AUTHOR=Yao Wenjun , Yang Shuo , Ge Yaqiong , Fan Wenlong , Xiang Li , Wan Yang , Gu Kangchen , Zhao Yan , Zha Rujing , Bu Junjie 

TITLE=Computed Tomography Radiomics-Based Prediction of Microvascular Invasion in Hepatocellular Carcinoma

JOURNAL=Frontiers in Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2022.819670

DOI=10.3389/fmed.2022.819670

ISSN=2296-858X

ABSTRACT=Background: Due to the high recurrence rate in hepatocellular carcinoma (HCC) after resection, preoperative prognostic prediction of HCC is important for appropriate patient management. Exploring and developing preoperative diagnostic methods has great clinical value in treating patients with HCC. This study sought to develop and evaluate a novel combined clinical predictive model based on standard triphasic computed tomography (CT) to discriminate microvascular invasion (MVI) in hepatocellular carcinoma (HCC).
Methods: The preoperative findings of 82 patients with HCC, including conventional clinical factors, CT imaging findings, and CT texture analysis (TA), were analysed retrospectively. All included cases were divided into MVI-negative (n=33; no MVI) and MVI-positive (n=49; low or high risk of MVI) groups. TA parameters were extracted from non-enhanced, arterial, portal venous, and equilibrium phase images and subsequently calculated using the Artificial Intelligence Kit. After statistical analyses, a clinical model comprising conventional clinical CT image risk factors, radiomics signature models, and a novel combined model (fused radiomic signature) was constructed. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was used to assess the performance of the various models in discriminating MVI. 
Results: We found that tumour diameter and pathological grade were effective clinical predictors in the combined model and that 12 features were effective for MVI prediction in HCC radiomics signatures. The AUCs of the clinical, plain, arterial, venous, and delay models were 0.77 (95% CI: 0.67–0.88), 0.75 (95% CI: 0.64–0.87), 0.79 (95% CI: 0.69–0.89), 0.73 (95% CI: 061–0.85), and 0.80 (95% CI: 0.70–0.91), respectively. The novel combined model exhibited the best performance, with an AUC of 0.83 (95% CI: 0.74–0.93).