Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Multi-Parametric MRI Radiomics.

Yang Zhang,Hongyang Jiang,Zhenyu Shu,Junfa Chen,Tianshi Ma,Qin Ye,Chunmiao Lin,Cuiyun Wu,Taihen Yu,Jianguo Zhong,Peipei Pang

doi:10.3389/fonc.2021.633596

Yang Zhang, Hongyang Jiang + Show 9 more

Open Access

https://doi.org/10.3389/fonc.2021.633596

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Abstract

ObjectivesTo systematically evaluate and compare the predictive capability for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients based on radiomics from multi-parametric MRI (mp-MRI) including six sequences when used individually or combined, and to establish and validate the optimal combined model.MethodsA total of 195 patients confirmed HCC were divided into training (n = 136) and validation (n = 59) datasets. All volumes of interest of tumors were respectively segmented on T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, artery phase, portal venous phase, and delay phase sequences, from which quantitative radiomics features were extracted and analyzed individually or combined. Multivariate logistic regression analyses were undertaken to construct clinical model, respective single-sequence radiomics models, fusion radiomics models based on different sequences and combined model. The accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of different models.ResultsAmong nine radiomics models, the model from all sequences performed best with AUCs 0.889 and 0.822 in the training and validation datasets, respectively. The combined model incorporating radiomics from all sequences and effective clinical features achieved satisfactory preoperative prediction of MVI with AUCs 0.901 and 0.840, respectively, and could identify the higher risk population of MVI (P < 0.001). The Delong test manifested significant differences with P < 0.001 in the training dataset and P = 0.005 in the validation dataset between the combined model and clinical model.ConclusionsThe combined model can preoperatively and noninvasively predict MVI in HCC patients and may act as a usefully clinical tool to guide subsequent individualized treatment.

Highlights

Hepatocellular carcinoma (HCC) accounts for 75%–85% of primary liver cancer, which is the sixth most common cancer and the fourth leading cause of cancer-related death globally [1, 2]
To systematically evaluate and compare the predictive capability for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients based on radiomics from multi-parametric MRI including six sequences when used individually or combined, and to establish and validate the optimal combined model
Early and accurate preoperative prediction of the Microvascular invasion (MVI) is of vital importance for clinical decision-making in choosing the best strategy to manage the individual HCC patients [9]

Summary

Introduction

Hepatocellular carcinoma (HCC) accounts for 75%–85% of primary liver cancer, which is the sixth most common cancer and the fourth leading cause of cancer-related death globally [1, 2]. Hepatectomy and liver transplantation are potential effective treatments for HCC. The prognosis is still poor with high tumor recurrence rate of 70% after hepatectomy and 25% after transplantation [3, 4]. Microvascular invasion (MVI) is regarded as an extremely important independent risk factor of postoperative recurrence and poor outcome [5, 6]. Study found that patients with higher MVI risk benefited from anatomical hepatectomy and widened surgical margin in terms of diseasefree survival and overall survival [7]. The diagnosis of MVI still depends on the postoperative histopathology in current clinical practice [8]. Early and accurate preoperative prediction of the MVI is of vital importance for clinical decision-making in choosing the best strategy to manage the individual HCC patients [9]

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