Baseline radiologic features as predictors of efficacy in patients with pancreatic neuroendocrine tumors with liver metastases receiving surufatinib.

IF 7 2区医学 Q1 ONCOLOGY Chinese Journal of Cancer Research Pub Date : 2023-10-30 DOI:10.21147/j.issn.1000-9604.2023.05.09

Jianwei Zhang, Haibin Zhu, Lin Shen, Jie Li, Xiaoyan Zhang, Chunmei Bai, Zhiwei Zhou, Xianrui Yu, Zhiping Li, Enxiao Li, Xianglin Yuan, Wenhui Lou, Yihebali Chi, Nong Xu, Yongmei Yin, Yuxian Bai, Tao Zhang, Dianrong Xiu, Jia Chen, Shukui Qin, Xiuwen Wang, Yujie Yang, Haoyun Shi, Xian Luo, Songhua Fan, Weiguo Su, Ming Lu, Jianming Xu

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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.

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基线放射学特征作为肝转移胰腺神经内分泌肿瘤患者接受苏菲替尼疗效的预测因素。

目的:目前，接受苏菲替尼治疗的胰腺神经内分泌肿瘤伴肝转移(PNELM)患者的治疗前预测并不令人满意。我们的目的是研究放射学特征与疗效/预后之间的关系。方法:我们在SANET-p III期试验(NCT02589821)中招募肝转移患者，并获得对比增强计算机断层扫描(CECT)图像。定性和定量参数包括肝肿瘤边缘、病变体积、增强模式、定位类型和增强比例。采用Cox比例风险模型计算无进展生存期(PFS)和风险比(HR)。采用logistic回归模型分析疗效。结果:在152例基线CECT评估并纳入本分析的患者中，在各种定性和定量参数中，与安慰剂相比，舒非替尼组在中位PFS方面具有统计学上的优势。在接受苏菲替尼治疗的患者(N=100)的多变量分析中，动脉期标准化增强比-病变周围(ASER-peri)越高的患者PFS越长[HR=0.039;95%置信区间(95% CI): 0.003-0.483;P = 0.012)。此外，高增强模式的患者在客观反应比上有改善[31.3% vs. 14.7%，优势比(OR)=3.488;95% ci: 1.024-11.875;P=0.046]，明确的肿瘤边缘与较高的疾病控制率(DCR)相关(89.3% vs. 68.2%， OR=4.535;95% ci: 1.285-16.011;P=0.019)，与定义不清的利润率相比。结论:这些治疗前的放射学特征，即高aser周期、高增强模式和明确的肿瘤边缘，有可能作为接受苏菲替尼治疗的PNELM患者疗效的预测指标。

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来源期刊

Chinese Journal of Cancer Research 医学-肿瘤学

自引率

9.80%

发文量

1726

审稿时长

4.5 months

期刊介绍： Chinese Journal of Cancer Research (CJCR; Print ISSN: 1000-9604; Online ISSN:1993-0631) is published by AME Publishing Company in association with Chinese Anti-Cancer Association.It was launched in March 1995 as a quarterly publication and is now published bi-monthly since February 2013. CJCR is published bi-monthly in English, and is an international journal devoted to the life sciences and medical sciences. It publishes peer-reviewed original articles of basic investigations and clinical observations, reviews and brief communications providing a forum for the recent experimental and clinical advances in cancer research. This journal is indexed in Science Citation Index Expanded (SCIE), PubMed/PubMed Central (PMC), Scopus, SciSearch, Chemistry Abstracts (CA), the Excerpta Medica/EMBASE, Chinainfo, CNKI, CSCI, etc.