Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma: Radiomics Algorithm Based on Ultrasound Original Radio Frequency Signals
| Autor: | Le-Yin Li, Wenping Wang, Qing-Min Wang, Jinhua Yu, Zhao Yao, Qi Zhang, Meng Dai, Qian Li, Yi Dong |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Surgical resection Cancer Research microvascular invasion Diagnostic accuracy lcsh:RC254-282 Grayscale 03 medical and health sciences 0302 clinical medicine Radiomics Area under curve Medicine radiomics analysis Original Research business.industry Ultrasound hepatocellular carcinoma prediction original radio frequency signals lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens medicine.disease 030104 developmental biology Oncology Feature (computer vision) 030220 oncology & carcinogenesis Hepatocellular carcinoma business Algorithm |
| Zdroj: | Frontiers in Oncology Frontiers in Oncology, Vol 9 (2019) |
| ISSN: | 2234-943X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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