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Radiomics-based classification of hepatocellular carcinoma and hepatic haemangioma on precontrast magnetic resonance images

BMC Medical Imaging. 2019;19(1):1-11 DOI 10.1186/s12880-019-0321-9

 

Journal Homepage

Journal Title: BMC Medical Imaging

ISSN: 1471-2342 (Online)

Publisher: BMC

LCC Subject Category: Medicine: Medicine (General): Medical technology

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS


Jingjun Wu (Department of Radiology, the First Affiliated Hospital of Dalian Medical University; Xigang district)

Ailian Liu (Department of Radiology, the First Affiliated Hospital of Dalian Medical University; Xigang district)

Jingjing Cui (Huiying Medical Technology Co., Ltd)

Anliang Chen (Department of Radiology, the First Affiliated Hospital of Dalian Medical University; Xigang district)

Qingwei Song (Department of Radiology, the First Affiliated Hospital of Dalian Medical University; Xigang district)

Lizhi Xie (GE Healthcare, MR Research)

EDITORIAL INFORMATION

Open peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 27 weeks

 

Abstract | Full Text

Abstract Background To evaluate the feasibility of using radiomics with precontrast magnetic resonance imaging for classifying hepatocellular carcinoma (HCC) and hepatic haemangioma (HH). Methods This study enrolled 369 consecutive patients with 446 lesions (a total of 222 HCCs and 224 HHs). A training set was constituted by randomly selecting 80% of the samples and the remaining samples were used to test. On magnetic resonance (MR) images of HCC and HH obtained with in-phase, out-phase, T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) sequences, we outlined the target lesions and extracted 1029 radiomics features, which were classified as first-, second-, higher-order statistics and shape features. Then, the variance threshold, select k best, and least absolute shrinkage and selection operator algorithms were explored for dimensionality reduction of the features. We used four classifiers (decision tree, random forest, K nearest neighbours, and logistic regression) to identify HCC and HH on the basis of radiomics features. Two abdominal radiologists also performed the conventional qualitative analysis for classification of HCC and HH. Diagnostic performances of radiomics and radiologists were evaluated by receiver operating characteristic (ROC) analysis. Results Valuable radiomics features for building a radiomics signature were extracted from in-phase (n = 22), out-phase (n = 24), T2WI (n = 34) and DWI (n = 24) sequences. In comparison, the logistic regression classifier showed better predictive ability by combining four sequences. In the training set, the area under the ROC curve (AUC) was 0.86 (sensitivity: 0.76; specificity: 0.78), and in the testing set, the AUC was 0.89 (sensitivity: 0.822; specificity: 0.714). The diagnostic performance for the optimal radiomics-based combined model was significantly higher than that for the less experienced radiologist (2-years experience) (AUC = 0.702, p < 0.05), and had no statistic difference with the experienced radiologist (10-years experience) (AUC = 0.908, p>0.05). Conclusions We developed and validated a radiomics signature as an adjunct tool to distinguish HCC and HH by combining in-phase, out-phase, T2W, and DW MR images, which outperformed the less experienced radiologist (2-years experience), and was nearly equal to the experienced radiologist (10-years experience).