Mirza-Aghazadeh-Attari Mohammad, Ambale Venkatesh Bharath, Aliyari Ghasabeh Mounes, Mohseni Alireza, Madani Seyedeh Panid, Borhani Ali, Shahbazian Haneyeh, Ansari Golnoosh, Kamel Ihab R
Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Diagnostics (Basel). 2023 Feb 2;13(3):552. doi: 10.3390/diagnostics13030552.
To study the additive value of radiomics features to the BCLC staging system in clustering HCC patients.
A total of 266 patients with HCC were included in this retrospective study. All patients had undergone baseline MR imaging, and 95 radiomics features were extracted from 3D segmentations representative of lesions on the venous phase and apparent diffusion coefficient maps. A random forest algorithm was utilized to extract the most relevant features to transplant-free survival. The selected features were used alongside BCLC staging to construct Kaplan-Meier curves.
Out of 95 extracted features, the three most relevant features were incorporated into random forest classifiers. The Integrated Brier score of the prediction error curve was 0.135, 0.072, and 0.048 for the BCLC, radiomics, and combined models, respectively. The mean area under the receiver operating curve (ROC curve) over time for the three models was 81.1%, 77.3%, and 56.2% for the combined radiomics and BCLC models, respectively.
Radiomics features outperformed the BCLC staging system in determining prognosis in HCC patients. The addition of a radiomics classifier increased the classification capability of the BCLC model. Texture analysis features could be considered as possible biomarkers in predicting transplant-free survival in HCC patients.
研究影像组学特征对巴塞罗那临床肝癌（BCLC）分期系统在肝细胞癌（HCC）患者聚类中的附加值。
本回顾性研究共纳入266例HCC患者。所有患者均接受了基线磁共振成像检查，并从静脉期和表观扩散系数图上代表病变的三维分割中提取了95个影像组学特征。采用随机森林算法提取与无移植生存期最相关的特征。所选特征与BCLC分期一起用于构建Kaplan-Meier曲线。
在提取的95个特征中，三个最相关的特征被纳入随机森林分类器。BCLC、影像组学和联合模型的预测误差曲线的综合Brier评分分别为0.135、0.072和0.048。三种模型随时间的受试者操作特征曲线（ROC曲线）下的平均面积，联合影像组学和BCLC模型分别为81.1%、77.3%和56.2%。
影像组学特征在确定HCC患者预后方面优于BCLC分期系统。添加影像组学分类器提高了BCLC模型的分类能力。纹理分析特征可被视为预测HCC患者无移植生存期的潜在生物标志物。
