An MR-based radiomics model for differentiation between hepatocellular carcinoma and focal nodular hyperplasia in non-cirrhotic liver

Abstract

Purpose We aimed to develop and validate a radiomics model for differentiating hepatocellular carcinoma (HCC) from focal nodular hyperplasia (FNH) in non-cirrhotic livers using Gd-DTPA contrast-enhanced magnetic resonance imaging (MRI). Methods We retrospectively enrolled 149 HCC and 75 FNH patients treated between May 2015 and May 2019 at our center. Patients were randomly allocated to a training (n=156) and validation set (n=68). In total, 2260 radiomics features were extracted from the arterial phase and portal venous phase of Gd-DTPA contrast-enhanced MRI. Using Max-Relevance and Min-Redundancy, random forest, least absolute shrinkage, and selection operator algorithm for dimensionality reduction, multivariable logistic regression was used to build the radiomics model. A clinical model and combined model were also established. The diagnostic performance of the models was compared. Results Eight radiomics features were chosen for the radiomics model, and four clinical factors (age, sex, HbsAg, and enhancement pattern) were chosen for the clinical model. A combined model was built using the factors from the previous models. The classification accuracy of the combined model differentiated HCC from FNH in both the training and validation sets (0.956 and 0.941, respectively). The area under the receiver operating characteristic curve of the combined model was significantly better than that of the clinical model for both the training (0.984 vs. 0.937, p=0.002) and validation (0.972 vs. 0.903, p=0.032) sets. Conclusions The combined model provided a non-invasive quantitative method for differentiating HCC from FNH in non-cirrhotic liver with high accuracy. Our model may assist clinicians in the clinical decision-making process.

The Value of Contrast‐Enhanced Ultrasound Liver Imaging Reporting and Data System in the Diagnosis of Hepatocellular Carcinoma: A Meta‐Analysis

“…Among all methods used in the early diagnosis of HCC, radiological examinations, including computerized tomography (CT), magnetic resonance imaging (MRI), and contrast‐enhanced ultrasound (CEUS), are widely used for its noninvasiveness. Based on these examinations, radiomics become a useful tool in the diagnosis and prognosis of liver tumors 4–6 . Compared with CT or MRI, the CEUS has some unique advantages.…”

“…Based on these examinations, radiomics become a useful tool in the diagnosis and prognosis of liver tumors. [4][5][6] Compared with CT or MRI, the CEUS has some unique advantages. It can permit real-time imaging and show some rapidly changing features that cannot be detected by CT or MRI.…”

A T2 Weighted Imaging-based Radiomics Nomogram for the Classification of Hepatic Blood-rich Lesions: Hepatocellular Carcinoma and Benign Liver Lesions

“…Formerly, many studies have supplied several methods to discriminate against HCC from various BLLs, such as fp-AMLs, FNH, HH and so on [23,24]. It is obvious that most of them analyzed HCC and another benign lesion with rich blood supply by CE-CT/MRI.…”

Precision diagnosis of Hepatocellular Carcinoma

“…Finally, the model is internally or externally validated. [76] Radiomics analysis is conducive to the early diagnosis, differential diagnosis, and evaluation of MVI in HCC [77][78][79][80][81][82][83] [Table 3]. Dankerl et al [84] reported that radiomics methods can outperform imaging doctors in predicting the nature of the lesion (benign and malignant), with an accuracy of 75.1%, whereas the range of imaging doctors is between 52% and 74%, depending on their level of experience.…”