Data_Sheet_1_Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma: Radiomics Algorithm Based on Ultrasound Original Radio Frequency Signals.docx

Background: To evaluate the accuracy of radiomics algorithm based on original radio frequency (ORF) signals for prospective prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) lesions.Methods: In this prospective study, we enrolled 42 inpatients diagnosed with HCC from January 2018 to December 2018. All HCC lesions were proved by surgical resection and histopathology results, including 21 lesions with MVI. Ultrasound ORF data and grayscale ultrasound images of HCC lesions were collected before operation for further radiomics analysis. Three ultrasound feature maps were calculated using signal analysis and processing (SAP) technology in first feature extraction. The diagnostic accuracy of model based on ORF signals was compared with the model based on grayscale ultrasound images.Results: A total of 1,050 radiomics features were extracted from ORF signals of each HCC lesion. The performance of MVI prediction model based on ORF was better than those based on grayscale ultrasound images. The best area under curve, accuracy, sensitivity, and specificity of ultrasound radiomics in prediction of MVI were 95.01, 92.86, 85.71, and 100%, respectively.Conclusions: Radiomics algorithm based on ultrasound ORF data combined with SAP technology can effectively predict MVI, which has potential clinical application value for non-invasively preoperative prediction of MVI in HCC patients.

背景：本研究旨在评估基于原始射频信号（ORF）的放射组学算法在预测肝细胞癌（HCC）病灶微血管侵犯（MVI）方面的准确性。方法：在本前瞻性研究中，我们于2018年1月至2018年12月间招募了42名被诊断为HCC的住院患者。所有HCC病灶均经手术切除和病理学检查证实，其中21个病灶存在MVI。在手术前收集了HCC病灶的超声ORF数据和灰度超声图像，以进行后续的放射组学分析。通过信号分析及处理（SAP）技术在首次特征提取中计算了三个超声特征图。基于ORF信号的模型诊断准确性与基于灰度超声图像的模型进行了比较。结果：从每个HCC病灶的ORF信号中提取了1,050个放射组学特征。基于ORF的MVI预测模型性能优于基于灰度超声图像的模型。超声放射组学在预测MVI方面的最佳曲线下面积、准确率、敏感性和特异性分别为95.01%、92.86%、85.71%和100%。结论：基于超声ORF数据并结合SAP技术的放射组学算法可有效预测MVI，对HCC患者术前非侵入性预测MVI具有潜在的临床应用价值。