ABSTRACT
Objective: Currently, pre-treatment prediction of patients with pancreatic neuroendocrine tumors with liver metastases (PNELM) receiving surufatinib treatment was unsatisfying. Our objective was to examine the association between radiological characteristics and efficacy/prognosis. Methods: We enrolled patients with liver metastases in the phase III, SANET-p trial (NCT02589821) and obtained contrast-enhanced computed tomography (CECT) images. Qualitative and quantitative parameters including hepatic tumor margins, lesion volumes, enhancement pattern, localization types, and enhancement ratios were evaluated. The progression-free survival (PFS) and hazard ratio (HR) were calculated using Cox's proportional hazard model. Efficacy was analyzed by logistic-regression models. Results: Among 152 patients who had baseline CECT assessments and were included in this analysis, the surufatinib group showed statistically superior efficacy in terms of median PFS compared to placebo across various qualitative and quantitative parameters. In the multivariable analysis of patients receiving surufatinib (N=100), those with higher arterial phase standardized enhancement ratio-peri-lesion (ASER-peri) exhibited longer PFS [HR=0.039; 95% confidence interval (95% CI): 0.003-0.483; P=0.012]. Furthermore, patients with a high enhancement pattern experienced an improvement in the objective response ratio [31.3% vs. 14.7%, odds ratio (OR)=3.488; 95% CI: 1.024-11.875; P=0.046], and well-defined tumor margins were associated with a higher disease control rate (DCR) (89.3% vs. 68.2%, OR=4.535; 95% CI: 1.285-16.011; P=0.019) compared to poorly-defined margins. Conclusions: These pre-treatment radiological features, namely high ASER-peri, high enhancement pattern, and well-defined tumor margins, have the potential to serve as predictive markers of efficacy in patients with PNELM receiving surufatinib.
ABSTRACT
The evaluation of a meteorological disaster can be regarded as a multiple-criteria decision making problem because it involves many indexes. Firstly, a comprehensive indexing system for an agricultural meteorological disaster is proposed, which includes the disaster rate, the inundated rate, and the complete loss rate. Following this, the relative weights of the three criteria are acquired using a novel proposed evolutionary algorithm. The proposed algorithm consists of a differential evolution algorithm and an evolution strategy. Finally, a novel evaluation model, based on the proposed algorithm and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), is presented to estimate the agricultural meteorological disaster of 2008 in China. The geographic information system (GIS) technique is employed to depict the disaster. The experimental results demonstrated that the agricultural meteorological disaster of 2008 was very serious, especially in Hunan and Hubei provinces. Some useful suggestions are provided to relieve agriculture meteorological disasters.
