DataSheet_1_Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study.docx

ObjectiveThis study aims to explore the clinical value of machine learning-based ultrasomics in the preoperative noninvasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC).
MethodsThe clinical data and ultrasonic images of 226 patients from three hospitals were retrospectively collected and divided into training set (n = 149), test set (n = 38), and independent validation set (n = 39). Manual segmentation of tumor lesion was performed with ITK-SNAP, the ultrasomics features were extracted by the pyradiomics, and ultrasomics signatures were generated using variance filtering and lasso regression. The prediction models for preoperative differentiation between HCC and ICC were established by using support vector machine (SVM). The performance of the three models was evaluated by the area under curve (AUC), sensitivity, specificity, and accuracy.
ResultsThe ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between HCC and ICC (p < 0.05). The combined model had a better performance than either the clinical model or the ultrasomics model. In addition to stability, the combined model also had a stronger generalization ability (p < 0.05). The AUC (along with 95% CI), sensitivity, specificity, and accuracy of the combined model on the test set and the independent validation set were 0.936 (0.806–0.989), 0.900, 0.857, 0.868, and 0.874 (0.733–0.961), 0.889, 0.867, and 0.872, respectively.
ConclusionThe ultrasomics signatures could facilitate the preoperative noninvasive differentiation between HCC and ICC. The combined model integrating ultrasomics signatures and clinical features had a higher clinical value and a stronger generalization ability.

研究目的：本研究旨在探讨基于机器学习的超声组学（ultrasomics）在肝细胞癌（hepatocellular carcinoma，HCC）与肝内胆管癌（intrahepatic cholangiocarcinoma，ICC）术前无创鉴别中的临床应用价值。研究方法：本研究回顾性收集了三家医院共226例患者的临床资料与超声图像，将其划分为训练集（n=149）、测试集（n=38）与独立验证集（n=39）。采用ITK-SNAP软件手动分割肿瘤病灶，通过pyradiomics工具提取超声组学特征，并结合方差过滤与Lasso回归生成超声组学特征集。采用支持向量机（support vector machine，SVM）构建HCC与ICC的术前鉴别预测模型。以曲线下面积（area under curve，AUC）、灵敏度、特异度及准确率评估三款模型的性能表现。研究结果：从灰阶超声图像中提取的超声组学特征集可有效鉴别HCC与ICC（p<0.05）。联合模型的性能优于单一临床模型或单一超声组学模型。该联合模型不仅具备良好稳定性，还拥有更强的泛化能力（p<0.05）。联合模型在测试集与独立验证集中的曲线下面积（及95%置信区间）、灵敏度、特异度与准确率分别为0.936（0.806~0.989）、0.900、0.857、0.868，以及0.874（0.733~0.961）、0.889、0.867、0.872。研究结论：超声组学特征集可助力HCC与ICC的术前无创鉴别。整合超声组学特征与临床特征的联合模型具备更高的临床应用价值与更强的泛化能力。