Published online Aug 21, 2022. doi: 10.3748/wjg.v28.i31.4399
Peer-review started: December 14, 2021
First decision: January 27, 2022
Revised: February 5, 2022
Accepted: July 24, 2022
Article in press: July 24, 2022
Published online: August 21, 2022
Microvascular invasion (MVI) of small hepatocellular carcinoma (sHCC) (≤ 3.0 cm) is an independent prognostic factor for poor progression-free and overall survival. Radiomics can help extract imaging information associated with tumor pathophysiology.
To develop and validate radiomics scores and a nomogram of gadolinium ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in sHCC.
In total, 415 patients were diagnosed with sHCC by postoperative pathology. A total of 221 patients were retrospectively included from our hospital. In addition, we recruited 94 and 100 participants as independent external validation sets from two other hospitals. Radiomics models of Gd-EOB-DTPA-enhanced MRI and diffusion-weighted imaging (DWI) were constructed and validated using machine learning. As presented in the radiomics nomogram, a prediction model was developed using multivariable logistic regression analysis, which included radiomics scores, radiologic features, and clinical features, such as the alpha-fetoprotein (AFP) level. The calibration, decision-making curve, and clinical usefulness of the radiomics nomogram were analyzed. The radiomic nomogram was validated using independent external cohort data. The areas under the receiver operating curve (AUC) were used to assess the predictive capability.
Pathological examination confirmed MVI in 64 (28.9%), 22 (23.4%), and 16 (16.0%) of the 221, 94, and 100 patients, respectively. AFP, tumor size, non-smooth tumor margin, incomplete capsule, and peritumoral hypointensity in hepatobiliary phase (HBP) images had poor diagnostic value for MVI of sHCC. Quantitative radiomic features (1409) of MRI scans) were extracted. The classifier of logistic regression (LR) was the best machine learning method, and the radiomics scores of HBP and DWI had great diagnostic efficiency for the prediction of MVI in both the testing set (hospital A) and validation set (hospital B, C). The AUC of HBP was 0.979, 0.970, and 0.803, respectively, and the AUC of DWI was 0.971, 0.816, and 0.801 (P < 0.05), respectively. Good calibration and discrimination of the radiomics and clinical combined nomogram model were exhibited in the testing and two external validation cohorts (C-index of HBP and DWI were 0.971, 0.912, 0.808, and 0.970, 0.843, 0.869, respectively). The clinical usefulness of the nomogram was further confirmed using decision curve analysis.
AFP and conventional Gd-EOB-DTPA-enhanced MRI features have poor diagnostic accuracies for MVI in patients with sHCC. Machine learning with an LR classifier yielded the best radiomics score for HBP and DWI. The radiomics nomogram developed as a noninvasive preoperative prediction method showed favorable predictive accuracy for evaluating MVI in sHCC.
Core Tip: Microvascular invasion (MVI) accounts for approximately 20% of small hepatocellular carcinoma (sHCC) (≤ 3.0 cm) and is a poor independent prognostic factor for progression-free survival and overall survival. However, no studies have been published on the preoperative prediction of the MVI of sHCC. This multi-center study was developed and validated radiomics scores and nomogram of gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) for the preoperative prediction of MVI in sHCC. The results demonstrated that AFP and conventional EOB-MRI features have poor diagnostic accuracy for MVI in patients with sHCC. The radiomics scores of HBP and diffusion-weighted imaging can improve the ability to predict MVI. As a noninvasive preoperative prediction method, the radiomics nomogram presented in this study showed a favorable predictive accuracy in evaluating MVI of sHCC, which may help reassess the clinical therapeutic regimen for patients with sHCC.