A Radiomics Nomogram Based On Multi-phase MRI For Preoperative Prediction Of Macrotrabecular-massive Hepatocellular Carcinoma

Purpose: Macrotrabecular-massive hepatocellular carcinoma(MTM-HCC)represents an aggressive form of hepatocellular carcinoma and is associated with poor survival outcomes.This study aimed to develop a radiomics nomogram based on multi-phase MRI for preoperative prediction of MTM-HCC.Methods: This study enrolled 88 patients with histologically confirmed HCC from June2017 to June 2020,including 32 MTM-HCCs and 56 Non-MTM-HCCs.All patients underwent preoperative gadobenate dimeglumine(Gd)-enhanced MRI examination.The clinical and 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 were manually delineated in MR images,from which radiomics features were extracted via Ma Zda software.The least absolute shrinkage and selection operator(LASSO)regression method was used for data dimensionality reduction,feature selection,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 by 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% CI0.714-0.912)and the optimal cutoff value was 0.60.The nomogram showed overall satisfactory prediction performance(AUC = 0.785 [95% CI 0.684-0.886]).Conclusion: As a non-invasive and quantitative method,our radiomics nomogram based on MRI for prediction of MTM-HCC showed overall satisfactory sensitivity and high specificity,indicating this method may be useful to clinicians in optimizing classification of MTM-HCC,allowing opportunity to improve the treatment course and patient outcomes.