There is considerable potential for integrating transarterial chemoembolization (TACE), programmed death-(ligand)1 (PD-[L]1) inhibitors, and molecular targeted treatments (MTT) in hepatocellular carcinoma (HCC). It is necessary to investigate the therapeutic efficacy and safety of TACE combined with PD-(L)1 inhibitors and MTT in real-world situations. In this nationwide, retrospective, cohort study, 826 HCC patients receiving either TACE plus PD-(L)1 blockades and MTT (combination group, n = 376) or TACE monotherapy (monotherapy group, n = 450) were included from January 2018 to May 2021. The primary endpoint was progression-free survival (PFS) according to modified RECIST. The secondary outcomes included overall survival (OS), objective response rate (ORR), and safety. We performed propensity score matching approaches to reduce bias between two groups. After matching, 228 pairs were included with a predominantly advanced disease population. Median PFS in combination group was 9.5 months (95% confidence interval [CI], 8.4–11.0) versus 8.0 months (95% CI, 6.6–9.5) (adjusted hazard ratio [HR], 0.70, P = 0.002). OS and ORR were also significantly higher in combination group (median OS, 19.2 [16.1–27.3] vs. 15.7 months [13.0–20.2]; adjusted HR, 0.63, P = 0.001; ORR, 60.1% vs. 32.0%; P < 0.001). Grade 3/4 adverse events were observed at a rate of 15.8% and 7.5% in combination and monotherapy groups, respectively. Our results suggest that TACE plus PD-(L)1 blockades and MTT could significantly improve PFS, OS, and ORR versus TACE monotherapy for Chinese patients with predominantly advanced HCC in real-world practice, with an acceptable safety profile.
Pavement performance prediction is a crucial issue in big data maintenance. This paper develops a hybrid grey relation analysis (GRA) and support vector machine regression (SVR) technique to predict pavement performance. The prediction model can solve the shortcomings of the traditional model including a single consideration factor, a short prediction period, and easy overfitting. GAR is employed in selecting the main factors affecting the performance of asphalt pavement. The SVR is performed to predict the performance. Finally, the data collected from the weather station installed on Guangyun Expressway were adopted to verify the validity of the GRA-SVR model. Meanwhile, the contrast with the grey model (GM (1, 1)), genetic algorithm optimization BP[[parms resize(1),pos(50,50),size(200,200),bgcol(156)]]081%, −0.823%, 1.270%, and −4.569%, respectively. The study concluded that the nonlinear and multivariate prediction model established by GRA-SVR has higher precision and operability, which can be used in long-period pavement performance prediction.
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