Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
First Clinical Medical College of Nanjing Medical University, Nanjing 210029, China.
Contrast Media Mol Imaging. 2022 Jun 25;2022:7693631. doi: 10.1155/2022/7693631. eCollection 2022.
To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC).
In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature selection with the least absolute shrinkage and operator (LASSO) and built a radiomics signature. Another improved model (radiomics index) was established using forward conditional multivariate logistic regression. Both models were tested in an internal validation group (38 HCC and 21 HH).
The radiomic signature we built including 5 radiomic features demonstrated significant differences between the hepatic HH and HCC groups ( < 0.05). The improved model demonstrated a higher net benefit based on only 2 radiomic features. In the validation group, radiomics signature and radiomics index achieved great diagnostic performance with AUC values of 0.716 (95% confidence interval (CI): 0.581, 0.850) and 0.870 (95% CI: 0.782, 0.957), respectively.
Our developed radiomics-based model can successfully distinguish HH and HCC patients, which can help clinical decision-making with lower cost.
基于平扫 CT 形成一个放射组学模型，以区分肝血管瘤（HH）和肝细胞癌（HCC）。
本回顾性研究共纳入 110 例患者，其中 HCC 患者 72 例，HH 患者 38 例。我们采用最小绝对值收缩和选择算子（LASSO）进行特征选择，并构建了放射组学特征。另一个改进模型（放射组学指数）采用前向条件多元逻辑回归建立。两个模型均在内部验证组（38 例 HCC 和 21 例 HH）中进行了测试。
我们构建的包括 5 个放射组学特征的放射组学特征能够显著区分 HH 组和 HCC 组（<0.05）。改进模型仅基于 2 个放射组学特征，具有更高的净获益。在验证组中，放射组学特征和放射组学指数的 AUC 值分别为 0.716（95%置信区间（CI）：0.581，0.850）和 0.870（95% CI：0.782，0.957），具有很好的诊断性能。
我们开发的基于放射组学的模型可以成功区分 HH 和 HCC 患者，有助于临床决策，且成本更低。
