Radiomics for diagnosis of dual-phenotype hepatocellular carcinoma using Gd-EOB-DTPA-enhanced MRI and patient prognosis

Abstract

Purpose To describe the clinical characteristics and outcomes of patients with dual-phenotype hepatocellular carcinoma (DPHCC) and investigate the use of radiomics to establish an image-based signature for preoperative differential diagnosis. Methods This study included 50 patients with a postoperative pathological diagnosis of DPHCC (observation group) and 50 patients with CK7-and CK19-negative HCC (control group) who attended our hospital between January 2015 and December 2018. All patients underwent Gd-EOB-DTPA-enhanced MRI within 1 month before surgery. Arterial phase (AP), portal venous phase (PVP), delayed phase (DP) and hepatobiliary phase (HBP) images were transferred into a radiomics platform. Volumes of interest covered the whole tumor. The dimensionality of the radiomics features were reduced using LASSO. Four classifiers, including multi-layer perceptron (MLP), support vector machines (SVM), logistic regression (LR) and K-nearest neighbor (KNN) were used to distinguish DPHCC from CK7-and CK19-negative HCC. Kaplan-Meier survival analysis was used to assess 1-year disease-free survival (DFS) and overall survival (OS) in the observation and control groups. Results The best preoperative diagnostic power for DPHCC will likely be derived from a combination of different phases and classifiers. The sensitivity, specificity and accuracy of LR in PVP (0.740, 0.780, 0.766), DP (0.893, 0.700, 0.798), HBP (0.800, 0.720, 0.756) and MLP in PVP (0.880, 0.720, 0.798) were better performance. The 1-year DFS and OS of the patients in the observation group were 69% and 78%, respectively. The 1-year DFS and OS of the patients in the control group were 83% and 85%, respectively. Kaplan-Meier survival analysis showed no statistical difference in DFS and OS between groups (P = 0.231 and 0.326), but DFS and OS were numerically lower in patients with DPHCC. Conclusion The radiomics features extracted from Gd-EOB-DTPA-enhanced MR images can be used to diagnose preoperative DPHCC. DPHCC is more likely to recur and cause death than HCC, suggesting that active postoperative management of patients with DPHCC is required.

Preoperative Prediction Power of Imaging Methods for Microvascular Invasion in Hepatocellular Carcinoma: A Systemic Review and Meta-Analysis

“…With the popularization of radiomics, different algorithms have been applied into modeling based on radiomics features for tumor phenotype prediction (53)(54)(55)(56). A recent quantitative review of radiomics on HCC suggests that radiomics is a very promising non-invasive method for individualized evaluation based on intra-tumor heterogeneity analysis (57)(58)(59).…”

Radiomic Analysis of Gd-EOB-DTPA-enhanced MRI Predicts Ki-67 Expression in Hepatocellular Carcinoma

“…Accordingly, the AP radiomics model added more net bene t in predicting Ki-67 expression of HCC compared with HBP radiomics model. Although a previous study has reported the AP model of Gd-EOB-DTPA-enhanced MRI was inferior, possibly due to artifacts affecting extraction and calculation of textural-based features [21], our study excluded those patients with obvious artifacts caused by transient severe motion (TSM) [22,23].…”

Radiomics in liver diseases: Current progress and future opportunities

“…Data used in radiomics studies can be single‐centre or multi‐centre, and retrospective or prospective. Here, we searched PubMed (8 October 2019) for radiomics studies on liver diseases using terms (liver diseases AND radiomics), and found 36 clinical target‐oriented published work 13‐48 . Most (33 out of 36) studies were performed on single‐centre with retrospective cohort, while only two studies were performed on multi‐centre and prospective cohort (Table 1).…”

“…In addition to imaging data, clinical factors were also involved in radiomics analysis, including patient age, gender, Child‐Pugh stage, histologic grading, BCLC stage, cirrhosis and its cause, etc 13‐48 . Laboratory examination indexes comprise serum α‐fetoprotein (AFP) level, prothrombin induced by vitamin K absence‐II (PIVKA‐II) level, carbohydrate antigen 19‐9 (CA 19‐9) level, hepatitis B virus surface antigen (HBsAg), serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum total bilirubin (TB), conjugated bilirubin (CB), serum albumin (ALB), prothrombin time (PT), platelet count (PLT), etc 13‐48 …”

“…This groundbreaking result laid a foundation and greatly encouraged researchers to explore the potential of quantitative imaging tool in preoperative genetic/pathological outcome prediction. Hence, a great deal of radiomics studies have been conducted using multi‐parametric and multi‐modality imaging in terms of liver disease diagnosis and treatment decision making 13‐48 . In certain scenarios, this artificial intelligence‐based technique could even compete pathological gold standard, providing new ways for unsolved clinical problems in the paradigm of liver disease management 16 .…”