Department of Radiology, Third Xiangya Hospital, Central South University, Changsha 410013.
Department of Radiology, Guangdong 999 Brain Hospital, Guangzhou 510080, China.
Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2022 Aug 28;47(8):1049-1057. doi: 10.11817/j.issn.1672-7347.2022.220027.
Microvascular invasion (MVI) is an important predictor of postoperative recurrence or poor outcomes of hepatocellular carcinoma (HCC). Radiomics is able to predict MVI in HCC preoperatively. This study aims to investigate the influence of different region of interest (ROI) sizes on CT-based radiomics model for MVI prediction in HCC.
Patients with HCC with or without MVI confirmed by pathology and those who underwent preoperative plain or enhanced abdominal CT scans in the Third Xiangya Hospital of Central South University from January 2010 to December 2020 were retrospectively and consecutively included. According to the ratio of 7 to 3, the patients were randomly assigned into a training set and a validation set. Clinical data were collected from medical records, and radiomics features were extracted from the arterial phase (AP) and portal venous phase (PVP) of preoperatively acquired CT in all patients. Six different ROI sizes were employed. The original ROI (OROI) was manually delineated along the visible borders of the tumor layer-by-layer. The OROI was expanded out by 1-5 mm. The OROI was combined with 5 different peritumoral regions to generate the other 5 ROIs, named Plus1-Plus5. Feature extraction, dimension reduction, and model development were conducted in 6 different ROIs separately. Supporter vector machine (SVM) was used for model construction. Model performance was assessed via receiver operating characteristic (ROC) curve.
A total of 172 HCC patients were included, in which 83 (48.3%) were MVI positive, and 89 (51.7%) were MVI negative. Three hundred and ninety-six features based on AP or PVP images were extracted from each ROI. After feature selection and dimension reduction, 4, 5, 15, 11, 6, and 3 features of OROI, Plus1, Plus2, Plus 3, Plus4, and Plus5 were selected for model construction, respectively. In the training set, the sensitivity, specificity, and area under the curve (AUC) of OROI were 0.759, 0.806, and 0.855, respectively. The AUC values of Plus2 (0.979) and Plus3 (0.954) were higher than that of OROI. The AUC values of Plus1 (0.802), Plus4 (0.792), and Plus5 (0.774) were not significantly different from those of OROI. In the validation set, the sensitivity, specificity, and AUC value of OROI were 0.640, 0.630, and 0.664, respectively. The AUC value of Plus3 was 0.903, which was higher than that of OROI. The AUC values of Plus1 (0.679), Plus2 (0.536), Plus4 (0.708), and Plus5 (0.757) were not significantly different from that of OROI (>0.05).
The size of ROI significantly inflluences on the performance of CT-based radiomics model for MVI prediction in HCC. Including appropriate area around the tumor into ROI could improve the predictive performance of the model, and 3 mm might be appropriate distance.
微血管侵犯（MVI）是肝细胞癌（HCC）术后复发或预后不良的重要预测指标。放射组学能够在术前预测 HCC 的 MVI。本研究旨在探讨不同感兴趣区域（ROI）大小对 CT 基放射组学模型预测 HCC 中 MVI 的影响。
回顾性连续纳入 2010 年 1 月至 2020 年 12 月在中南大学湘雅三医院接受术前平扫或增强腹部 CT 扫描且经病理证实有或无 MVI 的 HCC 患者。根据 7:3 的比例，患者被随机分配到训练集和验证集中。从病历中收集临床数据，并从所有患者术前 CT 的动脉期（AP）和门静脉期（PVP）中提取放射组学特征。使用了 6 种不同的 ROI 大小。原始 ROI（OROI）沿肿瘤层的可见边界逐层手动勾画。OROI 向外扩展 1-5mm。将 OROI 与 5 个不同的瘤周区域相结合，生成另外 5 个 ROI，分别命名为 Plus1-Plus5。分别在 6 种不同的 ROI 中进行特征提取、降维和模型开发。支持向量机（SVM）用于构建模型。通过接收者操作特征（ROC）曲线评估模型性能。
共纳入 172 例 HCC 患者，其中 83 例（48.3%）MVI 阳性，89 例（51.7%）MVI 阴性。从每个 ROI 提取了基于 AP 或 PVP 图像的 396 个特征。经过特征选择和降维后，OROI、Plus1、Plus2、Plus 3、Plus4 和 Plus5 分别选择了 4、5、15、11、6 和 3 个特征进行模型构建。在训练集中，OROI 的灵敏度、特异性和曲线下面积（AUC）分别为 0.759、0.806 和 0.855。Plus2（0.979）和 Plus3（0.954）的 AUC 值高于 OROI。Plus1（0.802）、Plus4（0.792）和 Plus5（0.774）的 AUC 值与 OROI 无显著差异。在验证集中，OROI 的灵敏度、特异性和 AUC 值分别为 0.640、0.630 和 0.664。Plus3 的 AUC 值为 0.903，高于 OROI。Plus1（0.679）、Plus2（0.536）、Plus4（0.708）和 Plus5（0.757）的 AUC 值与 OROI 无显著差异（>0.05）。
ROI 的大小显著影响 HCC 中基于 CT 的放射组学模型预测 MVI 的性能。将肿瘤周围适当的区域纳入 ROI 可以提高模型的预测性能，而 3mm 可能是合适的距离。
