Abstract

Objectives: To establish a radiomic algorithm based on grayscale ultrasound images and to make preoperative predictions of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients.Methods: In this retrospective study, 322 cases of histopathologically confirmed HCC lesions were included. The classifications based on preoperative grayscale ultrasound images were performed in two stages: (1) classifier #1, MVI-negative and MVI-positive cases; (2) classifier #2, MVI-positive cases were further classified as M1 or M2 cases. The gross-tumoral region (GTR) and peri-tumoral region (PTR) signatures were combined to generate gross- and peri-tumoral region (GPTR) radiomic signatures. The optimal radiomic signatures were further incorporated with vital clinical information. Multivariable logistic regression was used to build radiomic models.Results: Finally, 1,595 radiomic features were extracted from each HCC lesion. At the classifier #1 stage, the radiomic signatures based on features of GTR, PTR, and GPTR showed area under the curve (AUC) values of 0.708 (95% CI, 0.603–0.812), 0.710 (95% CI, 0.609–0.811), and 0.726 (95% CI, 0.625–0.827), respectively. Upon incorporation of vital clinical information, the AUC of the GPTR radiomic algorithm was 0.744 (95% CI, 0.646–0.841). At the classifier #2 stage, the AUC of the GTR radiomic signature was 0.806 (95% CI, 0.667–0.944).Conclusions: Our radiomic algorithm based on grayscale ultrasound images has potential value to facilitate preoperative prediction of MVI in HCC patients. The GTR radiomic signature may be helpful for further discriminating between M1 and M2 levels among MVI-positive patients.

Highlights

  • Hepatocellular carcinoma (HCC) is one of the most common type of liver malignancies all over the world and exhibits aggressive malignant behavior and a high mortality rate [1, 2]

  • At the classifier #1 stage, the radiomic signatures based on features of gross-tumoral region (GTR), peri-tumoral region (PTR), and gross- and peri-tumoral region (GPTR) showed area under the curve (AUC) values of 0.708, 0.710, and 0.726, respectively

  • Upon incorporation of vital clinical information, the AUC of the GPTR radiomic algorithm was 0.744

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Summary

Introduction

Hepatocellular carcinoma (HCC) is one of the most common type of liver malignancies all over the world and exhibits aggressive malignant behavior and a high mortality rate [1, 2]. For HCC patients, hepatic surgery is the primary treatment, but 5-years recurrence rates after hepatic surgery could be as high as 50% [1, 2], which varies from 20 to 44% [3]. Microvascular invasion (MVI) has been proved to be an independent predictor of poor outcomes subsequent to surgical hepatic resection [4,5,6]. To make noninvasive and accurate identification of MVI preoperatively would be of great benefit for stratifying HCC patients before surgery [4, 7, 8]

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