Predicting early recurrence of hepatocellular carcinoma with texture analysis of preoperative MRI: a radiomics study

Hui, Terrence Chi Hong; Chuah, Tong Kuan; Low, Hsien Min; Tan, Cher Heng

doi:10.1016/j.crad.2018.07.109

Cited by 55 publications

(48 citation statements)

References 25 publications

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“…In this study, the contributions of the different sequences to the prediction model were as follows: DCEI > DWI > T2WI > T1WI. Our results are in good agreement with those reported in previous radiomics studies of HCC, which suggested that the texture parameters on DCEI achieved the best performance compared with DWI or T2WI, 20 and that the radiomics signature based on T2WI demonstrated better predictive ability compared with T1WI. 21 Preoperative AFP level is an important prognostic marker of HCC associated with pathological grade, progression and survival.…”

Section: Discussionsupporting

confidence: 91%

“…Hui et al 20 analysed the largest cross-sectional tumour area on T2WI, DWI and DCEI in 50 patients with HCC and concluded that texture analysis on MRI had the potential to predict early recurrence with up to 84% accuracy using a single parameter. Wu et al 21 analysed three-dimensional tumour volume on T1WI and T2WI in 170 patients with HCC and found that the MRI radiomics signature could successfully categorise the grade of HCC; the AUC of the radiomics signature based on T1WI, T2WI and T1WI + T2WI was 0.712, 0.722 and 0.742, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…This research approach utilises high-throughput extraction of feature data from radiographic images, 12 and can potentially develop models to predict lesion phenotypes and prognosis in a non-invasive manner. 13,14 To our knowledge, there are relatively limited radiomics analysis data about prognosis estimation in HCC, with most of the radiomics models established on the basis of computed tomography (CT) images, [15][16][17][18][19] and only a few studies investigating the role of magnetic resonance imaging (MRI), [20][21][22][23] especially contrast-enhanced MRI. Moreover, previous MRI-based radiomics analyses of patients with HCC were based on a few MRI sequences from small-sample studies, and no MRI-based radiomics model for long-term survival prediction in HCC is currently available.…”

Section: Introductionmentioning

confidence: 99%

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MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma

et al. 2020

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BACKGROUND: Recurrence is the major cause of mortality in patients with resected HCC. However, without a standard approach to evaluate prognosis, it is difficult to select candidates for additional therapy. METHODS: A total of 201 patients with HCC who were followed up for at least 5 years after curative hepatectomy were enrolled in this retrospective, multicentre study. A total of 3144 radiomics features were extracted from preoperative MRI. The random forest method was used for radiomics signature building, and five-fold cross-validation was applied. A radiomics model incorporating the radiomics signature and clinical risk factors was developed. RESULTS: Patients were divided into survivor (n = 97) and non-survivor (n = 104) groups based on the 5-year survival after surgery. The 30 most survival-related radiomics features were selected for the radiomics signature. Preoperative AFP and AST were integrated into the model as independent clinical risk factors. The model demonstrated good calibration and satisfactory discrimination, with a mean AUC of 0.9804 and 0.7578 in the training and validation sets, respectively. CONCLUSIONS: This radiomics model is a valid method to predict 5-year survival in patients with HCC and may be used to identify patients for clinical trials of perioperative therapies and for additional surveillance.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma

et al. 2020

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Background: The purpose of this study was to develop and validate a radiomics nomogram for preoperative differentiating focal nodular hyperplasia (FNH) from hepatocellular carcinoma (HCC) in the non-cirrhotic liver. Methods: A total of 156 patients with FNH (n = 55) and HCC (n = 101) were divided into a training set (n = 119) and a validation set (n = 37). Radiomics features were extracted from triphasic contrast CT images. A radiomics signature was constructed with the least absolute shrinkage and selection operator algorithm, and a radiomics score (Radscore) was calculated. Clinical data and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed by multivariate logistic regression analysis. Nomogram performance was assessed with respect to discrimination and clinical usefulness. Results: Four thousand two hundred twenty-seven features were extracted and reduced to 10 features as the most important discriminators to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.964; 95% confidence interval [CI], 0.934-0.995) and the validation set (AUC, 0.865; 95% CI, 0.725-1.000). Age, Hepatitis B virus infection, and enhancement pattern were the independent clinical factors. The radiomics nomogram, which incorporated the Rad-score and clinical factors, showed good discrimination in the training set (AUC, 0.979; 95% CI, 0.959-0.998) and the validation set (AUC, 0.917; 95% CI, 0.800-1.000), and showed better discrimination capability (P < 0.001) compared with the clinical factors model (AUC, 0.799; 95% CI, 0.719-0.879) in the training set. Decision curve analysis showed the nomogram outperformed the clinical factors model in terms of clinical usefulness. Conclusions: The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating FNH from HCC in the noncirrhotic liver, which might facilitate clinical decision-making process.

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“…Hepatocellular carcinoma (HCC) is the most common primary liver malignancy in adults and is the most common cause of death in people with cirrhosis. HCC ranks as the second most common cause of cancer death worldwide, and more than 500,000 new patients are diagnosed annually (1)(2)(3)(4)(5). Detection, characterization, and identification of appropriate treatment strategies and improvement of HCC prognosis have always been the major concerns in the clinic.…”

Section: Introductionmentioning

confidence: 99%

A scientometric analysis on hepatocellular carcinoma magnetic resonance imaging research from 2008 to 2017

Wang

Yang

et al. 2019

Quant. Imaging Med. Surg

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Background: With the development of new magnetic resonance imaging (MRI) techniques, an increasing number of articles have been published regarding hepatocellular carcinoma magnetic resonance imaging (HCCMRI) in the past decade. However, few studies have statistically analyzed these published articles. In this study, we aim to systematically evaluate the scientific outcomes of HCCMRI research and explore the research hotspots from 2008 to 2017. Methods: The included articles regarding HCCMRI research from 2008 to 2017 were downloaded from the Web of Science Core Collection and verified by two experienced radiologists. Excel 2016 was used to analyze the literature data, including the publication years and journals. CiteSpace V was used to perform cooccurrence analyses for authors, countries/regions and institutions and to generate the related collaboration network maps. Reference co-citation analysis (RCA) and burst keyword detection were also performed using CiteSpace V to explore the research hotspots in the past decade. Results: A total of 835 HCCMRI articles published from 2008 to 2017 were identified. Journal of Magnetic Resonance Imaging published the most articles (79 publications, 9.46%). Extensive cooperating relationship were observed among countries/regions and among authors. South Korea had the most publications (199 publications, 21.82%), followed by the United States of America (USA) (190 publications, 20.83%), Japan (162 publications, 17.76%), and the People's Republic of China (148 publications, 16.23%). Among the top 10 co-cited authors, Bruix J (398 citations) was ranked first, followed by Llovet JM (235 citations), Kim YK (170 citations) and Forner A (152 citations). According to the RCA, ten major clusters were explored over the last decade; "LI-RADS data system" and "microvascular invasion" (MVI) were the two most recent clusters. Forty-seven burst keywords with the highest citation strength were detected over time. Of these keywords, "microvascular invasion" had the highest strength in the last 3 years. The LI-RADS has been constantly updated with the latest edition released in July 2018. However, the LI-RADS still has limitations in identifying certain categories of lesions by conceptual and non-quantitative probabilistic methods. Plenty of questions still need to be further answered such as the difference of diagnostic efficiency of each major/ancillary imaging features. Preoperative prediction of MVI of HCC is very important to therapeutic decision-making. Some parameters of Gd-EOB-DTPA-enhanced MRI were found to be useful in prediction of MVI, however, with a high specificity but a very low sensitivity. Comprehensive predictive model incorporating both imaging and clinical variables may be the more preferable in prediction of MVI of HCC. Conclusions: HCCMRI-related publications displayed a gradually increasing trend from 2008 to 2017. The USA has a central position in collaboration with other countries/regions, while South Korea contributed the most in the number of publications. Of the t...

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Predicting early recurrence of hepatocellular carcinoma with texture analysis of preoperative MRI: a radiomics study

Cited by 55 publications

References 25 publications

MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma

MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma

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

A scientometric analysis on hepatocellular carcinoma magnetic resonance imaging research from 2008 to 2017

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