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.

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.

scholarly journals Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study

2020 ◽  
Author(s):  
Bo Liu ◽  
Hexiang Wang ◽  
Shunli Liu ◽  
Shifeng Yang ◽  
Yancheng Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastrointestinal Stromal Tumors ◽  
Ct Images ◽  
Arterial Phase ◽  
Training Set ◽  
Clinical Model ◽  
Stromal Tumors ◽  
Contrast Enhanced ◽  
Validation Set ◽  
Exon 11

Abstract Background Knowing the genetic phenotype of gastrointestinal stromal tumors (GISTs) is essential for patients who receive therapy with tyrosine kinase inhibitors.Methods We enrolled 106 patients (80 in the training set, 26 in the validation set) with clinicopathologically confirmed GISTs from two centers. Preoperative and postoperative clinical characteristics were selected and analyzed to construct the clinical model. Arterial phase (A-phase), venous phase (V-phase), delayed phase (D-phase), and combined radiomics algorithms were generated from the training set based on contrast-enhanced computed tomography (CE-CT) images. Various radiomics feature selection methods were used, namely least absolute shrinkage and selection operator (LASSO); minimum redundancy maximum relevance (mRMR); and generalized linear model (GLM) as a machine-learning classifier. Independent predictive factors were determined to construct preoperative and postoperative radiomics nomograms by multivariate logistic regression analysis. The performances of the clinical model, radiomics algorithm, and radiomics nomogram in distinguishing GISTs with the KIT exon 11 mutation were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC).Results The combined radiomics algorithm was found to be the best prediction model for differentiating the expression status of the KIT exon 11 mutation (AUC = 0.836; 95% confidence interval (CI), 0.640–0.951) in the validation set. The clinical model, and preoperative and postoperative radiomics nomograms had AUCs of 0.606 (95% CI, 0.397–0.790), 0.715 (95% CI, 0.506–0.873), and 0.679 (95% CI, 0.468–0.847), respectively, with the validation set.Conclusion The radiomics algorithm could distinguish GISTs with the KIT exon 11 mutation based on CE-CT images and could potentially be used for selective genetic analysis to support the precision medicine of GISTs.

scholarly journals Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Mou Li ◽  
Ling Yang ◽  
Yufeng Yue ◽  
Jingxu Xu ◽  
Chencui Huang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Performance ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Set ◽  
Combined Model ◽  
Contrast Enhanced ◽  
Apparent Diffusion ◽  
Validation Set

ObjectiveTo investigate whether a radiomics model can help to improve the performance of PI-RADS v2.1 in prostate cancer (PCa).MethodsThis was a retrospective analysis of 203 patients with pathologically confirmed PCa or non-PCa between March 2015 and December 2016. Patients were divided into a training set (n = 141) and a validation set (n = 62). The radiomics model (Rad-score) was developed based on multi-parametric MRI including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast enhanced (DCE) imaging. The combined model involving Rad-score and PI-RADS was compared with PI-RADS for the diagnosis of PCa by using the receiver operating characteristic curve (ROC) analysis.ResultsA total of 112 (55.2%) patients had PCa, and 91 (44.8%) patients had benign lesions. For PCa versus non-PCa, the Rad-score had a significantly higher area under the ROC curve (AUC) [0.979 (95% CI, 0.940–0.996)] than PI-RADS [0.905 (0.844–0.948), P = 0.002] in the training set. However, the AUC between them was insignificant in the validation set [0.861 (0.749–0.936) vs. 0.845 (0.731–0.924), P = 0.825]. When Rad-score was added to PI-RADS, the performance of the PI-RADS was significantly improved for the PCa diagnosis (AUC = 0.989, P < 0.001 for the training set and AUC = 0.931, P = 0.038 for the validation set).ConclusionsThe radiomics based on multi-parametric MRI can help to improve the diagnostic performance of PI-RADS v2.1 in PCa.

scholarly journals Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Wei Li ◽  
Xiao-Zhou Lv ◽  
Xin Zheng ◽  
Si-Min Ruan ◽  
Hang-Tong Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Differential Diagnosis ◽  
Focal Nodular Hyperplasia ◽  
Diagnostic Performance ◽  
Arterial Phase ◽  
Support Vector ◽  
Focal Liver Lesions ◽  
Machine Method ◽  
Nodular Hyperplasia

BackgroundThe typical enhancement patterns of hepatocellular carcinoma (HCC) on contrast-enhanced ultrasound (CEUS) are hyper-enhanced in the arterial phase and washed out during the portal venous and late phases. However, atypical variations make a differential diagnosis both challenging and crucial. We aimed to investigate whether machine learning-based ultrasonic signatures derived from CEUS images could improve the diagnostic performance in differentiating focal nodular hyperplasia (FNH) and atypical hepatocellular carcinoma (aHCC).Patients and MethodsA total of 226 focal liver lesions, including 107 aHCC and 119 FNH lesions, examined by CEUS were reviewed retrospectively. For machine learning-based ultrasomics, 3,132 features were extracted from the images of the baseline, arterial, and portal phases. An ultrasomics signature was generated by a machine learning model. The predictive model was constructed using the support vector machine method trained with the following groups: ultrasomics features, radiologist’s score, and combination of ultrasomics features and radiologist’s score. The diagnostic performance was explored using the area under the receiver operating characteristic curve (AUC).ResultsA total of 14 ultrasomics features were chosen to build an ultrasomics model, and they presented good performance in differentiating FNH and aHCC with an AUC of 0.86 (95% confidence interval [CI]: 0.80, 0.89), a sensitivity of 76.6% (95% CI: 67.5%, 84.3%), and a specificity of 80.5% (95% CI: 70.6%, 85.9%). The model trained with a combination of ultrasomics features and the radiologist’s score achieved a significantly higher AUC (0.93, 95% CI: 0.89, 0.96) than that trained with the radiologist’s score (AUC: 0.84, 95% CI: 0.79, 0.89, P < 0.001). For the sub-group of HCC with normal AFP value, the model trained with a combination of ultrasomics features, and the radiologist’s score remain achieved the highest AUC of 0.92 (95% CI: 0.87, 0.96) compared to that with the ultrasomics features (AUC: 0.86, 95% CI: 0.74, 0.89, P < 0.001) and radiologist’s score (AUC: 0.86, 95% CI: 0.79, 0.91, P < 0.001).ConclusionsMachine learning-based ultrasomics performs as well as the staff radiologist in predicting the differential diagnosis of FNH and aHCC. Incorporating an ultrasomics signature into the radiologist’s score improves the diagnostic performance in differentiating FNH and aHCC.

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