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OBJECTIVE: Personalized precision medicine can be facilitated by clinically available preoperative microvascular invasion (MVI) prediction models that are reliable and postoperative MVI pathological grade-related recurrence prediction models that are accurate. In this study, we aimed to compare different mathematical models to derive the best preoperative prediction and postoperative recurrence prediction models for MVI. METHODS: A total of 143 patients with hepatocellular carcinoma (HCC) whose clinical, laboratory, imaging, and pathological data were available were included in the analysis. Logistic regression, Cox proportional hazards regression, LASSO regression with 10-fold cross-validation, stepwise regression, and random forest methods were used for variable screening and predictive modeling. The accuracy and validity of seven preoperative MVI prediction models and five postoperative recurrence prediction models were compared in terms of C-index, net reclassification improvement, and integrated discrimination improvement. RESULTS: Multivariate logistic regression analysis revealed that a preoperative nomogram model with the variables cirrhosis diagnosis, alpha-fetoprotein > 400, and diameter, shape, and number of lesions can predict MVI in patients with HCC reliably. Postoperatively, a nomogram model with MVI grade, number of lesions, capsule involvement status, macrovascular invasion, and shape as the variables was selected after LASSO regression and 10-fold cross-validation analysis to accurately predict the prognosis for different MVI grades. The number and shape of the lesions were the most common predictors of MVI preoperatively and recurrence postoperatively. CONCLUSIONS: Our study identified the best statistical approach for the prediction of preoperative MVI as well as postoperative recurrence in patients with HCC based on clinical, imaging, and laboratory tests results. This could expedite preoperative treatment decisions and facilitate postoperative management.
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Purpose: The main aim of this study is to analyze the relationship between body composition indices and metabolic unhealthy phenotypes in young and middle-aged obese patients and to assess their joint predictive ability. Patients and Methods: A cross-sectional study method was used to select 207 patients who were proposed to undergo weight loss surgery for morbid obesity from March to November 2022. Total adipose tissue (TAT), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), liver fat content (LFC), cross-sectional area (CSAmuscle), and intermuscular adipose tissue (CSAIMAT) of paraspinal muscles were measured using quantitative computed tomography. Participants were categorized into two groups: metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). The receiver operating characteristic curve comprised body composition variables that correlated with MUO, and the area under the curve (AUC) was calculated to compare their prediction capacity for MUO. Results: There were 71 patients with MHO (34.3%) and 136 patients with MUO (65.7%). The VAT, VAT/TAT ratio, LFC, and CSAmuscle was higher in MUO patients than in MHO (all P < 0.001), and SAT was lower than in MHO (P = 0.008). And all of these metrics were correlated with MUO (all P < 0.05). Inclusion of these body composition metrics in the ROC analysis showed that the AUC values for SAT, VAT, VAT/TAT ratio, LFC and CSAmuscle were 0.615, 0.663, 0.727, 0.694, 0.671, respectively, and the combination of the VAT/TAT ratio and the LFC had the ability to predict MUO best (AUC=0.746, P = 0.025). Conclusion: The combined use of VAT/TAT ratio and LFC is superior to the use of these two metrics alone in terms of their ability to predict the MUO, providing a more accurate approach to the management and prevention of obesity-related metabolic risk.
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OBJECTIVES: To retrospectively evaluate the association between the presence of collateral vessels and grade of clear cell renal cell carcinoma (ccRCC) and whether the presence of collateral vessels could serve as a predictor to differentiate high- and low-grade ccRCC. MATERIALS AND METHODS: From May 2018 to September 2022, a total of 160 ccRCC patients with pathological diagnosis were enrolled in this study. Patients were divided into a high-grade group and a low-grade group according to World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading system. The significant variables were extracted based on the univariate analyses using Student t test, Mann-Whitney U test, Chi-square test or Fisher's exact test. Multivariate logistic regression analyses were performed to determine independent factors among extracted variables. We calculated the sensitivity, specificity and their 95% confidence intervals (CI) of collateral vessels for predicting high WHO/ISUP grade to quantify its predictive performance. Furthermore, to investigate the additional predictive contribution of collateral vessels, a primary model and a control model were constructed to predict WHO/ISUP grade. The primary model included all extracted significant variables and the control model included significant variables except collateral vessels. RESULTS: The proportion of ccRCC patients with collateral vessels was significantly larger in high-grade ccRCC than those in low-grade ccRCC (87.5 % vs. 26.8 %, P < 0.001). Multivariate logistic regression analyses showed that the presence of collateral vessels was an independent predictor for high WHO/ISUP grade (P < 0.001). The sensitivity and specificity of the presence of collateral vessels for differentiating high- and low-grade ccRCC were 87.5 % (95 % CI 0.753-0.941) and 73.2 % (95 % CI 0.643-0.806) respectively. Including collateral vessels in predictive model improves predictive performance for WHO/ISUP grade, increasing the area under the curve (AUC) value from 0.889 to 0.914. CONCLUSION: The presence of collateral vessels has high sensitivity and specificity for differentiating high- and low-grade ccRCC and can improve the predictive performance for high WHO/ISUP grade.
