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Purpose：The purpose of this study was to explore the correlation between CT texture features and the pathological grading of Pancreatic Neuroendocrine tumors. Materials and methods: A retrospective study was conducted on 54 patients (32 males and 22 females; average age (55.3 +11.1 years) with Pancreatic Neuroendocrine tumors (from March 2011 to May 2018) All patients had definite pathological diagnosis and grading results. The texture parameters of tumors were extracted from enhanced CT images taken before treatment. All parameters were analyzed by ROC curve, and the parameters with the largest AUC and P < 0.05 under each texture parameter category were selected. The selected parameters are modeled by binary logistic regression, and three models are constructed: image model, texture model and combined model. ROC was used to test the diagnostic capacity of these models. Result：We get the most discriminating image-based parameter (maxvalue, AUC: 0.678, p=0.037) and 5 most discriminating texture parameters (SHAPE_Volume (# vx), GLCM_Correlation, GLRLM_RLNU, NGLDM_Coarseness and GLZLM_ZLNU, AUC: 0.703,0.706, 0.715, 0.676, 0.702, p< 0.05) were selected by ROC analysis. Among them, maxvalue, SHAPE_Volume (# vx), GLCM_Correlation, GLRLM_RLNU and GLZLM_ZLNU tends to indicate pathological high-grade. NGLDM_Coarseness tends to indicate pathological low-grade. The AUC of image-based model is 0.678, with sensitivity of 81.1%, specificity of 52.9%. The AUC of texture model is 0.749, with sensitivity of 51.4%, specificity of 94.1%. The AUC of combined model is 0.766, with sensitivity of 56.8%, specificity of 94.1%. Conclusion: The preoperative enhanced CT image texture analysis to predict the pathological grade of pancreatic neuroendocrine tumors has a potential application.

Purpose: To assess the utility of texture analysis for predicting the pathological degree of differentiation of pancreatic carcinoma (PC). Methods: Eighty-three patients with PC who went through postoperative pathology diagnose and CT examination were selected at Anhui Provincial Hospital. Among them, 34 cases were moderately differentiated, 13 cases were poorly differentiated, and 36 cases were moderately-poorly differentiated. The images in the arterial and venous phase with the lesions at their largest cross section were selected to manually outline the region of interest (ROI) to delineate lesions using open-source software. A total of 396 features were extracted from the ROI using AK software. Spearman correlation analysis and random forest selection by filter (rfSBF) in the caret package of R studio were used to select the discriminating features. The receiver operating characteristic ROC analysis was used to evaluate their discriminative performance. Results: Twelve and 6 features were selected in the arterial and venous phases, respectively. The areas under the ROC curve (AUC) in the arterial phase for diagnosing poorly-differentiated, moderately-differentiated and moderate-poorly differentiated cases were 0.80, 1, and 0.80 in the training group and 0.77, 1, and 0.77 in the test group; in the venous phase, the values were 0.81, 1, and 0.82 in the training group and 0.74, 1, and 0.74 in the test group. Conclusion: Texture analysis based on enhanced CT images can be used as an adjunct for the preoperative assessment of the pathological differentiation of PC. Conclusion: Texture analysis based on contrast-enhanced CT images can be used as an adjunct for the preoperative assessment of the pathological degrees of differentiation of PC.

Background/Aims: There is an increasing risk of end-stage renal disease (ESRD) among Asian people with immunoglobulin A nephropathy (IgAN). A computer-aided system for ESRD prediction in Asian IgAN patients has not been well studied. Methods: We retrospectively reviewed biopsy-proven IgAN patients treated at the Department of Nephrology of the Second Xiangya Hospital from January 2009 to November 2013. Demographic and clinicopathological data were obtained within 1 month of renal biopsy. A random forest (RF) model was employed to predict the ESRD status in IgAN patients. All cases were initially trained and validated, taking advantage of the out-of-bagging(OOB) error. Predictors used in the model were selected according to the Gini impurity index in the RF model and verified by logistic regression analysis. The area under the receiver operating characteristic(ROC) curve (AUC) and F-measure were used to evaluate the RF model. Results: A total of 262 IgAN patients were enrolled in this study with a median follow-up time of 4.66 years. The importance rankings of predictors of ESRD in the RF model were first obtained, indicating some of the most important predictors. Logistic regression also showed that these factors were statistically associated with ESRD status. We first trained an initial RF model using gender, age, hypertension, serum creatinine, 24-hour proteinuria and histological grading suggested by the Clinical Decision Support System for IgAN (CDSS, www.IgAN.net). This 6-predictor model achieved a F-measure of 0.8 and an AUC of 92.57%. By adding Oxford-MEST scores, this model outperformed the initial model with an improved AUC (96.1%) and F-measure (0.823). When C3 staining was incorporated, the AUC was 97.29% and F-measure increased to 0.83. Adding the estimated glomerular filtration rate (eGFR) improved the AUC to 95.45%. We also observed improved performance of the model with additional inputs of blood urea nitrogen (BUN), uric acid, hemoglobin and albumin. Conclusion: In addition to the predictors in the CDSS, Oxford-MEST scores, C3 staining and eGFR conveyed additional information for ESRD prediction in Chinese IgAN patients using a RF model.

The aim of this study was to investigate the value of quantitative 3-Tesla (3T) magnetic resonance (MR) assessment in the diagnosis of breast lesions. A total of 44 patients with breast lesions were selected. All the patients underwent MR plain scanning and T1 dynamic contrast-enhanced imaging. The vascular function parameters of the lesions, namely volume transfer constant (Ktrans), rate constant (Kep), extravascular extracellular volume fraction (Ve) and integrated area under the curve (iAUC), were acquired. These parameters were compared between benign and malignant breast lesions, and also among differential grades of invasive ductal carcinoma. The values of Ktrans, Kep and iAUC were significantly different between the benign and malignant tumors; however, the values of Ve in the benign and malignant tumors were not significantly different. The values of Ktrans, Kep and iAUC in invasive ductal carcinoma were significantly different between grade I and grade II, and between grade I and grade III; however, there was no significant difference between grade II and grade III. The Ve values in invasive ductal carcinoma did not significantly differ among grades I, II and III. Among the vascular function parameters, Ktrans exhibited the highest sensitivity and specificity in the differentiation of benign and malignant lesions. Quantitative 3-T MR assessment is valuable in the diagnosis of benign and malignant breast lesions. It can also provide reference values for the differentiation of the histological grade of breast invasive ductal carcinoma.

We conducted a clinicopathological analysis of chondrosarcomas in 17 patients treated in our institute. The 5- and 10-year overall survival rates of the patients were 72.3% and 61.9%, respectively. The significant prognostic factors were size and histologic grade of the tumor. Sex, age, location of the primary tumor, or the presence of a preceding exostosis did not affect the treatment results significantly. Chondrosarcomas of histologic grades I and II did not metastasize, while all grade III and dedifferentiated chondrosarcomas metastasized to the lung. The local recurrence rate depended on the surgical margin. Wide excision with an adequate surgical margin is important to achieve local control of the chondrosarcoma.