A radiomics nomogram model for predicting prognosis of pancreatic ductal adenocarcinoma after high-intensity focused ultrasound surgery.

OBJECTIVE: To develop and validate a radiomics nomogram for predicting the survival of patients with pancreatic ductal adenocarcinoma (PDAC) after receiving high-intensity focused ultrasound (HIFU) treatment. METHODS: A total of 52 patients with PDAC were enrolled. To select features, the least absolute shrinkage and selection operator algorithm were applied, and the radiomics score (Rad-Score) was obtained. Radiomics model, clinics model, and radiomics nomogram model were constructed by multivariate regression analysis. The identification, calibration, and clinical application of nomogram were evaluated. Survival analysis was performed using Kaplan-Meier (K-M) method. RESULTS: According to conclusions made from the multivariate Cox model, Rad-Score, and tumor size were independent risk factors for OS. Compared with the clinical model and radiomics model, the combination of Rad-Score and clinicopathological factors could better predict the survival of patients. Patients were divided into high-risk and low-risk groups according to Rad-Score. K-M analysis showed that the difference between the two groups was statistically significant (p < 0.05). In addition, the radiomics nomogram model indicated better discrimination, calibration, and clinical practicability in training and validation cohorts. CONCLUSION: The radiomics nomogram effectively evaluates the prognosis of patients with advanced pancreatic cancer after HIFU surgery, which could potentially improve treatment strategies and promote individualized treatment of advanced pancreatic cancer.

Changes in the structure and function of the left ventricle in patients with gout: A study based on three-dimensional speckle tracking echocardiography.

PURPOSE: Studies have shown that gout can increase the risk of cardiovascular disease. Three-dimensional speckle-tracking echocardiography (3D-STE), a sensitive imaging technology, enables the detection of subtle myocardial dysfunctions. Our aim is to evaluate the left ventricular (LV) functions in patients with gout using 3D-STE. METHODS: 80 subjects: 40 with gout and 40 as normal controls were involved. We obtained and analyzed these parameters from the dynamic images of a 3D full-volume dataset: global longitudinal strain (GLS), global circumferential strain (GCS), global radial strain (GRS), Twist, 16-segmental time-to-peak longitudinal strain (TTP) and systolic dyssynchrony index (SDI)besides other relevant parameters. RESULTS: Compared with the normal group, gout patients were more likely to have left ventricular remodeling. The patients with gout showed decreased Em, increased E/Em and larger volume index of the left atrium (LAVI) indicating reduced diastolic function. The peak GLS (-17.42 ± 2.02 vs. -22.40 ± 2.57, P < 0.001), GCS (-27.04 ± 3.75 vs. -34.85 ± 4.99, P < 0.001), GRS (38.22 ± 4.28 vs. 46.15 ± 5.17, P < 0.001), and Twist (15.18 ± 5.45 vs. 19.02 ± 5.29, P = 0.015) were significantly lower in patients with gout than in healthy participants. The SDI (5.57 ± 1.46 vs. 4.91 ± 1.19, P = 0.016) was significantly increased in patients with gout compared with normal controls. There was no significant between-group difference in TTP (P = 0.43). The systolic GLS, GRS and GCS peak values increased gradually from the base to the apex, with the lowest values in the basal segment in patients with gout. Receiver-operating characteristic curve analysis revealed among these strains GLS has the largest area under the curve (AUC: 0.93, P < 0.001), the cutoff value of -18.97% with a sensitivity and specificity of 80.0% and 92.0%, respectively, for differentiating two groups. A multivariate linear regression analysis shows that the relationship between gout and strain parameters including GLS, GRS, and GCS is statistically significant (P < 0.001). CONCLUSION: Although patients with gout having a normal ejection fraction, structural remodeling of the left ventricle and subclinical LV deformation may occur. 3D-STE can detect subtle cardiac dysfunctions in patients with gout at an early stage.

Development and validation of a simple-to-use nomogram for predicting minimal change disease based on quantification of color Doppler sonography data from a region of interest.

OBJECTIVES: To establish a simple-to-use nomogram based on quantification of color Doppler sonography data from a region of interest (ROI) to diagnose minimal change disease (MCD) promptly and non-invasively, and to evaluate the prediction capability of the nomogram. METHODS: We recruited 564 patients with pathology-proven renal disease who were admitted to our hospital from July 2020 to July 2021 (388 patients in the training dataset and 176 patients in the validation dataset), and their color Doppler sonography data were acquired from a ROI and underwent ipsilateral renal biopsy. The collected clinical features and ultrasonic features were imported into Rstuido and statistically significant features were selected by stepwise regression using the forward-backward method. Multivariate Logistic regression analysis was combined with clinical analysis to obtain the final modeling features. General and dynamic nomogram models were constructed with the selected features, depending on whether they were MCD or not. Bootstrapping and internal validation were used for internal and external validation of the nomogram, respectively. The performance of the nomogram was assessed by C-index, calibration curve, and receiver operating characteristic (ROC) curve. RESULTS: Age and VI were independent factors in predicting MCD. The value of Age (Best cut-off value: 33.5 years) combined with VI (Best cut-off value: 40.50 points) in the diagnosis of MCD was significantly higher than that of single diagnosis (AUC 0.901, 95% CI 0.863-0.938). The C-index of the nomogram constructed with age and VI in the training and validation datasets was 0.915 [95% confidence interval (CI) 0.874-0.956 and 0.875 95% CI 0.783-0.967], respectively. Calibration curves were fitted well. The sensitivity, specificity, and accuracy were 76.1%, 95.6%, and 78.3%, respectively, in the training dataset, and 74.1%, 94.4%, and 76.1% in the validation dataset, respectively. CONCLUSION: The nomogram constructed with age and VI showed a satisfactory degree of differentiation and accuracy, which is of great significance for early, non-invasively, and individually analysis of the risk of MCD.

Nomogram model for predicting early onset of chronic kidney disease using color Doppler region of interest technique.

PURPOSE: The risk factors of chronic kidney disease were analyzed by using the region of interest quantitative technology of color Doppler combined with QLab software, and a Nomogram was established to conduct an individualized assessment of patients with chronic kidney disease. METHODS: A total of 500 patients with chronic kidney disease diagnosed in our hospital from June 2019 to March 2021 were selected as the chronic kidney disease group, and 300 healthy patients during the same period were selected as the control group. Univariate analysis was performed on the test indexes and the vascularity index, flow index, and vascularization flow index measured by the color doppler region of interest quantitative technique. The above meaningful indicators were included in the Logistics regression analysis to obtain the independent risk factors of early chronic kidney disease. The independent risk factors were imported into R software to draw a Nomogram model for predicting early chronic kidney disease and evaluate the model. RESULTS: Single factor analysis results suggest age, hypertension, diabetes, hyperlipidemia, disease of heart head blood-vessel, body mass index, vascularity index, flow index, and vascularization flow index, fasting blood sugar, triglyceride, total cholesterol, urea nitrogen, creatinine, uric acid, glomerular filtration rate differences statistically significant (P < 0.05). Logistics regression analysis showed that hypertension, diabetes, flow index, and vascularization flow index, urea nitrogen, and albumin were independent risk factors for the early occurrence of chronic kidney disease. The C-index of this Nomogram using independent risk factors is 0.896 (95%CI 0.862-0.930), which indicates that the Nomogram has good discriminant power. The receiver operating curve of the histograph was area under the curve (AUC) 0.884 (95%CI 0.860-0.908). The receiver operator characteristic curve (ROC) of urea nitrogen, albumin, flow index, and vascularization flow index were evaluated. The results indicated that the best cutoff value of urea nitrogen was 5.9 mmol/L, flow index was 14.67, vascularization flow index was 4.6, and albumin was 40.26 g/L. CONCLUSION: In the prediction of chronic kidney disease I-II stage, the quantitative technique of color Doppler region of interest has certain diagnostic value. The model established in this study has good discriminative power and can be applied to clinical practice, giving certain indicative significance.