Hu Fang, Zhang Yuhan, Li Man, Liu Chen, Zhang Handan, Li Xiaoming, Liu Sanyuan, Hu Xiaofei, Wang Jian
Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
Department of Radiology, Tongliang District People's Hospital, Chongqing, China.
Front Oncol. 2022 Mar 22;12:853336. doi: 10.3389/fonc.2022.853336. eCollection 2022.
To predict preoperative microvascular invasion (MVI) risk grade by analyzing the radiomics signatures of tumors and peritumors on enhanced magnetic resonance imaging (MRI) images of hepatocellular carcinoma (HCC).
A total of 501 HCC patients (training cohort n = 402, testing cohort n = 99) who underwent preoperative Gd-EOB-DTPA-enhanced MRI and curative liver resection within a month were studied retrospectively. Radiomics signatures were selected using the least absolute shrinkage and selection operator (Lasso) algorithm. Unimodal radiomics models based on tumors and peritumors (10mm or 20mm) were established using the Logistic algorithm, using plain T1WI, arterial phase (AP), portal venous phase (PVP), and hepatobiliary phase (HBP) images. Multimodal radiomics models based on different regions of interest (ROIs) were established using a combinatorial modeling approach. Moreover, we merged radiomics signatures and clinico-radiological features to build unimodal and multimodal clinical radiomics models.
In the testing cohort, the AUC of the dual-region (tumor & peritumor 20 mm)radiomics model and single-region (tumor) radiomics model were 0.741 vs 0.694, 0.733 vs 0.725, 0.667 vs 0.710, and 0.559 vs 0.677, respectively, according to AP, PVP, T1WI, and HBP images. The AUC of the final clinical radiomics model based on tumor and peritumoral 20mm incorporating radiomics features in AP&PVP&T1WI images for predicting MVI classification in the training and testing cohorts were 0.962 and 0.852, respectively.
The radiomics signatures of the dual regions for tumor and peritumor on AP and PVP images are of significance to predict MVI.
通过分析肝细胞癌（HCC）增强磁共振成像（MRI）图像上肿瘤及瘤周的放射组学特征，预测术前微血管侵犯（MVI）风险等级。
回顾性研究501例接受术前钆塞酸二钠增强MRI检查并在1个月内接受根治性肝切除术的HCC患者（训练队列n = 402，测试队列n = 99）。使用最小绝对收缩和选择算子（Lasso）算法选择放射组学特征。基于肿瘤和瘤周（10mm或20mm）的单峰放射组学模型采用Logistic算法建立，使用平扫T1WI、动脉期（AP）、门静脉期（PVP）和肝胆期（HBP）图像。基于不同感兴趣区域（ROI）的多峰放射组学模型采用组合建模方法建立。此外，我们将放射组学特征与临床放射学特征合并，构建单峰和多峰临床放射组学模型。
在测试队列中，根据AP、PVP、T1WI和HBP图像，双区域（肿瘤和瘤周20mm）放射组学模型和单区域（肿瘤）放射组学模型的AUC分别为0.741对0.694、0.733对0.725、0.667对0.710和0.559对0.677。基于肿瘤和瘤周20mm并纳入AP&PVP&T1WI图像放射组学特征的最终临床放射组学模型在训练和测试队列中预测MVI分类的AUC分别为0.962和0.852。
AP和PVP图像上肿瘤和瘤周双区域的放射组学特征对预测MVI具有重要意义。
