Table_2_Intratumoral and peritumoral radiomics model based on abdominal ultrasound for predicting Ki-67 expression in patients with hepatocellular cancer.docx

BackgroundHepatocellular cancer (HCC) is one of the most common tumors worldwide, and Ki-67 is highly important in the assessment of HCC. Our study aimed to evaluate the value of ultrasound radiomics based on intratumoral and peritumoral tissues in predicting Ki-67 expression levels in patients with HCC.
MethodsWe conducted a retrospective analysis of ultrasonic and clinical data from 118 patients diagnosed with HCC through histopathological examination of surgical specimens in our hospital between September 2019 and January 2023. Radiomics features were extracted from ultrasound images of both intratumoral and peritumoral regions. To select the optimal features, we utilized the t-test and the least absolute shrinkage and selection operator (LASSO). We compared the area under the curve (AUC) values to determine the most effective modeling method. Subsequently, we developed four models: the intratumoral model, the peritumoral model, combined model #1, and combined model #2.
ResultsOf the 118 patients, 64 were confirmed to have high Ki-67 expression while 54 were confirmed to have low Ki-67 expression. The AUC of the intratumoral model was 0.796 (0.649-0.942), and the AUC of the peritumoral model was 0.772 (0.619-0.926). Furthermore, combined model#1 yielded an AUC of 0.870 (0.751-0.989), and the AUC of combined model#2 was 0.762 (0.605-0.918). Among these models, combined model#1 showed the best performance in terms of AUC, accuracy, F1-score, and decision curve analysis (DCA).
ConclusionWe presented an ultrasound radiomics model that utilizes both intratumoral and peritumoral tissue information to accurately predict Ki-67 expression in HCC patients. We believe that incorporating both regions in a proper manner can enhance the diagnostic performance of the prediction model. Nevertheless, it is not sufficient to include both regions in the region of interest (ROI) without careful consideration.

背景：肝细胞癌（Hepatocellular cancer, HCC）是全球最常见的恶性肿瘤之一，Ki-67在肝细胞癌的病情评估中具有重要临床价值。本研究旨在评估基于瘤内及瘤周组织的超声放射组学（ultrasound radiomics）模型，对肝细胞癌患者Ki-67表达水平的预测效能。
方法：本研究回顾性分析了2019年9月至2023年1月期间，我院经手术标本组织病理学检查确诊为肝细胞癌的118例患者的超声影像及临床资料。从瘤内与瘤周区域的超声图像中提取放射组学特征。为筛选最优特征子集，本研究采用t检验与最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）进行特征选择。通过比较受试者工作特征曲线下面积（area under the curve, AUC）以确定最优建模方案。随后构建了四种模型：瘤内模型、瘤周模型、联合模型1及联合模型2。
结果：118例入组患者中，64例经证实为Ki-67高表达，54例为Ki-67低表达。瘤内模型的AUC为0.796（0.649~0.942），瘤周模型的AUC为0.772（0.619~0.926）。此外，联合模型1的AUC为0.870（0.751~0.989），联合模型2的AUC为0.762（0.605~0.918）。在上述四种模型中，联合模型1在AUC、准确率、F1分数及决策曲线分析（decision curve analysis, DCA）方面均表现最优。
结论：本研究构建了一种整合瘤内与瘤周组织信息的超声放射组学模型，可精准预测肝细胞癌患者的Ki-67表达水平。我们认为，以合理方式整合两类区域的影像特征，可有效提升预测模型的诊断效能。但需注意，未经审慎评估即直接将两类区域纳入感兴趣区（region of interest, ROI）的做法并不可取。