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

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Zongren Ding ◽  
Kongying Lin ◽  
Jun Fu ◽  
Qizhen Huang ◽  
Guoxu Fang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Focal Nodular Hyperplasia ◽  
Clinical Decision Making ◽  
Cirrhotic Liver ◽  
Arterial Phase ◽  
Combined Model ◽  
Clinical Model ◽  
Portal Venous Phase ◽  
Contrast Enhanced ◽  
Nodular Hyperplasia

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.

An MR-Based Radiomics Model for Differentiation Between Hepatocellular Carcinoma and Focal Nodular Hyperplasia in Non-Cirrhotic Liver

2021 ◽  
Author(s):  
Zongren Ding ◽  
Kongying Lin ◽  
Jun Fu ◽  
Qizhen Huang ◽  
Guoxu Fang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Focal Nodular Hyperplasia ◽  
Cirrhotic Liver ◽  
Arterial Phase ◽  
Training Set ◽  
Combined Model ◽  
Clinical Model ◽  
Contrast Enhanced ◽  
Nodular Hyperplasia ◽  
Validation Set

Abstract Purpose:This study aimed to develop and validate a radiomics model for differentiating between hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) in non-cirrhotic livers using Gd-DTPA contrast-enhanced magnetic resonance imaging (MRI).Methods:We retrospectively enrolled 149 HCC patients and 75 FNH patients seen between May 2015 and May 2019 at our center and randomly allocated patients to a training set (n = 156) and a validation set (n = 68). A total of 2,260 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 forests, and the 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 three models was compared. Results:Eight radiomics features were chosen to build a radiomics model, and four clinical factors (age, sex, HbsAg, and enhancement pattern) were chosen to build the clinical model. When evaluating the performance of three models, the clinical model that included clinical data and visual MRI findings achieved excellent performance in the training set (AUC, 0.937; 95% CI, 0.887–0.970) and the validation set (AUC, 0.903; 95% CI, 0.807–0.962), and there was no significant difference between the radiomics model and the clinical model. The AUC 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 based on clinical and radiomics features can well distinguish HCC from FNH in non-cirrhotic liver. Our model may assist clinicians in the clinical decision-making process.

Hepatocellular carcinoma in association with hepatic focal nodular hyperplasia

2006 ◽  
Vol 47 (4) ◽  
pp. 340-344 ◽  
Author(s):  
J. M. Langrehr ◽  
R. Pfitzmann ◽  
M. Hermann ◽  
C. Radke ◽  
P. Neuhaus ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Focal Nodular Hyperplasia ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Surgical Removal ◽  
Iron Oxide Particles ◽  
Right Hemihepatectomy ◽  
Signal Characteristics ◽  
Nodular Hyperplasia ◽  
Magnetic Resonance Imaging Mri

Purpose: To report the association between hepatocellular carcinoma (HCC) and hepatic focal nodular hyperplasia (FNH) and the possible impact on clinical decision-making with regard to resective approaches in patients with FNH. Material and Methods: We retrospectively analyzed the findings in 77 adult patients who underwent liver resections for FNH between October 1989 and September 2001 at our center. HCC within the confines of FNH was found in two patients. We demonstrate the magnetic resonance imaging (MRI) and macroscopic and microscopic findings. Results: Presurgical MRI demonstrated heterogeneous signal characteristics of moderately hyperintense FNH on T2-weighted images and, after IV administration of super-paramagnetic iron oxide particles, HCC in FNH was barely delineable. Both patients underwent successful right hemihepatectomy to remove the suspicious FNH with diameters of 12 and 14 cm; intralesional HCC diameters were 3 and 5 cm, respectively. Patients could be rapidly dismissed. However, one patient died after recurrence of HCC 1.5 years after surgery, whereas the other patient continues tumor-free 4 years after surgery. Alpha-feto-protein was normal in both patients. Conclusion: In FNH with rapid growth tendency and heterogenic MR appearance, surgical removal should be considered to overcome the risk of inadequate therapy in the very rare group of patients with HCC in association with FNH.

scholarly journals Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound

2021 ◽  
Vol 11 ◽  
Author(s):  
Di Zhang ◽  
Qi Wei ◽  
Ge-Ge Wu ◽  
Xian-Ya Zhang ◽  
Wen-Wu Lu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Size ◽  
Multivariate Logistic Regression Analysis ◽  
Microvascular Invasion ◽  
Validation Dataset ◽  
Contrast Enhanced Ultrasound ◽  
Clinical Model ◽  
Portal Venous Phase ◽  
Preoperative Prediction ◽  
Contrast Enhanced

PurposeThis study aimed to develop a radiomics nomogram based on contrast-enhanced ultrasound (CEUS) for preoperatively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients.MethodsA retrospective dataset of 313 HCC patients who underwent CEUS between September 20, 2016 and March 20, 2020 was enrolled in our study. The study population was randomly grouped as a primary dataset of 192 patients and a validation dataset of 121 patients. Radiomics features were extracted from the B-mode (BM), artery phase (AP), portal venous phase (PVP), and delay phase (DP) images of preoperatively acquired CEUS of each patient. After feature selection, the BM, AP, PVP, and DP radiomics scores (Rad-score) were constructed from the primary dataset. The four radiomics scores and clinical factors were used for multivariate logistic regression analysis, and a radiomics nomogram was then developed. We also built a preoperative clinical prediction model for comparison. The performance of the radiomics nomogram was evaluated via calibration, discrimination, and clinical usefulness.ResultsMultivariate analysis indicated that the PVP and DP Rad-score, tumor size, and AFP (alpha-fetoprotein) level were independent risk predictors associated with MVI. The radiomics nomogram incorporating these four predictors revealed a superior discrimination to the clinical model (based on tumor size and AFP level) in the primary dataset (AUC: 0.849 vs. 0.690; p < 0.001) and validation dataset (AUC: 0.788 vs. 0.661; p = 0.008), with a good calibration. Decision curve analysis also confirmed that the radiomics nomogram was clinically useful. Furthermore, the significant improvement of net reclassification index (NRI) and integrated discriminatory improvement (IDI) implied that the PVP and DP radiomics signatures may be very useful biomarkers for MVI prediction in HCC.ConclusionThe CEUS-based radiomics nomogram showed a favorable predictive value for the preoperative identification of MVI in HCC patients and could guide a more appropriate surgical planning.

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

2021 ◽  
Vol 11 ◽  
Author(s):  
Yang Zhang ◽  
Zhenyu Shu ◽  
Qin Ye ◽  
Junfa Chen ◽  
Jianguo Zhong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Characteristic Curve ◽  
Microvascular Invasion ◽  
Training Dataset ◽  
Validation Dataset ◽  
Clinical Tool ◽  
Combined Model ◽  
Clinical Model ◽  
Portal Venous Phase ◽  
Preoperative Prediction

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.

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