OR, 11.9; 95 % CI, 1.0-138. 1; P =  0.048) and ulceration (OR, 151.9; 95 % CI, 1.4-16899.6; P =  0.037), degree of enhancement (OR, 9.3; 95 % CI, 3.2-27.4; P <  0.001), and long diameter/ short diameter (LD/SD) ratio (OR,170.9; 95 % CI, 8.4-3493.4; P =  0.001). At a cutoff of 9 points, AUC for this score model was 0.95, with 95.65 % sensitivity, 79.37 % specificity, 77.19 % PPV, 96.15 % NPV and 86.24 % diagnostic accuracy. An increasing trend was showed in diagnostic probability of GLMs among four groups based on the score (P <  0.001). CONCLUSIONS: The newly designed scoring system is reliable and easy-to-use for GLMs diagnosis by distinguishing from GISTs, including EGJ involvement, absence of ulceration and necrosis, mild enhancement and high LD/SD ratio. The overall score of model ranged from 1 to 17 points, which was divided into 4 groups: 1-7 points, 7-10 points, 10-13 points and 13-17 points, with a diagnostic probability of GLMs 0%, 45 %, 83 % and 100 %, respectively.

Improving the detection rate of prostate cancer in the gray zone of PI-RADS v2 and serum tPSA by using prostate-specific antigen-age volume.

To improve the detection of prostate cancer (PCa) by combining the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) and prostate-specific antigen-age volume (PSA-AV), especially among those in gray zone with PI-RADS v2 score 3 or serum total prostate-specific antigen (tPSA) 4 to 10 ng/mL.The 357 patients were enrolled in this study. The PI-RADS v2 scoring system was used to represent characteristics on multiparametric magnetic resonance imaging (mpMRI). PI-RADS v2 score 3 or tPSA 4 to 10 ng/mL were defined as the gray zone in detecting PCa. The formula equates to the patient age multiplied by the prostate volume, which is divided by the tPSA level. Univariate and multivariate analyses were done to ascertain significant predictors of prostate cancer.In all, 174 (48.7%) were benign prostatic hyperplasia, 183 (51.3%) had PCa. The results showed that PI-RADS v2, tPSA, and PSA-AV were significant independent predictors of prostate cancer. PI-RADS v2 score ≥4 could detect PCa with rate of 82.1%. Serum tPSA ≥10 ng/mL could detect PCa with rate of 66.2%, PSA density (PSAD) ≥0.15 ng/mL/cc with rate of 62.8%, and PSA-AV ≤250 with rate of 83.5%. Combining with PSA-AV ≤250, patients those with tPSA 4 to 10 ng/mL could improve the detection from 36.0% up to 81%, those with PI-RADS v2 score 3 from 28.6% up to 60.0%.PI-RADS v2 and PSA-AV are faithful variables for detecting PCa. And for patients, those in gray zones of PI-RADS v2 and tPSA, PSA-AV can improve detection rate of PCa.

Phytochemical constituents from Scutellaria baicalensis in soluble epoxide hydrolase inhibition: Kinetics and interaction mechanism merged with simulations.

In our search for soluble epoxide hydrolase (sEH) inhibitors from plants, we found that water extracts of Scutellaria baicalensis Georgi displayed significant inhibitory activity against sEH in vitro. Extracts of S. baicalensiswere separated, resulting in the isolation of thirty compounds (1-30), including six lignins (1-6), sixteen flavones (7-22), and five amides (23-27). Their structures were determined on the basis of1H and13C NMR and MS spectra. Compounds 1-6 were first reported in the genus Scutellaria. All the isolated compounds were assayed for their inhibitory activities against sEH. Compounds 25-27 showed significant inhibitory activities against sEH with IC50 values of 6.06 ±â€¯0.12, 7.83 ±â€¯0.52, and 6.32 ±â€¯0.31 µM, respectively, and compounds 3-6, 12, 18, and 22 displayed moderate inhibitory activities against sEH with IC50 values from 20.82 ±â€¯0.78 µM to 56.61 ±â€¯0.98 µM. The inhibition kinetic analysis results indicated that compounds 25-27 were all uncompetitive. Molecular docking studies were performed to get insights into inhibition mechanisms of compounds 25-27 against sEH.

A prediction model for lymph node metastases using pathologic features in patients intraoperatively diagnosed as stage I non-small cell lung cancer.

BACKGROUND: There is little information on which pattern should be chosen to perform lymph node dissection for stage I non-small-cell lung cancer. This study aimed to develop a model for predicting lymph node metastasis using pathologic features of patients intraoperatively diagnosed as stage I non-small-cell lung cancer. METHODS: We collected pathology data from 284 patients intraoperatively diagnosed as stage I non-small-cell lung cancer who underwent lobectomy with complete lymph node dissection from 2013 through 2014, assessing various factors for an association with metastasis to lymph nodes (age, gender, pathology, tumour location, tumour differentiation, tumour size, pleural invasion, bronchus invasion, multicentric invasion and angiolymphatic invasion). After analysing these variables, we developed a multivariable logistic model to estimate risk of metastasis to lymph nodes. RESULTS: Univariate logistic regression identified tumour size >2.65 cm (p < 0.001), tumour differentiation (p < 0.001), pleural invasion (p = 0.034) and bronchus invasion (p < 0.001) to be risk factors significantly associated with the presence of metastatic lymph nodes. On multivariable analysis, only tumour size >2.65 cm (p < 0.001), tumour differentiation (p = 0.006) and bronchus invasion (p = 0.017) were independent predictors for lymph node metastasis. We developed a model based on these three pathologic factors that determined that the risk of metastasis ranged from 3% to 44% for patients intraoperatively diagnosed as stage I non-small-cell lung cancer. By applying the model, we found that the values y > 0.80, 0.43 < y ≤ 0.80, y ≤ 0.43 plus tumour size >2 cm and y ≤0.43 plus tumour size ≤2 cm yielded positive lymph node metastasis predictive values of 44%, 18%, 14% and 0%, respectively. CONCLUSIONS: A non-invasive prediction model including tumour size, tumour differentiation and bronchus invasion may be useful to give thoracic surgeons recommendations on lymph node dissection for patients intraoperatively diagnosed as Stage I non-small cell lung cancer.

Effect of ART1 on the proliferation and migration of mouse colon carcinoma CT26 cells in vivo.

Arginine-specific mono-ADP-ribosyltransferase 1 (ART1) is an important enzyme that catalyzes arginine-specific mono­ADP­ribosylation. There is evidence that arginine­specific mono­ADP­ribosylation may affect the proliferation of smooth muscle cells via the Rho­dependent signaling pathway. Previous studies have demonstrated that ART1 may have a role in the proliferation, invasion and apoptosis of colon carcinoma in vitro. However, the effect of ART1 on the proliferation and invasion of colon carcinoma in vivo has yet to be elucidated. In the present study, mouse colon carcinoma CT26 cells were infected with a lentivirus to produce ART1 gene silencing or overexpression, and were then subcutaneously transplanted. To observe the effect of ART1 on tumor growth or liver metastasis in vivo, a spleen transplant tumor model of CT26 cells in BALB/c mice was successfully constructed. Expression levels of focal adhesion kinase (FAK), Ras homolog gene family member A (RhoA) and the downstream factors, c­myc, c­fos and cyclooxygenase­2 (COX­2) proteins, were measured in vivo. The results demonstrated that ART1 gene silencing inhibited the growth of the spleen transplanted tumor and its ability to spread to the liver via metastasis. There was also an accompanying increase in expression of FAK, RhoA, c­myc, c­fos and COX­2, whereas CT26 cells with ART1 overexpression demonstrated the opposite effect. These results suggest a potential role for ART1 in the proliferation and invasion of CT26 cells and a possible mechanism in vivo.