文章：

肝内胆管癌的影像组学分析：病理数据的无创预测——一项开发临床-影像组学模型的多中心研究

Radiomic Analysis of Intrahepatic Cholangiocarcinoma: Non-Invasive Prediction of Pathology Data: A Multicenter Study to Develop a Clinical–Radiomic Model

原文发布日期：22 August 2023

DOI: 10.3390/cancers15174204

类型: Article

开放获取: 是

英文摘要：

Standard imaging cannot assess the pathology details of intrahepatic cholangiocarcinoma (ICC). We investigated whether CT-based radiomics may improve the prediction of tumor characteristics. All consecutive patients undergoing liver resection for ICC (2009-2019) in six high-volume centers were evaluated for inclusion. On the preoperative CT, we segmented the ICC (Tumor-VOI, i.e., volume-of-interest) and a 5-mm parenchyma rim around the tumor (Margin-VOI). We considered two types of pathology data: tumor grading (G) and microvascular invasion (MVI). The predictive models were internally validated. Overall, 244 patients were analyzed: 82 (34%) had G3 tumors and 139 (57%) had MVI. For G3 prediction, the clinical model had an AUC = 0.69 and an Accuracy = 0.68 at internal cross-validation. The addition of radiomic features extracted from the portal phase of CT improved the model performance (Clinical data+Tumor-VOI: AUC = 0.73/Accuracy = 0.72; +Tumor-/Margin-VOI: AUC = 0.77/Accuracy = 0.77). Also for MVI prediction, the addition of portal phase radiomics improved the model performance (Clinical data: AUC = 0.75/Accuracy = 0.70; +Tumor-VOI: AUC = 0.82/Accuracy = 0.73; +Tumor-/Margin-VOI: AUC = 0.82/Accuracy = 0.75). The permutation tests confirmed that a combined clinical–radiomic model outperforms a purely clinical one (p< 0.05). The addition of the textural features extracted from the arterial phase had no impact. In conclusion, the radiomic features of the tumor and peritumoral tissue extracted from the portal phase of preoperative CT improve the prediction of ICC grading and MVI.

摘要翻译： 

常规影像学检查无法评估肝内胆管癌（ICC）的病理学细节。本研究旨在探讨基于CT的影像组学能否提高肿瘤特征的预测能力。我们评估了六家大型医疗中心连续接受肝切除术的ICC患者（2009-2019年）的纳入情况。在术前CT图像上，我们分割了ICC病灶（肿瘤感兴趣体积，Tumor-VOI）及肿瘤周围5毫米的肝实质边缘区域（边缘感兴趣体积，Margin-VOI）。研究分析了两种病理学数据：肿瘤分级（G）和微血管侵犯（MVI）。预测模型进行了内部验证。共纳入244例患者进行分析：82例（34%）为G3级肿瘤，139例（57%）存在MVI。在G3预测方面，临床模型内部交叉验证的曲线下面积（AUC）为0.69，准确率为0.68。加入CT门静脉期影像组学特征后，模型性能得到提升（临床数据+肿瘤感兴趣体积：AUC=0.73/准确率=0.72；+肿瘤/边缘感兴趣体积：AUC=0.77/准确率=0.77）。在MVI预测方面，加入门静脉期影像组学特征同样改善了模型性能（临床数据：AUC=0.75/准确率=0.70；+肿瘤感兴趣体积：AUC=0.82/准确率=0.73；+肿瘤/边缘感兴趣体积：AUC=0.82/准确率=0.75）。置换检验证实临床-影像组学联合模型显著优于单纯临床模型（p<0.05）。而加入动脉期纹理特征对模型性能无显著影响。综上所述，从术前CT门静脉期提取的肿瘤及瘤周组织影像组学特征能够提高ICC分级和MVI的预测准确性。

原文链接：

Radiomic Analysis of Intrahepatic Cholangiocarcinoma: Non-Invasive Prediction of Pathology Data: A Multicenter Study to Develop a Clinical–Radiomic Model