PURPOSE:This retrospective study is designed to develop a Radiomics-based strategy for preoperatively predicting lymph node (LN) status in the resectable pancreatic ductal adenocarcinoma (PDAC) patients. METHODS:Eighty-five patients with histopathological confirmed PDAC are included, of which 35 are LN metastasis positive and 50 are LN metastasis negative. Initially, 1,124 radiomics features are computed from CT images of each patient. After a series of feature selection, a Radiomics logistic regression (LOG) model is developed. Subsequently, the predictive efficiency of the model is validated using a leave-one-out cross-validation method. The model performance is evaluated on discrimination and compared with the conventional CT evaluation method based on subjective CT image features. RESULTS:Radiomics LOG model is developed based on eight most related radiomics features. Remarkable differences are demonstrated between patients with LN metastasis positive and LN metastasis negative in Radiomics LOG scores namely, 0.535±1.307 (mean±standard deviation) vs. −1.514±1.800 (mean±standard deviation) with p < 0.001. Radiomics LOG model shows significantly higher predictive efficiency compared to the conventional evaluation method of LN status in which areas under ROC curves are AUC = 0.841 with 95% confidence interval (CI: 0.758∼0.925) vs. AUC = 0.682 with (95% CI: 0.566∼0.798). Leave-one-out cross validation indicates that the Radiomics LOG model correctly classifies 70.3% cases, while the conventional CT evaluation method only correctly classifies 57.0% cases. CONCLUSION:A radiomics-based strategy provides an individualized LN status evaluation in PDAC patients, which may help clinicians implement an optimal personalized patient treatment.

中文翻译：

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应用基于放射组学的策略术前预测可切除胰腺导管腺癌的淋巴结转移

目的：这项回顾性研究旨在开发一种基于放射组学的策略，用于术前预测可切除胰腺导管腺癌 (PDAC) 患者的淋巴结 (LN) 状态。方法：85例经病理证实为PDAC的患者，其中LN转移阳性35例，LN转移阴性50例。最初，从每位患者的 CT 图像中计算出 1,124 个放射组学特征。经过一系列特征选择，开发了一个 Radiomics 逻辑回归 (LOG) 模型。随后，使用留一法交叉验证方法验证模型的预测效率。模型性能在判别上进行评估，并与基于主观 CT 图像特征的传统 CT 评估方法进行比较。结果：Radiomics LOG 模型是基于八个最相关的 radiomics 特征开发的。在 Radiomics LOG 评分中，LN 转移阳性和 LN 转移阴性患者之间表现出显着差异，即 0.535±1.307（平均值±标准偏差）与 -1.514±1.800（平均值±标准偏差），p < 0.001。与传统的 LN 状态评估方法相比，Radiomics LOG 模型显示出显着更高的预测效率，其中 ROC 曲线下的面积为 AUC = 0.841，具有 95% 置信区间（CI：0.758∼0.925）vs. AUC = 0.682，具有（95% CI： 0.566∼0.798)。留一法交叉验证表明，Radiomics LOG 模型正确分类了 70.3% 的病例，而传统 CT 评估方法仅正确分类了 57.0% 的病例。结论：