Published online Jun 28, 2022. doi: 10.3748/wjg.v28.i24.2733
Peer-review started: December 24, 2021
First decision: March 10, 2022
Revised: March 20, 2022
Accepted: May 12, 2022
Article in press: May 12, 2022
Published online: June 28, 2022
The prognosis of hepatocellular carcinoma (HCC) remains poor and relapse occurs in more than half of the patients within 2 years after hepatectomy. Microvascular invasion (MVI) is one of the potential predictors of recurrence. MVI is defined as the appearance of tumor cells in smaller vessels inside the liver which include small portal vein, and small lymphatic vessels or hepatic arteries. Accurate preoperative prediction of MVI is potentially beneficial to the optimization of treatment planning.
There have been some studies to preoperatively predict MVI in terms of serum markers, radiological features, or imaging techniques. However, the levels of serum markers are instable and likely to be affected by other diseases, and the imaging characteristics are evaluated subjectively and lack of conformance between observers. Thus, a more reliable biomarker is needed for preoperative prediction of MVI.
The aim of this study was to develop a radiomic analysis model based on pre-operative magnetic resonance imaging (MRI) data to predict MVI in HCC.
A total of 113 patients recruited to this study have been diagnosed as having HCC with histological confirmation, among whom, 73 were found to have MVI and 40 were not. All the patients received preoperative examination by Gd-enhanced MRI and then curative hepatectomy. We manually delineated the tumor lesion on the largest cross-sectional area of the tumor and the two adjacent images on MRI. Quantitative analyses included most discriminant factors (MDFs) developed using a linear discriminant analysis algorithm and histogram analysis via MaZda software. Independent significant variables of clinical and radiological features and MDFs for the prediction of MVI were estimated and a discriminant model was established by univariate and multivariate logistic regression analysis. Prediction ability of the above-mentioned parameters or model was then evaluated by receiver operating characteristic (ROC) curve analysis, and five-fold cross-validation was applied via R software.
The area under the ROC curve of the MDF (0.77-0.85) outperformed the histogram parameters (0.51-0.74). After multivariate analysis, MDF values of the arterial and portal venous phase, and peritumoral hypointensity in the hepatobiliary phase were identified to be independent predictors of MVI (P < 0.05). The area under the ROC curve (AUC) value of the model was 0.939. The result of internal five-fold cross-validation (AUC: 0.912) also showed favorable predictive efficacy.
Noninvasive MRI radiomic model based on MDF values and imaging biomarkers may be useful to make preoperative prediction of MVI in patients with primary HCC.
We believe that noninvasive radiomic models based on pre-operative MRI data have potential to be widely used in clinical fields.