PDF(Pubmed)
Abstract：
UNASSIGNED: Biomarkers for prediction of outcome in patients with pancreatic cancer are wanted in order to personalize the treatment. This study investigated the value of longitudinal diffusion-weighted magnetic resonance imaging (DWI) for prediction of overall survival (OS) in patients with locally advanced pancreatic cancer (LAPC) treated with stereotactic body radiotherapy (SBRT).
UNASSIGNED: The study included 45 patients with LAPC who received 5 fractions of 10 Gy on a 1.5T MRI-Linac. DWI was acquired prior to irradiation at each fraction. The analysis included baseline values and time-trends of the apparent diffusion coefficient (ADC) and DWI parameters obtained using a decomposition method. A multivariable Cox proportional hazards model for OS was made using best-subset selection, using cross-validation based on Bootstrap.
UNASSIGNED: The median OS from the first day of SBRT was 15.5 months (95% CI: 13.2-20.6), and the median potential follow-up time was 19.8 months. The best-performing multivariable model for OS included two decomposition-based DWI parameters: one baseline and one time-trend parameter. The C-Harrell index describing the model\'s discriminating power was 0.754. High baseline ADC values were associated with reduced OS, whereas no association between the ADC time-trend and OS was observed.
UNASSIGNED: Decomposition-based DWI parameters indicated value in the prediction of OS in LAPC. A DWI time-trend parameter was included in the best-performing model, indicating a potential benefit of acquiring longitudinal DWI during the SBRT course. These findings support both baseline and longitudinal DWI as candidate prognostic biomarkers, which may become tools for personalization of the treatment of patients with LAPC.
UNASSIGNED: The study included 45 patients with LAPC who received 5 fractions of 10 Gy on a 1.5T MRI-Linac. DWI was acquired prior to irradiation at each fraction. The analysis included baseline values and time-trends of the apparent diffusion coefficient (ADC) and DWI parameters obtained using a decomposition method. A multivariable Cox proportional hazards model for OS was made using best-subset selection, using cross-validation based on Bootstrap.
UNASSIGNED: The median OS from the first day of SBRT was 15.5 months (95% CI: 13.2-20.6), and the median potential follow-up time was 19.8 months. The best-performing multivariable model for OS included two decomposition-based DWI parameters: one baseline and one time-trend parameter. The C-Harrell index describing the model\'s discriminating power was 0.754. High baseline ADC values were associated with reduced OS, whereas no association between the ADC time-trend and OS was observed.
UNASSIGNED: Decomposition-based DWI parameters indicated value in the prediction of OS in LAPC. A DWI time-trend parameter was included in the best-performing model, indicating a potential benefit of acquiring longitudinal DWI during the SBRT course. These findings support both baseline and longitudinal DWI as candidate prognostic biomarkers, which may become tools for personalization of the treatment of patients with LAPC.
摘要：
■为了个性化治疗,需要用于预测胰腺癌患者的结果的生物标志物。这项研究调查了纵向扩散加权磁共振成像（DWI）对接受立体定向放疗（SBRT）治疗的局部晚期胰腺癌（LAPC）患者总生存期（OS）的预测价值。
■该研究包括45例LAPC患者，他们在1.5TMRI-Linac上接受了5分10Gy。在每个部分的辐照之前获取DWI。分析包括使用分解方法获得的表观扩散系数（ADC）和DWI参数的基线值和时间趋势。使用最佳子集选择建立了OS的多变量Cox比例风险模型，使用基于Bootstrap的交叉验证。
■SBRT第一天的中位OS为15.5个月（95％CI：13.2-20.6），中位电位随访时间为19.8个月。操作系统性能最佳的多变量模型包括两个基于分解的DWI参数：一个基线和一个时间趋势参数。描述模型鉴别能力的C-Harrell指数为0.754。高基线ADC值与降低OS相关，而ADC时间趋势和OS之间没有相关性。
■基于分解的DWI参数在LAPC中OS的预测中指示值。DWI时间趋势参数包含在性能最佳的模型中，表明在SBRT课程期间获得纵向DWI的潜在好处。这些发现支持基线和纵向DWI作为候选预后生物标志物，这可能成为个性化治疗LAPC患者的工具。
■该研究包括45例LAPC患者，他们在1.5TMRI-Linac上接受了5分10Gy。在每个部分的辐照之前获取DWI。分析包括使用分解方法获得的表观扩散系数（ADC）和DWI参数的基线值和时间趋势。使用最佳子集选择建立了OS的多变量Cox比例风险模型，使用基于Bootstrap的交叉验证。
■SBRT第一天的中位OS为15.5个月（95％CI：13.2-20.6），中位电位随访时间为19.8个月。操作系统性能最佳的多变量模型包括两个基于分解的DWI参数：一个基线和一个时间趋势参数。描述模型鉴别能力的C-Harrell指数为0.754。高基线ADC值与降低OS相关，而ADC时间趋势和OS之间没有相关性。
■基于分解的DWI参数在LAPC中OS的预测中指示值。DWI时间趋势参数包含在性能最佳的模型中，表明在SBRT课程期间获得纵向DWI的潜在好处。这些发现支持基线和纵向DWI作为候选预后生物标志物，这可能成为个性化治疗LAPC患者的工具。
