Dataset Information

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models.

ABSTRACT: Background: We conduct a study in developing and validating two radiomics-based models to preoperatively distinguish hepatic epithelioid angiomyolipoma (HEAML) from hepatic carcinoma (HCC) as well as focal nodular hyperplasia (FNH). Methods: Totally, preoperative contrast-enhanced computed tomography (CT) data of 170 patients and preoperative contrast-enhanced magnetic resonance imaging (MRI) data of 137 patients were enrolled in this study. Quantitative texture features and wavelet features were extracted from the regions of interest (ROIs) of each patient imaging data. Then two radiomics signatures were constructed based on CT and MRI radiomics features, respectively, using the random forest (RF) algorithm. By integrating radiomics signatures with clinical characteristics, two radiomics-based fusion models were established through multivariate linear regression and 10-fold cross-validation. Finally, two diagnostic nomograms were built to facilitate the clinical application of the fusion models. Results: The radiomics signatures based on the RF algorithm achieved the optimal predictive performance in both CT and MRI data. The area under the receiver operating characteristic curves (AUCs) reached 0.996, 0.879, 0.999, and 0.925 for the training as well as test cohort from CT and MRI data, respectively. Then, two fusion models simultaneously integrated clinical characteristics achieved average AUCs of 0.966 (CT data) and 0.971 (MRI data) with 10-fold cross-validation. Through decision curve analysis, the fusion models were proved to be excellent models to distinguish HEAML from HCC and FNH in comparison between the clinical models and radiomics signatures. Conclusions: Two radiomics-based models derived from CT and MRI images, respectively, performed well in distinguishing HEAML from HCC and FNH and might be potential diagnostic tools to formulate individualized treatment strategies.

SUBMITTER: Liang W

PROVIDER: S-EPMC7573543 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models.

Liang Wenjie W Shao Jiayuan J Liu Weihai W Ruan Shijian S Tian Wuwei W Zhang Xiuming X Wan Dalong D Huang Qiang Q Ding Yong Y Xiao Wenbo W

Frontiers in oncology 20201006

<b>Background:</b> We conduct a study in developing and validating two radiomics-based models to preoperatively distinguish hepatic epithelioid angiomyolipoma (HEAML) from hepatic carcinoma (HCC) as well as focal nodular hyperplasia (FNH). <b>Methods:</b> Totally, preoperative contrast-enhanced computed tomography (CT) data of 170 patients and preoperative contrast-enhanced magnetic resonance imaging (MRI) data of 137 patients were enrolled in this study. Quantitative texture features and wavele ...[more]

PMID: 33123475

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Project description: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.

Project description:Introduction: Epithelioid hepatic angiomyolipoma (Epi-HAML) can easily be misdiagnosed as a malignant tumor such as hepatocellular carcinoma (HCC) because of the low-fat content on imaging. We analyzed and compared the magnetic resonance imaging (MRI) features of Epi-HAML and HCC, which would aid in disease diagnosis. Methods: We included 30 pathologically confirmed patients with Epi-HAML and 80 with HCC, who underwent both MRI unenhanced scan and three-phase contrast-enhanced MRI scan. The clinical and MRI features of the two groups were summarized and analyzed. Results: Epi-HAML showed significant differences compared to HCC group in terms of clinical features such as sex preference, age, concomitant diseases (hepatitis B and cirrhosis), and elevated plasma alpha-fetoprotein (AFP) (P < 0.001). In addition, there were statistically significant differences between both tumor types with regard to conventional MRI findings such as a solitary tumor (100 vs. 83.8%, P = 0.018), well-defined (93.3 vs. 71.3%, P = 0.027), mild hyperintensity (40.0 vs. 3.7%, P < 0.001) on DWI with high b-value, fat within the tumor (43.3 vs. 8.8%, P < 0.001), and rare necrosis (3.3 vs. 26.3%, P = 0.016). Besides, Epi-HAML displayed significant differences compared to HCC in terms of contrast-enhanced MRI characteristics such as draining hepatic vein (30.0 vs. 3.8%, P < 0.001), portal vein tumor thrombus (0 vs. 13.8%, P = 0.033), hypointensity at delayed phase (70.0 vs. 95%, P = 0.001), intra-tumor vessel at delayed phase (36.7 vs. 10.0%, P = 0.003), pseudocapsule (20.0 vs. 78.8%, P < 0.001), and prolonged enhancement (56.7 vs. 1.2%, P < 0.001). Conclusion: Epi-HAML frequently occurs in middle-aged women and usually lacks characteristic clinical symptoms. Typically, Epi-HAML presents as an isolated and well-defined tumor with rich vasculature. Specific MRI features such as intra-tumor fat, intra-tumor vessel, draining hepatic vein, prolonged enhancement, and lack of capsule may contribute to a more confident diagnosis of Epi-HAML.

Dataset Information

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models.

Publications

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models.

Similar Datasets

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