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Is the radiomic approach, utilizing diffusion-weighted imaging (DWI), capable of predicting the various pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC)? Furthermore, which model demonstrates superior performance among the diverse algorithms currently available? The objective of our study is to develop DWI radiomic models based on different machine learning algorithms and identify the optimal prediction model. We undertook a retrospective analysis of the DWI data of 77 patients with IMCC confirmed by pathological testing. Fifty-seven patients initially included in the study were randomly assigned to either the training set or the validation set in a ratio of 7:3. We established four different classifier models, namely random forest (RF), support vector machines (SVM), logistic regression (LR), and gradient boosting decision tree (GBDT), by manually contouring the region of interest and extracting prominent radiomic features. An external validation of the model was performed with the DWI data of 20 patients with IMCC who were subsequently included in the study. The area under the receiver operating curve (AUC), accuracy (ACC), precision (PRE), sensitivity (REC), and F1 score were used to evaluate the diagnostic performance of the model. Following the process of feature selection, a total of nine features were retained, with skewness being the most crucial radiomic feature demonstrating the highest diagnostic performance, followed by Gray Level Co-occurrence Matrix lmc1 (glcm-lmc1) and kurtosis, whose diagnostic performances were slightly inferior to skewness. Skewness and kurtosis showed a negative correlation with the pathological grading of IMCC, while glcm-lmc1 exhibited a positive correlation with the IMCC pathological grade. Compared with the other three models, the SVM radiomic model had the best diagnostic performance with an AUC of 0.957, an accuracy of 88.2%, a sensitivity of 85.7%, a precision of 85.7%, and an F1 score of 85.7% in the training set, as well as an AUC of 0.829, an accuracy of 76.5%, a sensitivity of 71.4%, a precision of 71.4%, and an F1 score of 71.4% in the external validation set. The DWI-based radiomic model proved to be efficacious in predicting the pathological grade of IMCC. The model with the SVM classifier algorithm had the best prediction efficiency and robustness. Consequently, this SVM-based model can be further explored as an option for a non-invasive preoperative prediction method in clinical practice. [ABSTRACT FROM AUTHOR]

Abstract Objectives To develop radiomics models based on multi-sequence MRI from two centers for the preoperative prediction of the WHO/ISUP grade of Clear Cell Renal Cell Carcinoma (ccRCC). Methods This retrospective study included 334 ccRCC patients from two centers. Significant clinical factors were identified through univariate and multivariate analyses. MRI sequences included Dynamic contrast-enhanced MRI, axial fat-suppressed T2-weighted imaging, diffusion-weighted imaging, and in-phase/out-of-phase images. Feature selection methods and logistic regression (LR) were used to construct clinical and radiomics models, and a combined model was developed using the Rad-score and significant clinical factors. Additionally, seven classifiers were used to construct the combined model and different folds LR was used to construct the combined model to evaluate its performance. Models were evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), and decision curve analysis (DCA). The Delong test compared ROC performance, with p

Abstract Background Renal cold ischemia-reperfusion injury (CIRI), a pathological process during kidney transplantation, may result in delayed graft function and negatively impact graft survival and function. There is a lack of an accurate and non-invasive tool for evaluating the degree of CIRI. Multi-parametric MRI has been widely used to detect and evaluate kidney injury. The machine learning algorithms introduced the opportunity to combine biomarkers from different MRI metrics into a single classifier. Objective To evaluate the performance of multi-parametric magnetic resonance imaging for grading renal injury in a rat model of renal cold ischemia-reperfusion injury using a machine learning approach. Methods Eighty male SD rats were selected to establish a renal cold ischemia -reperfusion model, and all performed multiparametric MRI scans (DWI, IVIM, DKI, BOLD, T1mapping and ASL), followed by pathological analysis. A total of 25 parameters of renal cortex and medulla were analyzed as features. The pathology scores were divided into 3 groups using K-means clustering method. Lasso regression was applied for the initial selecting of features. The optimal features and the best techniques for pathological grading were obtained. Multiple classifiers were used to construct models to evaluate the predictive value for pathology grading. Results All rats were categorized into mild, moderate, and severe injury group according the pathologic scores. The 8 features that correlated better with the pathologic classification were medullary and cortical Dp, cortical T2*, cortical Fp, medullary T2*, ∆T1, cortical RBF, medullary T1. The accuracy(0.83, 0.850, 0.81, respectively) and AUC (0.95, 0.93, 0.90, respectively) for pathologic classification of the logistic regression, SVM, and RF are significantly higher than other classifiers. For the logistic model and combining logistic, RF and SVM model of different techniques for pathology grading, the stable and perform are both well. Based on logistic regression, IVIM has the highest AUC (0.93) for pathological grading, followed by BOLD(0.90). Conclusion The multi-parametric MRI-based machine learning model could be valuable for noninvasive assessment of the degree of renal injury.

Background Fibrinogen (FIB) is often elevated in children with Henoch Schonlein purpuric nephritis (HSPN), but the correlation between FIB and renal lesions has been less studied. Objective To explore the correlation between FIB in children with HSPN and the International Study Group on Pediatric Kidney Disease (ISKDC) pathology grading and micropathological changes in parts of renal units, and to clarify whether FIB can assess the severity of renal injury in children with HSPN. Methods In total, 922 children with HSPN who were hospitalized in the First Affiliated Hospital of Henan University of Chinese Medicine in the pediatric nephrology ward and underwent kidney biopsy at the same time from December 2017 to December 2022 were collected, and the clinical information, FIB and renal pathological information during renal biopsy were summarized, and based on the FIB level, the children were categorized into group A (low) 4.98 g/L. The correlation between FIB and ISKDC pathological grades, glomerular mesangial hyperplasia ratio, the crescentic bodies ratio and the nature of the glomerular lesions from acute to chronic was investigated by Spearman rank correlation analysis, and the prediction of FIB on the micropathological changes of renal units was analyzed by the subject's work characteristic (ROC) curve. Results Among 922 children with HSPN who had undergone renal biopsy, the FIB was (3.48±1.01) g/L. 113 cases in group A had a low FIB rate of 12.26%; 734 cases in group B had a standardized FIB rate of 79.61%; and 75 cases in group C had a high FIB rate of 8.13%. The ISKDC pathology classification was type Ⅱa in 173 cases (18.76%), type Ⅱb in 29 cases (3.15%), 466 cases (50.54%) of type Ⅲa, 232 cases (25.16%) of type Ⅲb, and 22 cases (2.39%) of type Ⅳ and above (including 2 cases of type Ⅳa, 18 cases of type Ⅳb, and 2 cases of type Ⅴ). The results of the Spearman's rank correlation analysis showed that the FIB and the grouping of the FIB of the children with HSPN were positively related to the renal pathology ISKDC grading (rs=0.146, P

Objectives: To develop radiomics models based on multi-sequence MRI from two centers for the preoperative prediction of the WHO/ISUP grade of Clear Cell Renal Cell Carcinoma (ccRCC). Methods: This retrospective study included 334 ccRCC patients from two centers. Significant clinical factors were identified through univariate and multivariate analyses. MRI sequences included Dynamic contrast-enhanced MRI, axial fat-suppressed T2-weighted imaging, diffusion-weighted imaging, and in-phase/out-of-phase images. Feature selection methods and logistic regression (LR) were used to construct clinical and radiomics models, and a combined model was developed using the Rad-score and significant clinical factors. Additionally, seven classifiers were used to construct the combined model and different folds LR was used to construct the combined model to evaluate its performance. Models were evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), and decision curve analysis (DCA). The Delong test compared ROC performance, with p < 0.050 considered significant. Results: Multivariate analysis identified intra-tumoral vessels as an independent predictor of high-grade ccRCC. In the external validation set, the radiomics model (AUC = 0.834) outperformed the clinical model (AUC = 0.762), with the combined model achieving the highest AUC (0.855) and significantly outperforming the clinical model (p = 0.003). DCA showed that the combined model had a higher net benefit within the 0.04–0.54 risk threshold range than clinical model. Additionally, the combined model constructed using logistic regression has a higher priority compared to other classifiers. Additionally, 10-fold cross-validation with LR for the combined model showed consistent AUC values (0.849–0.856) across different folds. Conclusion: The radiomics models based on multi-sequence MRI might be a noninvasive and effective tool, demonstrating good efficacy in preoperatively predicting the WHO/ISUP grade of ccRCC. [ABSTRACT FROM AUTHOR]

Background: Renal cold ischemia-reperfusion injury (CIRI), a pathological process during kidney transplantation, may result in delayed graft function and negatively impact graft survival and function. There is a lack of an accurate and non-invasive tool for evaluating the degree of CIRI. Multi-parametric MRI has been widely used to detect and evaluate kidney injury. The machine learning algorithms introduced the opportunity to combine biomarkers from different MRI metrics into a single classifier. Objective: To evaluate the performance of multi-parametric magnetic resonance imaging for grading renal injury in a rat model of renal cold ischemia-reperfusion injury using a machine learning approach. Methods: Eighty male SD rats were selected to establish a renal cold ischemia -reperfusion model, and all performed multiparametric MRI scans (DWI, IVIM, DKI, BOLD, T1mapping and ASL), followed by pathological analysis. A total of 25 parameters of renal cortex and medulla were analyzed as features. The pathology scores were divided into 3 groups using K-means clustering method. Lasso regression was applied for the initial selecting of features. The optimal features and the best techniques for pathological grading were obtained. Multiple classifiers were used to construct models to evaluate the predictive value for pathology grading. Results: All rats were categorized into mild, moderate, and severe injury group according the pathologic scores. The 8 features that correlated better with the pathologic classification were medullary and cortical Dp, cortical T2*, cortical Fp, medullary T2*, ∆T1, cortical RBF, medullary T1. The accuracy(0.83, 0.850, 0.81, respectively) and AUC (0.95, 0.93, 0.90, respectively) for pathologic classification of the logistic regression, SVM, and RF are significantly higher than other classifiers. For the logistic model and combining logistic, RF and SVM model of different techniques for pathology grading, the stable and perform are both well. Based on logistic regression, IVIM has the highest AUC (0.93) for pathological grading, followed by BOLD(0.90). Conclusion: The multi-parametric MRI-based machine learning model could be valuable for noninvasive assessment of the degree of renal injury. [ABSTRACT FROM AUTHOR]

Background: Meningiomas constitute a significant proportion of primary intracranial tumors, demanding a nuanced understanding of their radiological features for informed clinical decisions. This prospective study aimed to explore the intricate relationship between magnetic resonance imaging (MRI) findings and the pathological grade of meningiomas to provide insights into their diverse characteristics. Materials and Methods: A cohort of 52 meningioma patients underwent comprehensive MRI evaluations. The study encompassed various aspects of tumor radiology, including location, peritumoral edema severity, tumor margin distinctiveness, bone infiltration, adjacent bone reaction, apparent diffusion coefficient (ADC) patterns, intratumoral calcifications, bleeding within the tumor, vascularization, and tumor enhancement. Results: The analysis revealed that 73.1% of patients presented with grade 1 meningioma, while 26.9% exhibited grade 2 tumors, with no grade 3 cases detected. Intriguingly, while age and gender did not significantly differ between grades, several MRI findings demonstrated noteworthy distinctions. Grade 2 meningiomas were associated with moderate to severe peritumoral edema, indistinct tumor margins, increased vascularization, and heterogeneous tumor enhancement patterns. Notably, logistic regression analysis indicated that none of the investigated radiological parameters independently predicted the pathological grade of meningioma. Conclusion: These findings emphasize the need for a comprehensive meningioma assessment approach, integrating radiological insights into clinical decision-making and prognosis for enhanced patient care.

Xuejiao Liu, Lei Teng, Junzhu Dai, Hongxue Shao, Rui Chen, Haixiang Li, Jing Li, Huichao Zou Department of Pain Medicine, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, People’s Republic of ChinaCorrespondence: Huichao Zou, Email zouhuichao@hrbmu.edu.cnBackground: The neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) are inflammatory biomarkers. Until now, it is unknown the impact of opioid dosage on perioperative immunity in glioma patients. The aim of this study was to explore the effect of intraoperative opioid dosage on perioperative immune perturbations using NLR and LMR as inflammatory biomarkers and evaluate the correlation between inflammatory biomarkers and pathological grade of glioma.Methods: The study included 208 patients with primary glioma who underwent glioma resection from February 2012 to November 2019 at Harbin Medical University Cancer Hospital. Complete blood count (CBC) was collected at 3 time points: one week before surgery, and 24 hours and one week after surgery. Patients were divided into high-dose and low-dose groups, based on the median value of intraoperative opioid dose. The relationships between perioperative NLR, LMR and intraoperative opioid dosage were analyzed using repeated measurement analysis of variance (ANOVA). Correlations between preoperative various factors and pathological grade were analyzed by Spearman analysis. Receiver operating characteristic (ROC) curves were performed to assess the predictive performance of the NLR and LMR for pathological grade.Results: The NLR (P=0.020) and lower LMR (P=0.037) were statistically significant different between high-dose and low-dose groups one week after surgery. The area under the curve (AUC) of the NLR to identify poor diagnosis was 0.685, which was superior to the LMR (AUC: 0.607) and indicated a correlation between the NLR with pathological grade. The preoperative NLR (P=0.000), LMR (P=0.009), age (P=0.000) and tumor size (P=0.001) exhibited a significant correlation with the pathological grade of glioma.Conclusion: Intraoperative opioids in the high-dose group were associated with higher NLR and lower LMR in postoperative glioma patients. The preoperative NLR and LMR demonstrated predictive value for distinguishing between high-grade and low-grade gliomas.Keywords: glioma, intraoperative opioid dosage, neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, pathological grade

Objective: To construct and validate radiomics models for hepatocellular carcinoma (HCC) grade predictions based on contrast-enhanced CT (CECT). Methods: Patients with pathologically confirmed HCC after surgery and underwent CECT at our institution between January 2016 and December 2020 were enrolled and randomly divided into training and validation datasets. With tumor segmentation and feature extraction, radiomic models were constructed using univariate analysis, followed by least absolute shrinkage and selection operator (LASSO) regression. In addition, combined models with clinical factors and radiomics scores (Radscore) were constructed using logistic regression. Finally, all models were evaluated using the receiver operating characteristic (ROC) curve with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Results: In total 242 patients were enrolled in this study, of whom 170 and 72 formed the training and validation datasets, respectively. The arterial phase and portal venous phase (AP+VP) radiomics model were evaluated as the best for predicting HCC pathological grade among all the models built in our study (AUC = 0.981 in the training dataset; AUC = 0.842 in the validation dataset) andwas used to build a nomogram. Furthermore, the calibration curve and DCA indicated that the AP+VP radiomics model had a satisfactory prediction efficiency. Conclusions: Low- and high-grade HCC can be distinguished with good diagnostic performance using a CECT-based radiomics model. [ABSTRACT FROM AUTHOR]

Background: Meningiomas constitute a significant proportion of primary intracranial tumors, demanding a nuanced understanding of their radiological features for informed clinical decisions. This prospective study aimed to explore the intricate relationship between magnetic resonance imaging (MRI) findings and the pathological grade of meningiomas to provide insights into their diverse characteristics. Materials and Methods: A cohort of 52 meningioma patients underwent comprehensive MRI evaluations. The study encompassed various aspects of tumor radiology, including location, peritumoral edema severity, tumor margin distinctiveness, bone infiltration, adjacent bone reaction, apparent diffusion coefficient (ADC) patterns, intratumoral calcifications, bleeding within the tumor, vascularization, and tumor enhancement. Results: The analysis revealed that 73.1% of patients presented with grade 1 meningioma, while 26.9% exhibited grade 2 tumors, with no grade 3 cases detected. Intriguingly, while age and gender did not significantly differ between grades, several MRI findings demonstrated noteworthy distinctions. Grade 2 meningiomas were associated with moderate to severe peritumoral edema, indistinct tumor margins, increased vascularization, and heterogeneous tumor enhancement patterns. Notably, logistic regression analysis indicated that none of the investigated radiological parameters independently predicted the pathological grade of meningioma. Conclusion: These findings emphasize the need for a comprehensive meningioma assessment approach, integrating radiological insights into clinical decision-making and prognosis for enhanced patient care. [ABSTRACT FROM AUTHOR]

Objectives: To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to predict the pathological grade of pancreatic neuroendocrine tumors (pNETs) in a non-invasive manner. Methods: Patients with pNETs who underwent contrast-enhanced abdominal CT between 2010 and 2022 were included in this retrospective study. Radiomics features were extracted, and five radiomics-based ML models, namely logistic regression (LR), random forest (RF), support vector machine (SVM), XGBoost, and GaussianNB, were developed. The performance of these models was evaluated using a time-independent testing set, and metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) were calculated. The accuracy of the radiomics model was compared to that of needle biopsy. The Shapley Additive Explanation (SHAP) tool and the correlation between radiomics and biological features were employed to explore the interpretability of the model. Results: A total of 122 patients (mean age: 50 ± 14 years; 53 male) were included in the training set, whereas 100 patients (mean age: 48 ± 13 years; 50 male) were included in the testing set. The AUCs for LR, SVM, RF, XGBoost, and GaussianNB were 0.758, 0.742, 0.779, 0.744, and 0.745, respectively, with corresponding accuracies of 73.0%, 70.0%, 77.0%, 71.9%, and 72.9%. The SHAP tool identified two features of the venous phase as the most significant, which showed significant differences among the Ki-67 index or mitotic count subgroups (p < 0.001). Conclusions: An interpretable radiomics-based RF model can effectively differentiate between G1 and G2/3 of pNETs, demonstrating favorable interpretability. Clinical relevance statement: The radiomics-based interpretable model developed in this study has significant clinical relevance as it offers a non-invasive method for assessing the pathological grade of pancreatic neuroendocrine tumors and holds promise as an important complementary tool to traditional tissue biopsy. Key Points: • A radiomics-based interpretable model was developed to predict the pathological grade of pNETs and compared with preoperative needle biopsy in terms of accuracy. • The model, based on CT radiomics, demonstrated favorable interpretability. • The radiomics model holds potential as a valuable complementary technique to preoperative needle biopsy; however, it should not be considered a replacement for biopsy. [ABSTRACT FROM AUTHOR]

Objective: Investigate the expression and potential clinical significance of FK506-binding protein 38 (FKBP38) in breast cancer tissue. Methods: The expression of FKBP38 in 100 cases of normal breast tissue, 300 cases of invasive ductal carcinoma tissue (IDC) and 59 cases of invasive lobular carcinoma tissue (ILC) were detected by immunohistochemistry, and analyzed the relationship between the expression level of FKBP38 protein and clinicopathological parameters of breast cancer. Results: Immunohistochemical results showed that comparing with normal breast tissue, the expression levels of FKBP38 in invasive ductal carcinoma and invasive lobular carcinoma tissue significantly decreased, with a statistical difference (P<0.0001). Further analysis showed that in invasive ductal carcinoma, the expression level of FKBP38 protein was negatively correlated with the pathological grade and clinical stage, and there was a statistical difference, while the expression of FKBP38 protein was negatively correlated with the expression of PR protein. In addition, the current study demonstrated that the expression level of FKBP38 protein in triple-negative breast cancer (TNBC) was much higher than that in non-triple-negative breast cancer, and the expression of FKBP38 in TNBC was negatively correlated with the primary tumor stages. Conclusions: The expression level of FKBP38 protein significantly decreases in breast cancer patients, and it is related to the pathological grade and clinical stage of breast cancer. It is suggested that FKBP38 protein can be used as a potential target for the diagnosis and treatment of breast cancer, but the involving mechanism still needs further exploration. [ABSTRACT FROM AUTHOR]

Objectives: Development and validation of a computed tomography urography (CTU)-based machine learning (ML) model for prediction of preoperative pathology grade of upper urinary tract urothelial carcinoma (UTUC). Methods: A total of 140 patients with UTUC who underwent CTU examination from January 2017 to August 2023 were retrospectively enrolled. Tumor lesions on the unenhanced, medullary, and excretory periods of CTU were used to extract Features, respectively. Feature selection was screened by the Pearson and Spearman correlation analysis, least absolute shrinkage and selection operator algorithm, random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). The logistic regression (LR) was used to screen for independent influencing factors of clinical baseline characteristics. Machine learning models based on different feature datasets were constructed and validated using algorithms such as LR, RF, SVM, and XGBoost. By computing the selected features, a radiomics score was generated, and a diverse feature dataset was constructed. Based on the training set, 16 ML models were created, and their performance was evaluated using the validation set for metrics including sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and others. Results: The training set consisted of 98 patients (mean age: 64.5±10.5 years; 30 males), whereas the validation set consisted of 42 patients (mean age: 65.3±9.78 years; 17 males). Hydronephrosis was the best independent influence factor (p<0.05). The RF model had the best performance in predicting highgrade UTUC, with AUC of 0.914 (95% Confidence Interval [95%CI] 0.852–0.977) and 0.903 (95%CI 0.809–0.997) in the training set and validation set, and accuracy Conclusions: An ML model based on the RF algorithm exhibits excellent predictive performance, offering a non-invasive approach for predicting preoperative high-grade UTUC. [ABSTRACT FROM AUTHOR]

This study was designed to evaluate the diagnostic efficacy of relevant parameters of 18F-prostate-specific membrane antigen (PSMA)-1007 PET/CT in predicting the pathological grade of primary prostate cancer. Briefly, a prospective analysis was performed on 53 patients diagnosed with prostate cancer by systematic puncture biopsy, followed by 18F-PSMA-1007 PET/CT examination prior to treatment within 10 d. The patients were grouped in accordance with the Gleason grading system revised by the International Association of Urology Pathology (ISUP). They were divided into high-grade group (ISUP 4-5 group) and low-grade group (ISUP 1-3 group). The differences in maximum standardized uptake value (SUVmax), tumor-to-background ratio (TBR), intraprostatic PSMA-derived tumor volume (iPSMA-TV), and intraprostatic total lesion PSMA (iTLPSMA) between the high- and low-grade group were statistically significant (p < .001). No significant difference was found for mean standardized uptake value (SUVmean) between the high- and low-grade groups (Z = -1.131, p = .258). Besides, binary multivariate logistic regression analysis showed that only iPSMA-TV and iTL-PSMA were independent predictors of the pathological grading, for which the odds ratios were 18.821 [95% confidence interval (CI): 2.040-173.614, p = .010] and 0.758 (95% CI: 0.613-0.938, p = .011), respectively. The area under the ROC of this regression model was 0.983 (95% CI: 0.958-1.00, p < .001). Only iTL-PSMA was a significant parameter for distinguishing ISUP-4 and ISUP-5 groups (Z = -2.043, p = .041). In a nutshell, 18F-PSMA-1007 PET/CT has good application value in predicting the histopathological grade of primary prostate cancer. Three-dimensional volume metabolism parameters iPSMA-TV and iTL-PSMA were found to be independent predictors for pathological grade. [ABSTRACT FROM AUTHOR]

Abstract Background Spectral CT imaging parameters have been reported to be useful in the differentiation of pathological grades in different malignancies. This study aims to investigate the value of spectral CT in the quantitative assessment of esophageal squamous cell carcinoma (ESCC) with different degrees of differentiation. Methods There were 191 patients with proven ESCC who underwent enhanced spectral CT from June 2018 to March 2020 retrospectively enrolled. These patients were divided into three groups based on pathological results: well differentiated ESCC, moderately differentiated ESCC, and poorly differentiated ESCC. Virtual monoenergetic 40 keV-equivalent image (VMI40keV), iodine concentration (IC), water concentration (WC), effective atomic number (Eff-Z), and the slope of the spectral curve(λHU) of the arterial phase (AP) and venous phase (VP) were measured or calculated. The quantitative parameters of the three groups were compared by using one-way ANOVA and pairwise comparisons were performed with LSD. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of these parameters in poorly differentiated groups and non-poorly differentiated groups. Results There were significant differences in VMI40keV, IC, Eff-Z, and λHU in AP and VP among the three groups (all p 0.05). The VMI40keV, IC, Eff-Z, and λHU in the poorly differentiated group were significantly higher than those in the other groups both in AP and VP (all p

Objective To explore the expression of programmed cell death protein 5 in cervical cancer and its relationship with lymph node metastasis. Methods 98 cases of cervical cancer patients admitted to our hospital were selected as the observation group, and 98 cases of cervical benign lesions were selected as the control group. The expression levels of PDCD5 in serum and lesion tissues of the two groups were compared to analyze the relationship between PDCD5 and pathological features of cervical cancer, and the diagnostic value of PDCD5 in lymph node metastasis of patients with cervical cancer was analyzed by ROC curve. Results The expression levels of PDCD5 in serum and lesion tissue of observation group were lower than those of control group (P < 0.05) . With the increase of clinical stage and pathological grade of cervical cancer, the expression of PDCD5 in serum and lesion tissue was significantly decreased (P < 0.05) . Among the 98 patients with cervical cancer, 32 had lymph node metastasis. The expression levels of PDCD5 in serum and lesion tissue of lymph node metastasis group were lower than those of non-lymph node metastasis group (P < 0.05) . ROC curve results showed that the AUCs of PDCD5 in serum and lesion tissue to predict lymph node metastasis of cervical cancer patients were 0.810 and 0.850, respectively, with no statistical significance (P > 0.05) . Conclusion The Programmed cell death protein 5 is closely related to the pathological features of patients with cervical cancer, and it has a good predictive effect on lymph node metastasis, which is worthy of further study and application. [ABSTRACT FROM AUTHOR]

Objective To investigate the value of tumor volume to uterine volume ratio (N/U) combined with tumor apparent diffusion coefficient (ADC) in predicting the pathological grade of endometrial carcinoma (EC). Methods Data of 107 patients with EC admitted from July 2020 to January 2023 were retrospectively analyzed. All patients underwent preoperative magnetic resonance imaging (MRI), and pathological diagnosis was used as the gold standard. Patients with low differentiation were included in the high grade group, and patients with medium and high differentiation were included in the low grade group. The factors affecting the pathological grading of EC patients were analyzed, and the value of MRI index in predicting the pathological grading of EC patients was analyzed. Results The pathological examination showed that there were 24 high-grade patients and 83 low-grade patients.. Logistic multivariate regression analysis showed that N/U, ADC, rADC internal obturator muscle were factors affecting the pathological grade of EC patients (P < 0.05). ROC curve results showed that the sensitivity of N/U, ADC, rADC internus obturator muscle and their combination to predict the pathological grading of EC patients were 70.83%, 75.00%, 79.17%, 83.33%, the specificity were 71.08%, 79.52%, 78.31%, 85.54%, and the AUC were 0.734, 0.756, 0.741 and 0.891. Conclusion The combination of N/U, ADC, rADC and obturator internal muscle was effective in predicting the preoperative pathological grading of EC patients. [ABSTRACT FROM AUTHOR]

Objective To investigate the utility of CT-enhanced combined with peripheral blood inflammation indicators in preoperative identification of clear cell renal cell carcinoma (CCRCC) according to the World Health Organization/International Society of Urological Pathology (WHO/ISUP) classification. Methods The retrospective study included 144 patients with surgical pathologically confirmed CCRCC were divided into a high-grade group (38 cases) and a low-grade group (106 cases) according to WHO/ISUP classification. Preoperative examinations involved CT-enhanced imaging and assessment of peripheral blood inflammatory indicators. The differences between groups were assessed by conducting univariate analysis of tumor CT signs, quantitative parameter values, neutrophil-to- lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune inflammation index (SII). Multifactorial logistic regression analysis was performed to identify independent predictors. Subsequently, a joint diagnostic model in the form of a nomogram was constructed and presented. The calibration, stability, and net clinical benefit of the nomogram were evaluated. Results The variables of size ( OR = 1.681 P = 0.006), border blur (0R = 5.549 P = 0.003), cortical phase reinforcement rate (OR = 2.577 P<0.001), and SII (OR= 1.189, P = 0.002 ) demonstrated independent predictive value for diagnostic classification of CCRCC WHO/ISUP; the nomogram achieved area under curve (AUC) of 0.910 (0.862-0.958, P < 0.001), exhibiting superior diagnostic performance compared to each individual variable in univariate analysis (all P < 0.05). The calibration curve demonstrated a high level of accuracy in the college setting; the 5-fold cross-validation AUC yielded a consistent value of 0.896, aligning with the nomogram's predictions; and the decision curve exhibited tangible clinical benefit. Conclusion The combination of CT-enhanced imaging and peripheral blood inflammatory indicators enables the diagnosis of CCRCC WHO/ISUP classification, while the nomogram visually presents the diagnostic results, thus possessing significant clinical value. [ABSTRACT FROM AUTHOR]

Background: Spectral CT imaging parameters have been reported to be useful in the differentiation of pathological grades in different malignancies. This study aims to investigate the value of spectral CT in the quantitative assessment of esophageal squamous cell carcinoma (ESCC) with different degrees of differentiation. Methods: There were 191 patients with proven ESCC who underwent enhanced spectral CT from June 2018 to March 2020 retrospectively enrolled. These patients were divided into three groups based on pathological results: well differentiated ESCC, moderately differentiated ESCC, and poorly differentiated ESCC. Virtual monoenergetic 40 keV-equivalent image (VMI40keV), iodine concentration (IC), water concentration (WC), effective atomic number (Eff-Z), and the slope of the spectral curve(λHU) of the arterial phase (AP) and venous phase (VP) were measured or calculated. The quantitative parameters of the three groups were compared by using one-way ANOVA and pairwise comparisons were performed with LSD. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of these parameters in poorly differentiated groups and non-poorly differentiated groups. Results: There were significant differences in VMI40keV, IC, Eff-Z, and λHU in AP and VP among the three groups (all p < 0.05) except for WC (p > 0.05). The VMI40keV, IC, Eff-Z, and λHU in the poorly differentiated group were significantly higher than those in the other groups both in AP and VP (all p < 0.05). In the ROC analysis, IC performed the best in the identification of the poorly differentiated group and non-poorly differentiated group in VP (AUC = 0.729, Sensitivity = 0.829, and Specificity = 0.569 under the threshold of 21.08 mg/ml). Conclusions: Quantitative parameters of spectral CT could offer supplemental information for the preoperative differential diagnosis of ESCC with different degrees of differentiation. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this research was to develop a radiomics model that combines several clinical features for preoperative prediction of the pathological grade of bladder cancer (BCa) using non-enhanced computed tomography (NECT) scanning images. Materials and methods: The computed tomography (CT), clinical, and pathological data of 105 BCa patients attending our hospital between January 2017 and August 2022 were retrospectively evaluated. The study cohort comprised 44 low-grade BCa and 61 high-grade BCa patients. The subjects were randomly divided into training (n = 73) and validation (n = 32) cohorts at a ratio of 7:3. Radiomic features were extracted from NE-CT images. A total of 15 representative features were screened using the least absolute shrinkage and selection operator (LASSO) algorithm. Based on these characteristics, six models for predicting BCa pathological grade, including support vector machine (SVM), k-nearest neighbor (KNN), gradient boosting decision tree (GBDT), logical regression (LR), random forest (RF), and extreme gradient boosting (XGBOOST) were constructed. The model combining radiomics score and clinical factors was further constructed. The predictive performance of the models was evaluated based on the area under the receiver operating characteristic (ROC) curve, DeLong test, and decision curve analysis (DCA). Results: The selected clinical factors for the model included age and tumor size. LASSO regression analysis identified 15 features most linked to BCa grade, which were included in the machine learning model. The SVM analysis revealed that the highest AUC of the model was 0.842. A nomogram combining the radiomics signature and selected clinical variables showed accurate prediction of the pathological grade of BCa preoperatively. The AUC of the training cohort was 0.919, whereas that of the validation cohort was 0.854. The clinical value of the combined radiomics nomogram was validated using calibration curve and DCA. Conclusion: Machine learning models combining CT semantic features and the selected clinical variables can accurately predict the pathological grade of BCa, offering a non-invasive and accurate approach for predicting the pathological grade of BCa preoperatively. [ABSTRACT FROM AUTHOR]

Abstract Purpose To develop and validate a preoperative cystoscopic-based predictive model for predicting postoperative high-grade bladder cancer (BCa), which could be used to guide the surgical selection and postoperative treatment strategies. Materials and methods We retrospectively recruited 366 patients with cystoscopy biopsy for pathology and morphology evaluation between October 2010 and January 2021. A binary logistic regression model was used to assess the risk factors for postoperative high-grade BCa. Diagnostic performance was analyzed by plotting receiver operating characteristic curve and calculating area under the curve (AUC), sensitivity, specificity. From January 2021 to July 2021, we collected 105 BCa prospectively to validate the model's accuracy. Results A total of 366 individuals who underwent transurethral resection of bladder tumor (TURBT) or radical cystectomy following cystoscopy biopsy were included for analysis. 261 (71.3%) had a biopsy pathology grade that was consistent with postoperative pathology grade. We discovered five cystoscopic parameters, including tumor diameter, site, non-pedicled, high-grade biopsy pathology, morphology, were associated with high-grade BCa. The established multi-parameter logistic regression model (“JSPH” model) revealed AUC was 0.917 (P

Purpose: This study aimed to establish a reliable diagnostic score model for the preoperative determination of pathological grade in HCC based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI and biochemical indicators. Methods: In this retrospective study, we analyzed 139 patients with HCC who underwent Gd-EOB-DTPA MRI between 2014 and 2020, including an establishment cohort of 76 patients and a validation cohort of 63 patients. Based on the imaging features demonstrated on Gd-EOB-DTPA MRI images and biochemical indicators of the establishment cohort, a scoring model based on logistic regression was developed, and compared with postoperative pathological findings in terms of effective determination of pathological grade. The validity of the scoring model was assessed by ROC curves and an independent external validation cohort. Results: Three parameters related to pathological grades were identified, including maximum diameter of the tumor, peritumoral hypointensity in the hepatobiliary phase, and [alkaline phosphatase (U/L) + gamma glutamyl transpeptidase (U/L)]/ lymphocyte count (× 109/L) (AGLR) ratios. Based on these three parameters, a scoring model was developed. ROC curve showed that a score of > 5 was set as the threshold for determining pathological grades with accuracy, sensitivity, specificity, PPV, and NPV of 89.5%, 75.0%, 95.1%, 85.7%, and 90.7%, respectively. Conclusion: The study provided the groundwork for a promising and easily implementable scoring model for preoperative determination of HCC pathological grades, for which further validation should be pursued. [ABSTRACT FROM AUTHOR]

This study was designed to evaluate the diagnostic efficacy of relevant parameters of 18 F-prostate-specific membrane antigen (PSMA)-1007 PET/CT in predicting the pathological grade of primary prostate cancer. Briefly, a prospective analysis was performed on 53 patients diagnosed with prostate cancer by systematic puncture biopsy, followed by 18 F-PSMA-1007 PET/CT examination prior to treatment within 10 d. The patients were grouped in accordance with the Gleason grading system revised by the International Association of Urology Pathology (ISUP). They were divided into high-grade group (ISUP 4-5 group) and low-grade group (ISUP 1-3 group). The differences in maximum standardized uptake value (SUVmax), tumor-to-background ratio (TBR), intraprostatic PSMA-derived tumor volume (iPSMA-TV), and intraprostatic total lesion PSMA (iTL-PSMA) between the high- and low-grade group were statistically significant ( p  < .001). No significant difference was found for mean standardized uptake value (SUVmean) between the high- and low-grade groups (Z =  -1.131, p  = .258). Besides, binary multivariate logistic regression analysis showed that only iPSMA-TV and iTL-PSMA were independent predictors of the pathological grading, for which the odds ratios were 18.821 [95% confidence interval (CI): 2.040-173.614, p  = .010] and 0.758 (95% CI: 0.613-0.938, p  = .011), respectively. The area under the ROC of this regression model was 0.983 (95% CI: 0.958-1.00, p  < .001). Only iTL-PSMA was a significant parameter for distinguishing ISUP-4 and ISUP-5 groups (Z =  -2.043, p  = .041). In a nutshell, 18 F-PSMA-1007 PET/CT has good application value in predicting the histopathological grade of primary prostate cancer. Three-dimensional volume metabolism parameters iPSMA-TV and iTL-PSMA were found to be independent predictors for pathological grade.

Is the radiomic approach, utilizing diffusion-weighted imaging (DWI), capable of predicting the various pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC)? Furthermore, which model demonstrates superior performance among the diverse algorithms currently available? The objective of our study is to develop DWI radiomic models based on different machine learning algorithms and identify the optimal prediction model. We undertook a retrospective analysis of the DWI data of 77 patients with IMCC confirmed by pathological testing. Fifty-seven patients initially included in the study were randomly assigned to either the training set or the validation set in a ratio of 7:3. We established four different classifier models, namely random forest (RF), support vector machines (SVM), logistic regression (LR), and gradient boosting decision tree (GBDT), by manually contouring the region of interest and extracting prominent radiomic features. An external validation of the model was performed with the DWI data of 20 patients with IMCC who were subsequently included in the study. The area under the receiver operating curve (AUC), accuracy (ACC), precision (PRE), sensitivity (REC), and F1 score were used to evaluate the diagnostic performance of the model. Following the process of feature selection, a total of nine features were retained, with skewness being the most crucial radiomic feature demonstrating the highest diagnostic performance, followed by Gray Level Co-occurrence Matrix lmc1 (glcm-lmc1) and kurtosis, whose diagnostic performances were slightly inferior to skewness. Skewness and kurtosis showed a negative correlation with the pathological grading of IMCC, while glcm-lmc1 exhibited a positive correlation with the IMCC pathological grade. Compared with the other three models, the SVM radiomic model had the best diagnostic performance with an AUC of 0.957, an accuracy of 88.2%, a sensitivity of 85.7%, a precision of 85.7%, and an F1 score of 85.7% in the training set, as well as an AUC of 0.829, an accuracy of 76.5%, a sensitivity of 71.4%, a precision of 71.4%, and an F1 score of 71.4% in the external validation set. The DWI-based radiomic model proved to be efficacious in predicting the pathological grade of IMCC. The model with the SVM classifier algorithm had the best prediction efficiency and robustness. Consequently, this SVM-based model can be further explored as an option for a non-invasive preoperative prediction method in clinical practice., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Purpose: To develop and validate a preoperative cystoscopic-based predictive model for predicting postoperative high-grade bladder cancer (BCa), which could be used to guide the surgical selection and postoperative treatment strategies.Materials and Methods: We retrospectively recruited 366 patients with cystoscopy biopsy for pathology and morphology evaluation between October 2010 and January 2021. A binary logistic regression model was used to assess the risk factors for postoperative high-grade BCa. Diagnostic performance was analyzed by plotting receiver operating characteristic curve and calculating area under the curve (AUC), sensitivity, specificity. From January 2021 to July 2021, we collected 105 BCa prospectively to validate the model's accuracy.Results: A total of 366 individuals who underwent transurethral resection of bladder tumor (TURBT) or radical cystectomy following cystoscopy biopsy were included for analysis. 261 (71.3%) had a biopsy pathology grade that was consistent with postoperative pathology grade. We discovered five cystoscopic parameters, including tumor diameter, site, non-pedicled, high-grade biopsy pathology, morphology, were associated with high-grade BCa. The established multi-parameter logistic regression model ("JSPH" model) revealed AUC was 0.917 (P < 0.001). Sensitivity and specificity were 86.2% and 84.0%, respectively. And the consistency of pre- and post-operative high-grade pathology was improved from biopsy-based 70.5% to JSPH model-based 85.2%. In a 105-patients prospective validation cohort, the consistency of pre- and post-operative high-grade pathology was increased from 63.1 to 84.2% after incorporation into JSPH model for prediction.Conclusion: The cystoscopic parameters based "JSPH model" is accurate at predicting postoperative pathological high-grade tumors prior to operations. [ABSTRACT FROM AUTHOR]

Objective To investigate the expression of Discs large homolog-associated protein 5（DLGAP5）in glioma tissues and its role in tumor progression. Methods We selected 65 cases of surgically resected glioma tissues and their paired adjacent tissue specimens, including 26 cases of WHO grade Ⅰ-Ⅱ, 39 cases of grade Ⅲ-Ⅳ, 36 cases of p53 wild type and 29 cases of mutant type. The positive expression of DLGAP5 in different grades of gliomas and adjacent tissues was detected by immunohistochemistry. The relationships between different positive expression of DLGAP5 and clinicopathological parameters were analyzed. We selected 30 tumor tissues and 10 adjacent tumor tissues from 65 specimens, and detected the quantitative expression of DLGAP5 by Western blotting. All patients were followed up regularly after operation, the survival rate was calculated, and the survival curve of glioma patients was drawn. We statistically analyzed the relationship between DLGAP5 expression and patients´ prognosis. Results There were 65 cases of glioma tissues, all of which were positive for DLGAP5. There was significant difference in the expression of DLGAP5 between different grades of glioma tissues (P<0. 05). There was no positive expression of DLGAP5 in the adjacent tissues. The relative expression levels of DLGAP5 in the adjacent tissues, low-grade gliomas, and high-grade gliomas were 0. 102 ± 0. 004, 0. 309 ± 0. 237, and 1. 108 ± 0. 237, respectively, and the differences were statistically significant (all P<0. 05). There was significant difference in the expression of DLGAP5 among patients with different grades and different types of p53（P<0. 05）. Logistic regression analysis showed that WHO grade and p53 type were independent influencing factors for DLGAP5 expression in patients (both P<0. 05). The survival rate of patients with high expression of DLGAP5 was lower than that of patients with low expression (P<0. 01). Conclusions DLGAP5 is highly expressed in glioma patients. The expression level of DLGAP5 is related to the WHO grade and p53 classification of glioma. DLGAP5 can be used as a biomarker for judging the prognosis of glioma patients. [ABSTRACT FROM AUTHOR]

Objective: To retrospectively investigate the value of various MRI image menifestations in the hepatobiliary phase (HBP), DWI and T2WI sequences in predicting the pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC). Materials and Methods: Forty-three patients of IMCCs confirmed by pathology were enrolled including 25 cases in well- or moderately-differentiated group and 18 cases in poorly-differentiated group. All patients underwent DWI, T2WI and HBP scan. The Chi square test was used to compare the differences in the general information. Logistic regression analysis was used to analyze the risk factors in predicting the pathological grade of IMCCs. Results: The maximal diameter of the IMCC lesion was < 3 cm in 11 patients, between 3 cm and 6 cm in 15, and > 6 cm in 17. Sixteen cases had intrahepatic metastasis, including 5 in the well- or moderately-differentiated group and 11 in the poorly-differentiated group. Seventeen (39.5%) patients presented with target signs in the DWI sequence, including 9 in the well- or moderately-differentiated group and 8 in the poorly-differentiated group. Twenty (46.5%) patients presented with target signs in the T2WI sequence, including 8 in the well- or moderately-differentiated group and 12 in the poorly-differentiated group. Nineteen cases (54.3%) had a complete hypointense signal ring, including 13 in the well- or moderately-differentiated group and 6 in the poorly-differentiated group. Sixteen (45.7%) cases had an incomplete hypointense signal ring, including 5 in the well- or moderately-differentiated group and 11 in the poorly-differentiated group. The lesion size, intrahepatic metastasis, T2WI signal, and integrity of a hypointense signal ring in HBP were statistically significantly different between two gourps. T2WI signal, presence or non-presence of intrahepatic metastasis, and integrity of hypointense signal ring were the independent influencing factors for pathological grade of IMCC. Conclusion: Target sign in T2WI sequence, presence of intrahepatic metastasis and an incomplete hypointense-signal ring in HBP are more likely to be present in poorly-differentiated IMCCs. [ABSTRACT FROM AUTHOR]

Glioma grading plays an important role in surgical resection. We investigated the ability of different feature reduction methods in support vector machine (SVM)-based diffusion kurtosis imaging (DKI) histogram parameters to distinguish glioma grades. A total of 161 glioma patients who underwent magnetic resonance imaging (MRI) from January 2017 to January 2020 were included retrospectively. The patients were divided into low-grade (n = 61) and high-grade (n = 100) groups. Parametric DKI maps were derived, and 45 features from the DKI maps were extracted semi-automatically for analysis. Three feature selection methods [principal component analysis (PCA), recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO)] were used to establish the glioma grading model with an SVM classifier. To evaluate the performance of SVM models, the receiver operating characteristic (ROC) curves of SVM models for distinguishing glioma grades were compared with those of conventional statistical methods. The conventional ROC analysis showed that mean diffusivity (MD) variance, MD skewness and mean kurtosis (MK) C50 could effectively distinguish glioma grades, particularly MD variance. The highest classification distinguishing AUC was found using LASSO at 0.904 ± 0.069. In comparison, classification AUC by PCA was 0.866 ± 0.061, and 0.899 ± 0.079 by RFE. The SVM-PCA model with the lowest AUC among the SVM models was significantly better than the conventional ROC analysis (z = 1.947, p = 0.013). These findings demonstrate the superiority of DKI histogram parameters by LASSO analysis and SVM for distinguishing glioma grades. [ABSTRACT FROM AUTHOR]

The research aimed to investigate the expression of human leukocyte antigen G (HLA-G) in cancer tissues and normal endometrium and the expression of HLA-G in the three different grades of Endometrial cancer, to determine if HLA-G expression is related with the diagnosis and grading of endometrial cancer. The expression of HLA-G protein was analysed in the primary tumour in 97 tissue samples obtained from endometrial cancer, in which 30 samples were at pathological Grade 1; 37 samples were at Grade 2; 27 samples were at Grade 3; and the other 5 samples were obtained from normal endometrium. The HLA-G protein level was measured by immunohistochemical method and analysed according to the clinicopathological parameters of patients. A statistically significant difference (p <.05) was observed in HLA-G expression between the cancerous tissue and the normal endometrium (p =.0007), and the histochemistry score (H-score) of the negative control was 0.05 ± 0.03 (mean ± SD). Statistically significant correlations were also observed between samples of pathological Grade 1 and Grade 2 (p =.0126), Grade 2 and Grade 3 (p =.0359), Grade 1 and Grade 3 (p =.0001). Endometrial cancer cells express higher levels of HLA-G probably to escape immune surveillance, and HLA-G expression level is related with the pathological grade of endometrial cancer. Therefore, HLA-G detecting and quantifying could possibly help diagnosing, grading and treatment of endometrial cancer. What is already known on this subject? The expression of a member of the non-classical HLA antigens, HLA-G, is one of the main ways for tumour immune escape and progression. The significance of HLA-G in tumour biology has been intensively investigated (Carosella et al. 2015), and now it is widely acknowledged that HLA-G expression in tumours is highly linked with immune suppressive microenvironments, advanced tumour stage, poor therapeutic responses and prognosis (Lin and Yan, 2018). However, to our knowledge, no research has been conducted on the correlation between HLA-G expression and pathological grades of endometrial cancer. What do the results of this study add? Our study demonstrated that the expression of HLA-G plays an important role in the pathological grading of endometrial cancer. What are the implications of these findings for clinical practice and/or further research? Measuring the level of HLA-G expression to help pathological grading of endometrial cancer is important in determining the treatment of patients with endometrial cancer and studying the underlying mechanisms of the development of endometrial cancer, while proving or finding new targeted therapies inhibiting or modifying these processes still requires further investigation. [ABSTRACT FROM AUTHOR]

Medical image processing and its quantitative analysis plays a vital role in several clinical scenarios, such as diagnosis as well as treatment. The results of this study show that there are 478 mRNAs differentially expressed in low grade as compared to anaplastic oligodendroglial tumor (2 Fold greater, p

ObjectiveTo retrospectively investigate the value of various MRI image menifestations in the hepatobiliary phase (HBP), DWI and T2WI sequences in predicting the pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC).Materials and MethodsForty-three patients of IMCCs confirmed by pathology were enrolled including 25 cases in well- or moderately-differentiated group and 18 cases in poorly-differentiated group. All patients underwent DWI, T2WI and HBP scan. The Chi square test was used to compare the differences in the general information. Logistic regression analysis was used to analyze the risk factors in predicting the pathological grade of IMCCs.ResultsThe maximal diameter of the IMCC lesion was < 3 cm in 11 patients, between 3 cm and 6 cm in 15, and > 6 cm in 17. Sixteen cases had intrahepatic metastasis, including 5 in the well- or moderately-differentiated group and 11 in the poorly-differentiated group. Seventeen (39.5%) patients presented with target signs in the DWI sequence, including 9 in the well- or moderately-differentiated group and 8 in the poorly-differentiated group. Twenty (46.5%) patients presented with target signs in the T2WI sequence, including 8 in the well- or moderately-differentiated group and 12 in the poorly-differentiated group. Nineteen cases (54.3%) had a complete hypointense signal ring, including 13 in the well- or moderately-differentiated group and 6 in the poorly-differentiated group. Sixteen (45.7%) cases had an incomplete hypointense signal ring, including 5 in the well- or moderately-differentiated group and 11 in the poorly-differentiated group. The lesion size, intrahepatic metastasis, T2WI signal, and integrity of a hypointense signal ring in HBP were statistically significantly different between two gourps. T2WI signal, presence or non-presence of intrahepatic metastasis, and integrity of hypointense signal ring were the independent influencing factors for pathological grade of IMCC.ConclusionTarget sign in T2WI sequence, presence of intrahepatic metastasis and an incomplete hypointense-signal ring in HBP are more likely to be present in poorly-differentiated IMCCs.

Introduction: Recent studies have suggested that including presence or absence of ground-glass opacity (GGO) may improve the tumor descriptor (T descriptor) classification in clinical stage I NSCLC. In this study, we analyzed prognostic implications of presence or absence of GGO, size of the solid component, and predominant histology to identify the true prognostic determinant for early-stage NSCLC. Methods: We retrospectively examined 384 patients with clinical stage I NSCLC (solid: 242, part solid: 142) who underwent complete resection between 2009 and 2013. Results: Survival curves of the whole cohort revealed good separation using the current TNM classification. Nevertheless, the part-solid group had a favorable prognosis irrespective of solid component size. Conversely, patients in the solid tumor group with tumors between 3 and 4 cm had a worse prognosis than patients whose tumors were less than or equal to 3 cm. Thus, we propose the following novel T descriptor classification: IA, part-solid tumors; IB, solid tumors less than or equal to 3 cm; and IC, solid tumors between 3 and 4 cm. This novel classification system stratified patient prognosis better than the current classification. On pathologic evaluation, the part-solid group always had better prognoses than the solid group in each subgroup divided by pathologic grade. Conclusions: These results suggest that presence of GGO is the true prognostic determinant of stage I NSCLC, irrespective of the size of the solid component. Our novel T descriptor classification system could more accurately predict prognoses of clinical stage I NSCLC cases.

This study explored the value of contrast-enhanced ultrasound (CEUS) in the differentiation of benign and malignant bladder occupying lesions. Eighty-two patients with space-occupying bladder lesions were selected as the research object, there were a total of 105 lesions. The differences of contrast-enhanced ultrasound parameters between benign and malignant lesions were compared, including time to arrival (AT), time to peak (TTP), peak intensity (PI) and washout time (WT). The results showed that 61 malignant lesions were confirmed pathologically after operation, and the sensitivity, specificity and accuracy of CEUS in diagnosing malignant bladder lesions were 90.16%, 90.91% and 90.48% respectively. PI and WT values of the high-grade malignant lesions were significantly higher than those of the low-grade malignant lesions (P<0.05). The PI and WT of T staged T3 lesions were significantly higher than those of Ta-1 and T2 lesions (P<0.05). The areas under the ROC curve of PI and WT for predicting high-grade lesions were 0.712 and 0.888, respectively. The areas under the ROC curve of PI and WT in predicting T stage ≥ T3 lesions were 0.812 and 0.852, respectively. CEUS is of high value in the diagnosis of bladder space occupying lesions, preoperative T staging and pathological grading, and is worthy of clinical use. [ABSTRACT FROM AUTHOR]

This article aims to explore the correlation between CT imaging findings and histopathological grading of lung adenocarcinoma. A retrospective collection of 149 patients with pathologically confirmed lung adenocarcinoma, according to the grading standard, the patients were divided into a moderately well-differentiated group (level I and II), a poorly differentiated group (level II), and the relationship between CT imaging manifestations of lung adenocarcinoma and postoperative pathological grading was analyzed. Results: (1) The smallest and largest diameters of the tumors in the moderately well-differentiated group were significantly lower than those in the poorly differentiated group (P<0.05). (2) 47.4% of the tumor marginal signs in the moderately well-differentiated group showed lobular signs, while 68.5% in the poorly differentiated group, there was a significant difference between the two group (P<0.01). (3) 26. 3% of the moderately differentiated group has ground glass shadow, and 3.7% of the poorly differentiated group, there was a significant difference between the two group (P<0.01). In short, the CT appearance of lung adenocarcinoma is significantly correlated with its pathological grade. [ABSTRACT FROM AUTHOR]

Objective To investigate the difference of FBLIM1 expression between human glioma and normal brain tissue, and the relationship between FBLIM1 expression and glioma pathological grade, IDHl molecular typing and patient prognosis. Methods The data of gliomas were obtained from the public database, and the expression level of FBLIM1 mRNA in different grades and IDH1 typing gliomas and its relationship with patient prognosis were analyzed. The expression levels of FBLIM1 in gliomas and non tumor brain tissues after brain injury were detected by real-time quantitative PCR, Western Blot and immunohistochemical staining. Kaplan Meier was used to analyze the relationship between FBLIM1 expression level and overall survival (OS). Results The analysis of glioma data in public database and the detection of surgically resected glioma samples showed that the expression level of FBLIM1 increased with the increase of glioma pathological grade, and the expression level of FBLIM1 in high-grade glioma was significantly higher than that in non tumor brain tissues; The expression level of FBLIM1 in IDH1 wild-type gliomas was significantly higher than that in IDH1 mutant gliomas (P < 0. 05 - 0. 001). Survival analysis showed that the OS of glioma patients in FBLIM1 high expression group was significantly shorter than that in low expression group, and the OS of IDH1 wild-type high-grade glioma patients in FBLIM1 high expression group was shorter than that in low expression group (P < 0. 05 ~ 0. 005). Conclusions The expression level of FBLIM1 in high-grade gliomas was significantly higher than that in IDH1 mutant gliomas; The prognosis of glioma patients with high expression of FBLIM1 is worse. FBLIM1 may be a biomarker for pathological diagnosis and prognostic evaluation of glioma. [ABSTRACT FROM AUTHOR]

Objectives: To identify the clinical value of ultrasound radiomic features in the preoperative prediction of tumor stage and pathological grade of bladder cancer (BLCA) patients. Methods: We retrospectively collected patients who had been diagnosed with BLCA by pathology. Ultrasound‐based radiomic features were extracted from manually segmented regions of interest. Participants were randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. Radiomic features were Z‐score normalized and submitted to dimensional reduction analysis (including Spearman's correlation coefficient analysis, the random forest algorithm, and statistical testing) for core feature selection. Classifiers for tumor stage and pathological grade prediction were then constructed. Prediction performance was estimated by the area under the curve (AUC) of the receiver operating characteristic curve and was verified by the validation cohort. Results: A total of 5936 radiomic features were extracted from each of the ultrasound images obtained from 157 patients. The BLCA tumor stage and pathological grade prediction models were developed based on 30 and 35 features, respectively. Both models showed good predictive ability. For the tumor stage prediction model, the AUC was 0.94 in the training cohort and 0.84 in the validation cohort. For the pathological grade model, the AUCs obtained were 0.84 in the training cohort and 0.75 in the validation cohort. Conclusions: The ultrasound‐based radiomics models performed well in the preoperative tumor staging and pathological grading of BLCA. These findings should be applied clinically to optimize treatment and to assess prognoses for BLCA. [ABSTRACT FROM AUTHOR]

Pathological grading of meningioma is insufficient to predict recurrence after resection and to guide individualized treatment strategies. One hundred and thirty-three patients with meningiomas who underwent total resection were enrolled in this retrospective study. Univariate analyses were conducted to evaluate the association between factors and recurrence. Least absolute shrinkage and selection operator (Lasso) was used to further select variables to build a logistic model. The predictive efficiency of the model and WHO grade was compared by using receiver operating characteristic curve (ROC), decision curve analysis (DCA), and net reclassification improvement (NRI). Patients were given a new risk layer based on a nomogram. The recurrence of meningioma in different groups was observed through the Kaplan-Meier curve. Univariate analysis demonstrated that 11 risk factors were associated with prognosis (P < 0.05). The result of ROC proved that the quantified risk-scoring system (AUC = 0.853) had a higher benefit than pathological grade (AUC = 0.689, P = 0.011). The incidence of recurrence of the high risk cohort (69%) was significantly higher than that of the low risk cohort (9%) by Kaplan-Meier analysis (P < 0.001). And all patients who did not relapse in the high risk group received adjuvant radiotherapy. The novel risk stratification algorithm has a significant value for the recurrence of meningioma and can help in optimizing the individualized design of clinical therapy. [ABSTRACT FROM AUTHOR]

Pathological grading of meningioma is insufficient to predict recurrence after resection and to guide individualized treatment strategies. One hundred and thirty-three patients with meningiomas who underwent total resection were enrolled in this retrospective study. Univariate analyses were conducted to evaluate the association between factors and recurrence. Least absolute shrinkage and selection operator (Lasso) was used to further select variables to build a logistic model. The predictive efficiency of the model and WHO grade was compared by using receiver operating characteristic curve (ROC), decision curve analysis (DCA), and net reclassification improvement (NRI). Patients were given a new risk layer based on a nomogram. The recurrence of meningioma in different groups was observed through the Kaplan-Meier curve. Univariate analysis demonstrated that 11 risk factors were associated with prognosis (P < 0.05). The result of ROC proved that the quantified risk-scoring system (AUC = 0.853) had a higher benefit than pathological grade (AUC = 0.689, P = 0.011). The incidence of recurrence of the high risk cohort (69%) was significantly higher than that of the low risk cohort (9%) by Kaplan-Meier analysis (P < 0.001). And all patients who did not relapse in the high risk group received adjuvant radiotherapy. The novel risk stratification algorithm has a significant value for the recurrence of meningioma and can help in optimizing the individualized design of clinical therapy.

Purpose: Pathological grade is important for the treatment selection and outcome prediction in bladder cancer (BCa). We aimed to construct a radiomics-clinical nomogram to preoperatively differentiate high-grade BCa from low-grade BCa. Methods: A total of 185 BCa patients who received multiparametric MRI (mpMRI) before surgery between August 2014 and April 2020 were enrolled in our study. Radiomics features were extracted from the largest tumor located on dynamic contrast-enhancement and T2WI images. After feature selection, the synthetic minority over-sampling technique (SMOTE) was performed to balance the minority group (low-grade group). Radiomics signatures were constructed in the training set and assessed in the validation set. Univariable and multivariable logistic regression were applied to build a nomogram. Results: The radiomics signature generated by the least absolute shrinkage and selection operator model achieved the optimal performance for BCa grading in both the SMOTE-balanced training [accuracy: 93.2%, area under the curve (AUC): 0.961] and validation sets (accuracy: 89.9%, AUC: 0.952). A radiomics-clinical nomogram incorporating the radiomics signature and the Vesical Imaging-Reporting and Data System (VI-RADS) score had novel calibration and discrimination both in the training (AUC: 0.956) and validation sets (AUC: 0.958). Decision curve analysis presented the clinical utility of the nomogram for decision-making. Conclusions: The mpMRI-based radiomics signature had the potential to preoperatively predict the pathological grade of BCa. The proposed nomogram combining the radiomics signature with the VI-RADS score improved the diagnostic power, which may aid in clinical decision-making. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To develop an ultrasomics model for preoperative pathological grading of hepatocellular carcinoma (HCC) using contrast-enhanced ultrasound (CEUS).Material and Methods: A total of 235 HCCs were retrospectively enrolled, including 65 high-grade and 170 low-grade HCCs. Representative images of four-phase CEUS were selected from the baseline sonography, arterial, portal venous, and delayed phase images. Tumor ultrasomics features were automatically extracted using Ultrasomics-Platform software. Models were built via the classifier support vector machine, including an ultrasomics model using the ultrasomics features, a clinical model using the clinical factors, and a combined model using them both. Model performances were tested in the independent validation cohort considering efficiency and clinical usefulness.Results: A total of 1502 features were extracted from each image. After the reproducibility test and dimensionality reduction, 25 ultrasomics features and 3 clinical factors were selected to build the models. In the validation cohort, the combined model showed the best predictive power, with an area under the curve value of 0.785 (95% confidence interval [CI] 0.662-0.909), compared to the ultrasomics model of 0.720 (95% CI 0.576-0.864) and the clinical model of 0.665 (95% CI 0.537-0.793). Decision curve analysis suggested that the combined model was clinically useful, with a corresponding net benefit of 0.760 compared to the other two models.Conclusion: We presented an ultrasomics-clinical model based on multiphase CEUS imaging and clinical factors, which showed potential value for the preoperative discrimination of HCC pathological grades. [ABSTRACT FROM AUTHOR]

Abstract Background The therapeutic planning varies for different grades of choroid plexus tumours (CPTs). The aim of this study was to define the similarities and distinctions among MRIs for different grades of CPTs, providing more guidance for clinical decisions. Methods We reviewed the MRI findings in 35 patients with CPT verified by surgical pathology, including 18 choroid plexus papillomas (CPPs, grade I), 11 atypical choroid plexus papillomas (aCPPs, grade II), and 6 choroid plexus carcinomas (CPCs, grade III). Nonparametric testing based on ranks was performed to evaluate the association of pathological grade with MRI findings. Results Among the 35 CPTs, 29 were located in the ventricular system. The tumours were generally slightly hypo- or isointense on T1WI, slightly hyper- or isointense on T2WI, and moderately or strongly enhanced in post-contrast imaging. Twenty cases were accompanied by hydrocephalus. The median tumour longest diameters of CPPs, aCPPs, and CPCs were 28.6, 44.6, and 60.6 mm, respectively. Four cases were purely cystic, 6 were papillary, 10 were lobulated, and 2 were irregular. Three cases had necrosis. The median oedema diameters of CPPs, aCPPs, and CPCs were 0, 0, and 24.1 mm, respectively. The grades of CPTs were statistically associated with tumour longest diameter (r s = 0.68, P

ObjectivesTo externally validate the extraprostatic extension (EPE) grade criteria on MRI and analyze the incremental value of EPE grade to clinical models of prostate cancer.MethodsA consecutive 130 patients who underwent preoperative prostate MRI followed by radical prostatectomy between January 2015 to January 2020 in our institution were retrospectively enrolled. The EPE grade, Cancer of the Prostate Risk Assessment (CAPRA), and Memorial Sloan Kettering Cancer Center nomogram (MSKCCn) score for each patient were assigned. Significant clinicopathological factors in univariate and multivariate analyses were combined with EPE grade to build the Clinical + EPE grade model, and the CAPRA and MSKCCn score were also combined with EPE grade to build the CAPRA + EPE grade and MSKCCn + EPE grade model, respectively. The area under the curve (AUC), sensitivity and specificity of these models were calculated to evaluate their diagnostic performance. Calibration and decision curve analyses were used to analyze their calibration performance and clinical utility.ResultsThe AUC for predicting EPE was 0.767–0.778 for EPE grade, 0.704 for CAPRA, and 0.723 for MSKCCn. After combination with EPE grade, the AUCs of these clinical models increased significantly than using clinical models along (P < 0.05), but was comparable with using EPE grade alone (P > 0.05). The calibration curves of EPE grade, clinical models and combined models showed that these models are well-calibrated for EPE. In the decision curve analysis, EPE grade showed slightly higher net benefit than MSKCCn and CAPRA.ConclusionThe EPE grade showed good performance for evaluating EPE in our cohort and possessed well clinical utility. Further combinations with the EPE grade could improve the diagnostic performance of clinical models.

PurposeTo compare the diagnostic efficiency of the mono-exponential model and bi-exponential model deriving from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating the pathological grade of esophageal squamous cell carcinoma (ESCC).MethodsFifty-four patients with ESCC were divided into three groups of poorly-differentiated (PD), moderately-differentiated (MD), and well-differentiated (WD), and underwent the IVIM-DWI scan. Mono-exponential (Dmono, D*mono, and fmono) and bi-exponential fit parameters (Dbi, D*bi, and fbi) were calculated using the IVIM data for the tumors. Mean parameter values of three groups were compared using a one-way ANOVA followed by post hoc tests. The receiver operating characteristic curve was drawn for differentiating pathological grade of ESCC. Correlations between pathological grades and IVIM parameters were analyzed.ResultsThere were significant differences in fmono and fbi among the PD, MD and WD ESCC groups (all p0.05). The D*mono, Dbi, and D*bi showed no significant difference among the three groups (all p>0.05). The area under the curve (AUC) of Dmono, fmono and fbi in differentiating WD from PD ESCC were 0.764, 0.961 and 0.932, and the sensitivity and specificity were 92.9% and 60%, 92.9% and 90%, 85.7% and 100%, respectively. The AUC of fmono and fbi in differentiating MD from PD ESCC were 0.839 and 0.757, and the sensitivity and specificity were 78.6% and 80%, 85.7% and 70%, respectively. The AUC of fmono and fbi in differentiating MD from WD ESCC were 0.746 and 0.740, and the sensitivity and specificity were 65% and 85%, 80% and 60%, respectively. The pathologically differentiated grade was correlated with all IVIM parameters (all p

Purpose: To compare the diagnostic efficiency of the mono-exponential model and bi-exponential model deriving from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating the pathological grade of esophageal squamous cell carcinoma (ESCC). Methods: Fifty-four patients with ESCC were divided into three groups of poorly-differentiated (PD), moderately-differentiated (MD), and well-differentiated (WD), and underwent the IVIM-DWI scan. Mono-exponential (Dmono, D*mono, and fmono) and bi-exponential fit parameters (Dbi, D*bi, and fbi) were calculated using the IVIM data for the tumors. Mean parameter values of three groups were compared using a one-way ANOVA followed by post hoc tests. The receiver operating characteristic curve was drawn for differentiating pathological grade of ESCC. Correlations between pathological grades and IVIM parameters were analyzed. Results: There were significant differences in fmono and fbi among the PD, MD and WD ESCC groups (all p<0.05). The fmono were 0.32 ± 0.07, 0.23 ± 0.08, and 0.16 ± 0.05, respectively, and the fbi were 0.35 ± 0.08, 0.26 ± 0.10, and 0.18 ± 0.07, respectively. There was a significant difference in the Dmono between the WD and the PD group (1.48 ± 0.51* 10-3 mm2/s versus 1.05 ± 0.44*10-3 mm2/s, p<0.05), but there was no significant difference between the WD and MD groups, MD and PD groups (all p>0.05). The D*mono, Dbi, and D*bi showed no significant difference among the three groups (all p>0.05). The area under the curve (AUC) of Dmono, fmono and fbi in differentiating WD from PD ESCC were 0.764, 0.961 and 0.932, and the sensitivity and specificity were 92.9% and 60%, 92.9% and 90%, 85.7% and 100%, respectively. The AUC of fmono and fbi in differentiating MD from PD ESCC were 0.839 and 0.757, and the sensitivity and specificity were 78.6% and 80%, 85.7% and 70%, respectively. The AUC of fmono and fbi in differentiating MD from WD ESCC were 0.746 and 0.740, and the sensitivity and specificity were 65% and 85%, 80% and 60%, respectively. The pathologically differentiated grade was correlated with all IVIM parameters (all p<0.05). Conclusions: The mono-exponential IVIM model is superior to the bi-exponential IVIM model in differentiating pathological grades of ESCC, which may be a promising imaging method to predict pathological grades of ESCC. [ABSTRACT FROM AUTHOR]

Objectives: To externally validate the extraprostatic extension (EPE) grade criteria on MRI and analyze the incremental value of EPE grade to clinical models of prostate cancer. Methods: A consecutive 130 patients who underwent preoperative prostate MRI followed by radical prostatectomy between January 2015 to January 2020 in our institution were retrospectively enrolled. The EPE grade, Cancer of the Prostate Risk Assessment (CAPRA), and Memorial Sloan Kettering Cancer Center nomogram (MSKCCn) score for each patient were assigned. Significant clinicopathological factors in univariate and multivariate analyses were combined with EPE grade to build the Clinical + EPE grade model, and the CAPRA and MSKCCn score were also combined with EPE grade to build the CAPRA + EPE grade and MSKCCn + EPE grade model, respectively. The area under the curve (AUC), sensitivity and specificity of these models were calculated to evaluate their diagnostic performance. Calibration and decision curve analyses were used to analyze their calibration performance and clinical utility. Results: The AUC for predicting EPE was 0.767–0.778 for EPE grade, 0.704 for CAPRA, and 0.723 for MSKCCn. After combination with EPE grade, the AUCs of these clinical models increased significantly than using clinical models along (P < 0.05), but was comparable with using EPE grade alone (P > 0.05). The calibration curves of EPE grade, clinical models and combined models showed that these models are well-calibrated for EPE. In the decision curve analysis, EPE grade showed slightly higher net benefit than MSKCCn and CAPRA. Conclusion: The EPE grade showed good performance for evaluating EPE in our cohort and possessed well clinical utility. Further combinations with the EPE grade could improve the diagnostic performance of clinical models. [ABSTRACT FROM AUTHOR]

Introduction: The International Association for the Study of Lung Cancer proposed a new grading criteria for invasive adenocarcinoma. However, its utility has not been validated. Methods: Patients who underwent complete resection of lung adenocarcinoma were included in this study. Then, they were divided into the following three groups on the basis of the criteria recently proposed by the International Association for the Study of Lung Cancer: grade 1, lepidic predominant tumor, with less than 20% of high-grade patterns; grade 2, acinar or papillary predominant tumor, with less than 20% of high-grade patterns; and grade 3, any tumor with greater than or equal to 20% of high-grade patterns. Results: Recurrence-free survival (RFS) was significantly different among the proposed grades (p < 0.001). The RFS of patients upgrading from current grade 2 (papillary or acinar predominant tumor) to proposed grade 3 (5-y RFS, 65.2%) was significantly worse than that of patients with proposed grade 2 (77.1%, hazard ratio = 1.882, 95% confidence interval: 1.236–2.866) but not significantly different from that of patients with grade 3 in both the current (micropapillary or solid predominant tumor) and proposed criteria (53.2%, hazard ratio = 0.761, 95% confidence interval: 0.456–1.269). Among patients with pathologic stage 0 or I, RFS was well stratified by the new grading system (p < 0.001) but not among patients with stage II or III (p = 0.334). In the multivariable analysis, the new grading was not a predictive factor of RFS. Conclusions: Although the proposed grading system well stratified RFS in patients with pathologic stage 0 or I lung adenocarcinoma, there is room for improvement.

Kremen2 (Krm2) plays an important role in embryonic development, bone formation, and tumorigenesis as a crucial regulator of classical Wnt/β-catenin signaling pathway. However, the role of Krm2 in gastric cancer is not clear. The aim of this study was to explore the regulatory role of Krm2 in the tumorigenesis and metastasis of gastric cancer. It was demonstrated that, compared to para-cancerous tissues, Krm2 was significantly up-regulated in gastric cancer tissues and was positively correlated with the pathological grade of gastric cancer patients. Given that Krm2 is abundantly expressed in most tested gastric cancer cell lines, Krm2 knockdown cell models were established and further used to construct mice xenograft model. After knocking down Krm2, both the cell survival in vitro and tumorigenesis in vivo of gastric cancer cells were inhibited. At the same time, knockdown of Krm2 induced apoptosis, cell cycle arrest at G2/M phase and repression of migration in gastric cancer cells in vitro. Mechanistically, we found that knockdown of Krm2 suppressed PI3K/Akt pathway. Therefore, we revealed the novel role and the molecular mechanism of Krm2 in promoting the tumorigenesis and metastasis in gastric cancer. Krm2 can be a potent candidate for designing of targeted therapy.

Medical image processing and its quantitative analysis plays a vital role in several clinical scenarios, such as diagnosis as well as treatment. The results of this study show that there are 478 mRNAs differentially expressed in low grade as compared to anaplastic oligodendroglial tumor (2 Fold greater, p < 0.05) group. These genes participated in Gene Ontology and KEGG pathways such as cell migration, cell motility, and cytokine-cytokine receptor interaction. Oligodendroglial classification models derived from advanced imaging will improve the accuracy of tumor grading, provide prognostic information, and have potential to influence treatment decisions. [ABSTRACT FROM AUTHOR]

Kremen2 (Krm2) plays an important role in embryonic development, bone formation, and tumorigenesis as a crucial regulator of classical Wnt/ β -catenin signaling pathway. However, the role of Krm2 in gastric cancer is not clear. The aim of this study was to explore the regulatory role of Krm2 in the tumorigenesis and metastasis of gastric cancer. It was demonstrated that, compared to para-cancerous tissues, Krm2 was significantly up-regulated in gastric cancer tissues and was positively correlated with the pathological grade of gastric cancer patients. Given that Krm2 is abundantly expressed in most tested gastric cancer cell lines, Krm2 knockdown cell models were established and further used to construct mice xenograft model. After knocking down Krm2, both the cell survival in vitro and tumorigenesis in vivo of gastric cancer cells were inhibited. At the same time, knockdown of Krm2 induced apoptosis, cell cycle arrest at G2/M phase and repression of migration in gastric cancer cells in vitro. Mechanistically, we found that knockdown of Krm2 suppressed PI3K/Akt pathway. Therefore, we revealed the novel role and the molecular mechanism of Krm2 in promoting the tumorigenesis and metastasis in gastric cancer. Krm2 can be a potent candidate for designing of targeted therapy. [ABSTRACT FROM AUTHOR]

The correlation between the quantitative parameters of magnetic resonance diffusion tensor imaging (DTI) and the expression of vascular endothelial growth factor (VEGF), matrix metalloproteinase-9 (MMP-9) was analyzed in this study, as well as the diagnostic value of the parameters on pathological grade in brain gliomas. According to the pathological grade, 102 patients with brain gliomas were divided into low-grade group (47 cases) and high-grade group (55 cases). All patients were examined by MRI and DTI, through which the ADC, FA, rADC, rFA and rAD were measured. The expression of VEGF and MMP-9 were detected by immunohistochemistry. The results show that, the rADC, ADC, rAD, FA and rFA in high-grade group were lower than those of low-grade group (P < 0.05). The positive expression of VEGF and MMP-9 in high-grade group were higher than those of low-grade group (P<0.05). The rADC, ADC, rAD, FA and rFA were negatively correlated with VEGF and MMP-9 expression (P < 0.05). The AUC of rADC, ADC, rAD, FA and rFA in diagnosing the grade of brain glioma had a certain value (P < 0.05). In conclusion, the quantitative parameters of DTI were correlated with the expression of VEGF and MMP-9 in brain gliomas, which was helpful to diagnose the grade of brain glioma. [ABSTRACT FROM AUTHOR]