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

Abstract

BackgroundTo evaluate the feasibility of using radiomics with precontrast magnetic resonance imaging for classifying hepatocellular carcinoma (HCC) and hepatic haemangioma (HH).MethodsThis 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.ResultsValuable 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).ConclusionsWe 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).

Biomarkers in Hepatocellular Carcinoma: Diagnosis, Prognosis and Treatment Response Assessment

“…Appropriate candidate selection for each therapeutic modality based solely on these biomarkers is still far away from its clinical applicability in the clinical decision-making processes. Ideal biomarkers for HCC are those that would enable clinicians to diagnose this cancer at asymptomatic stages and also, to help and identify better candidates in each tumor stage for appropriate therapeutic modalities [ 186 , 187 ]. So far, there is still a need for specific biomarkers to improve detection of HCC at early or very early stages, assess specific prognosis and prediction of treatment response.…”

“…Finally, radiomics in HCC is a novel but very preliminary approach based on artificial intelligence and imaging data [ 184 , 185 , 186 ]. Imaging features are registered on machine-learning algorithms, and these signs can assess and help HCC diagnosis and prognosis.…”

A CT-based radiomics nomogram for differentiation of focal nodular hyperplasia from hepatocellular carcinoma in the non-cirrhotic liver

“…Seventeen FNHs, 19 HCAs, 25 HCCs, and 19 cases of normal liver parenchyma were analyzed, and the texture model successfully distinguished the three lesion types and normal liver with predicted classification performance accuracy for 91.2% for HCA, 94.4% for FNH, and 98.6% for HCC. Wu et al [37] developed and validated an MRI-based radiomics signature to distinguish HCC and HH using four feature classifiers, and found that the logistic regression classifier showed better predictive ability, achieving an AUC of 0.89 for differentiating HCC from HH. Stocker et al [38] enrolled 55 cases of HCC and 45 cases of benign FNHs) in the non-cirrhotic liver to assess the accuracy of MRI texture features in differentiating benign from malignant liver tumours.…”

“…Radiomics, as an emerging field involved with the extraction of high-throughput data from quantitative imaging features and the subsequent combination of this information with clinical data, has the potential to provide diagnostic, prognostic, and predictive information and improve clinical decision making [16,17]. Successful applications of radiomics in liver tumours have been reported in prediction of histologic grade, recurrence, liver failure and survival after curative treatment or chemotherapy in HCC patients [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33], in preoperative prediction of HCC microvascular invasion [34][35][36], in differentiating benign hepatic lesions (including hepatic haemangioma [HH], HCA, FNH, and hepatic abscess) from malignant tumours (including HCC and metastases) [37][38][39][40][41] and in discriminating different benign (HCA and FNH) [42,43] or malignant liver tumours (HCC, intrahepatic cholangiocarcinoma [ICC] and combined HCC-ICC) [44]. To the best of our knowledge, few studies focused on radiomics in differentiating HCC from FNH in non-cirrhotic patients.…”

Radiomics for liver tumours

“…Morphological and functional characterization of liver tumours with and without contrast-enhanced sequences is the state of the art in oncologic liver imaging. Recent radiomics studies demonstrated for the first time the predictive value for different liver tumours, such as the grade of hepatocellular carcinoma (HCC) or the differential diagnosis of other primary or secondary liver tumours and benign liver lesions [9][10][11][12][13]. Table 1 summarizes these studies.…”