Preoperative Evaluation of Gd‐EOB‐DTPA‐Enhanced MRI Radiomics‐Based Nomogram in Small Solitary Hepatocellular Carcinoma (≤3 cm) With Microvascular Invasion: A Two‐Center Study

Abstract

Background Preoperative evaluation of microvascular invasion (MVI) in small solitary hepatocellular carcinoma (HCC; maximum lesion diameter ≤ 3 cm) is important for treatment decisions. Purpose To apply gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd‐EOB‐DTPA)‐enhanced MRI to develop and validate a nomogram for preoperative evaluation of MVI in small solitary HCC and to compare the effectiveness of radiomics evaluation models based on different volumes of interest (VOIs). Study Type Retrospective. Population A total of 196 patients include 62 MVI‐positive and 134 MVI‐negative patients were enrolled (training cohort, n = 105; testing cohort, n = 45; external validation cohort, n = 46). Field Strength/Sequence 3.0 T, fat suppressed fast‐spin‐echo T2‐weighted and Gd‐EOB‐DTPA‐enhanced T1‐weighted magnetization‐prepared rapid gradient‐echo sequences. Assessment Radiomics features were extracted on T2‐weighted, arterial phase (AP), and hepatobiliary phase (HBP) images from different VOIs (VOIintratumor and VOIintratumor+peritumor) and filtered by the least absolute shrinkage selection operator (LASSO) regression. From VOIintratumor and VOIintratumor+peritumor, eight radiomics models were constructed based on three MRI sequences (T2‐weighted, AP, and HBP) and fused sequences (combined of three sequences). Nomograms were constructed of a clinical‐radiological (CR) model and a clinical‐radiological‐radiomics (CRR) model. Statistical Tests One‐way analysis of variance, independent t‐test, Chi‐square test or Fisher's exact test, Wilcoxon rank‐sum test, LASSO, logistic regression analysis, area under the curve (AUC), nomograms, decision curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI) analyses, and DeLong test. Results Among eight radiomics models, the fused sequences‐based VOIintratumor+peritumor radiomics model showed the best performance. The CRR model containing the best performance radiomics model and CR model with the AUC values were 0.934, 0.889, and 0.875, respectively. NRI and IDI analyses showed that the CRR model improved evaluation efficacy over the CR model for all three cohorts (all P‐value <0.05). Data Conclusion The CRR model nomogram could preoperatively evaluate MVI in small solitary HCC. The radiomics model based on VOIintratumor+peritumor might achieve better evaluation results. Evidence Level 4 Technical Efficacy Stage 2

Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

“…In recent years, due to the progress and rapid developments of various hardware and software technologies, radiomics has gradually developed into a relatively mature discipline or medical image analysis method (1). There are more and more publications on the application of radiomics for the diagnosis, differential diagnosis, treatment options, and prognosis evaluation of many human diseases (11)(12)(13)(14)(15)(16). Among them, Hong et al (13) extracted 10 radiomics features from the contrast-enhanced CT (CECT) images of 241 patients with a bone island or osteoblastic metastasis to establish a random forest (RF) prediction model.…”

“…The results showed that the RF model based on CT was helpful to differentiate bone islands from osteoblastic metastases, and its diagnostic performance was higher than that of inexperienced radiologists but equivalent to that of experienced radiologists. In another study, Tian et al ( 16 ) reported the diagnostic value of preoperative evaluation of microvascular invasion of solitary small hepatocellular carcinoma (HCC) based on nomogram of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI. The results indicated that the clinical-radiological-radiomics model achieved the highest diagnostic performance with area under the receiver operating characteristic curves (AUCs) of 0.934, 0.889 and 0.875 for the training, internal and external validation sets, respectively.…”

Intratumoral and Peritumoral Radiomics Based on Contrast-enhanced MRI for Preoperatively Predicting Treatment Response of Transarterial Chemoembolization in Hepatocellular Carcinoma

“…This approach would capture the three-dimensional (3D) information of the entire tumor and has been proved effective in characterizing the heterogeneity of the tumor by acting as a whole tumor virtual biopsy [17,18] . Numerous studies have demonstrated that radiomics-based models effectively identify the diagnosis and pathological characteristics or predict therapeutic e cacy and prognosis of cancer patients for clinical decisionmaking [19][20][21][22] . Recently, there has been increasing interest in evaluating radiomics patterns of the region surrounding the visible tumor [20,23,24] .…”

“…Numerous studies have demonstrated that radiomics-based models effectively identify the diagnosis and pathological characteristics or predict therapeutic e cacy and prognosis of cancer patients for clinical decisionmaking [19][20][21][22] . Recently, there has been increasing interest in evaluating radiomics patterns of the region surrounding the visible tumor [20,23,24] . Recurrence or metastasis of HCC is mainly intrahepatic, indicating that the peritumoral liver tissue may be a favorable soil for the spreading hepatoma cells [25] .…”

Editorial for “Preoperative Evaluation of Gd‐EOB‐DTPA–Enhanced MRI Radiomics‐Based Nomogram in Small Solitary Hepatocellular Carcinoma (≤3 cm) With Microvascular Invasion: A Two‐Center Study”

“…In this issue of JMRI , an article by Tian et al 5 repot on a preoperative prediction performance of Gd‐EOB‐DTPA–enhanced MRI radiomics‐based nomogram for MVI in the patients with small solitary HCC. Radiomics is a method that extracts quantitative and spatial features of images as a number of parameters and clarifying their relationship to factors that may affect image findings, such as MVI 6 .…”