Prediction of therapeutic outcome and survival in a transgenic mouse model of pancreatic ductal adenocarcinoma treated with dendritic cell vaccination or CDK inhibitor using MRI texture: a feasibility study.
Prediction of therapeutic outcome and survival in a transgenic mouse model of pancreatic ductal adenocarcinoma treated with dendritic cell vaccination or CDK inhibitor using MRI texture: a feasibility study.
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发表时间：
2020-05
影响因子：
2.2
通讯作者：
A. Eresen;Jia Yang;J. Shangguan;Yu Li;Su Hu;Chong Sun;V. Yaghmai;Al B Benson Iii;Zhuoli Zhang
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文献类型：
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作者：
A. Eresen;Jia Yang;J. Shangguan;Yu Li;Su Hu;Chong Sun;V. Yaghmai;Al B Benson Iii;Zhuoli Zhang
目前尚缺乏一种成熟的方法来评估胰腺导管腺癌（PDAC）现代疗法（如dinaciclib或树突状细胞（DC）疫苗接种）的早期治疗结局。在本文中，我们使用MRI纹理特征开发了多变量模型，以检测在PDAC转基因小鼠模型中dinaciclib药物或DC疫苗治疗后的治疗效果，该模型包括21只LSL-KrasG 12 D; LSL-Trp 53 R172 H; Pdx-1-Cre（KPC）小鼠，用作未治疗对照受试者（n=8）或接受dinaciclib（n=7）或DC疫苗（n=6）治疗的受试者。采用支持向量机（SVM）技术建立了一个三变量线性分类器，用于检测药物或疫苗治疗后肿瘤组织的变化。此外，用五个变量生成多变量回归模型，以预测生存行为和组织病理学肿瘤标志物（纤维化、CK 19和Ki 67）。使用准确性、受试者工作特征曲线下面积（AUC）和决策曲线分析评价诊断性能。使用校正的r平方（Radj 2）评价回归模型。SVM分类器成功区分了肿瘤组织的变化，准确率为95.24%，AUC为0.93。由5个变量生成的多变量模型与组织病理学肿瘤标志物纤维化（Radj 2=0.82，P<0.001）、CK 19（Radj 2=0.92，P<0.001）和Ki 67（Radj 2=0.97，P<0.001）密切相关。此外，通过从MRI数据解释肿瘤特征，多变量回归模型成功地预测了KPC小鼠的存活率（Radj 2=0.91，P<0.001）。结果表明，MRI纹理特征具有很大的潜力，可以生成诊断和预后模型，用于监测dinaciclib药物或DC疫苗治疗后的早期治疗反应，并预测组织病理学肿瘤标志物和长期临床结局。
There is a lack of a well-established approach for assessment of early treatment outcomes for modern therapies for pancreatic ductal adenocarcinoma (PDAC) e.g. dinaciclib or dendritic cell (DC) vaccination. Here, we developed multivariate models using MRI texture features to detect treatment effects following dinaciclib drug or DC vaccine therapy in a transgenic mouse model of PDAC including 21 LSL-KrasG12D ; LSL-Trp53R172H ; Pdx-1-Cre (KPC) mice used as untreated control subjects (n=8) or treated with dinaciclib (n=7) or DC vaccine (n=6). Support vector machines (SVM) technique was performed to build a linear classifier with three variables for detection of tumor tissue changes following drug or vaccine treatments. Besides, multivariate regression models were generated with five variables to predict survival behavior and histopathological tumor markers (Fibrosis, CK19, and Ki67). The diagnostic performance was evaluated using accuracy, area under the receiver operating characteristic curve (AUC) and decision curve analyses. The regression models were evaluated with adjusted r-squared (Radj 2). SVM classifier successfully distinguished changes in tumor tissue with an accuracy of 95.24% and AUC of 0.93. The multivariate models generated with five variables were strongly associated with histopathological tumor markers, fibrosis (Radj 2=0.82, P<0.001), CK19 (Radj 2=0.92, P<0.001) and Ki67 (Radj 2=0.97, P<0.001). Furthermore, the multivariate regression model successfully predicted survival of KPC mice by interpreting tumor characteristics from MRI data (Radj 2=0.91, P<0.001). The results demonstrated that MRI texture features had great potential to generate diagnosis and prognosis models for monitoring early treatment response following dinaciclib drug or DC vaccine treatment and also predicting histopathological tumor markers and long-term clinical outcomes.