A radiomics nomogram based on contrast-enhanced MRI for preoperative prediction of macrotrabecular-massive hepatocellular carcinoma.

BACKGROUND

Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) represents an aggressive form of hepatocellular carcinoma and is associated with poor survival outcomes.

AIMS

This study aimed to develop a radiomics nomogram based on contrast-enhanced MRI for preoperative prediction of MTM-HCC.

METHODS

This study enrolled 88 patients with histologically confirmed HCC, including 32 MTM-HCCs and 56 Non-MTM-HCCs. The clinical and gadobenate dimeglumine (Gd)-enhanced MRI features were retrospectively reviewed by two abdominal radiologists. The regions of interest (ROIs) on the largest cross-sectional image and two adjacent images of the tumor, from which radiomics features were extracted via MaZda software and a radiomics score (Rad-score) was calculated via Python software. Combined with the Rad-score and independent imaging factors, a radiomics nomogram was constructed using R software. Nomogram performance was estimated with calibration curve.

RESULTS

A total of eleven top weighted radiomics features were selected among five sequences of MR images. There was a significant difference in Rad-score between MTM-HCC and non-MTM-HCC patients (P < 0.001), where patients with MTM-HCC generally had higher Rad-scores (absolute value). After multivariate analysis, radiomics score (OR = 7.794, P < 0.001) and intratumor fat (OR = 9.963, P = 0.014) were determined as independent predictors associated with MTM-HCC. The area under the receiver operating characteristic (ROC) curve of the selected model was 0.813 (95% CI 0.714-0.912) and the optimal cutoff value was 0.60. The nomogram showed overall satisfactory prediction performance (AUC = 0.785 [95% CI 0.684-0.886]).

CONCLUSIONS

A contrast-enhanced MRI-based radiomics nomogram may be useful for preoperative prediction of MTM-HCC in primary HCC patients, allowing opportunity to improve the treatment course and patient outcomes.