Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading

Overview

Journal Front Oncol

Specialty Oncology

Date 2021 Jun 21

PMID 34150628

Citations 10

Authors

Wen Chen

Tao Zhang

Lin Xu

Liang Zhao

Huan Liu

Liang Rui Gu

Dai Zhong Wang

Ming Zhang

Affiliations

Soon will be listed here.

Abstract

Objectives: To investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery.

Methods: The retrospective study including 161 consecutive subjects with HCC which was approved by the institutional review board, and the patients were divided into a training group (n = 112) and test group (n = 49) from January 2013 to January 2018. The least absolute shrinkage and selection operator (LASSO) was used to select the most valuable features to build a support vector machine (SVM) model. The performance of the predictive model was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity.

Results: The SVM model showed an acceptable ability to differentiate high-grade from low-grade HCC, with an AUC of 0.904 in the training dataset and 0.937 in the test dataset, accuracy (92.2% versus 95.7%), sensitivity(82.5% versus 88.0%), and specificity (92.7% versus 95.8%), respectively.

Conclusion: The machine learning-based radiomics reflects a better evaluating performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment.

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References

Huang Y, Liu Z, He L, Chen X, Pan D, Ma Z . Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer. Radiology. 2016; 281(3):947-957. DOI: 10.1148/radiol.2016152234. View

Nazari M, Shiri I, Hajianfar G, Oveisi N, Abdollahi H, Deevband M . Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning. Radiol Med. 2020; 125(8):754-762. DOI: 10.1007/s11547-020-01169-z. View

Tabrizian P, Jibara G, Shrager B, Schwartz M, Roayaie S . Recurrence of hepatocellular cancer after resection: patterns, treatments, and prognosis. Ann Surg. 2014; 261(5):947-55. DOI: 10.1097/SLA.0000000000000710. View

Davnall F, Yip C, Ljungqvist G, Selmi M, Ng F, Sanghera B . Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?. Insights Imaging. 2012; 3(6):573-89. PMC: 3505569. DOI: 10.1007/s13244-012-0196-6. View

Perez-Saborido B, de los Galanes S, Meneu-Diaz J, Jimenez Romero C, Moreno Elola-Olaso A, Fundora Suarez Y . Tumor recurrence after liver transplantation for hepatocellular carcinoma: recurrence pathway and prognostic factors. Transplant Proc. 2007; 39(7):2304-7. DOI: 10.1016/j.transproceed.2007.06.059. View

Tarhan N, Hatipoglu T, Ercan E, Bener M, Keles G, Basaran C . Correlation of dynamic multidetector CT findings with pathological grades of hepatocellular carcinoma. Diagn Interv Radiol. 2011; 17(4):328-33. DOI: 10.4261/1305-3825.DIR.2682-09.3. View

EDMONDSON H, STEINER P . Primary carcinoma of the liver: a study of 100 cases among 48,900 necropsies. Cancer. 1954; 7(3):462-503. DOI: 10.1002/1097-0142(195405)7:3<462::aid-cncr2820070308>3.0.co;2-e. View

Kulik L, El-Serag H . Epidemiology and Management of Hepatocellular Carcinoma. Gastroenterology. 2018; 156(2):477-491.e1. PMC: 6340716. DOI: 10.1053/j.gastro.2018.08.065. View

Oh J, Lee J, Park J, Joo I, Yoon J, Lee D . Hepatocellular Carcinoma: Texture Analysis of Preoperative Computed Tomography Images Can Provide Markers of Tumor Grade and Disease-Free Survival. Korean J Radiol. 2019; 20(4):569-579. PMC: 6424831. DOI: 10.3348/kjr.2018.0501. View

10.

Wang H, Chen H, Duan S, Hao D, Liu J . Radiomics and Machine Learning With Multiparametric Preoperative MRI May Accurately Predict the Histopathological Grades of Soft Tissue Sarcomas. J Magn Reson Imaging. 2019; 51(3):791-797. DOI: 10.1002/jmri.26901. View

11.

Kumar V, Gu Y, Basu S, Berglund A, Eschrich S, Schabath M . Radiomics: the process and the challenges. Magn Reson Imaging. 2012; 30(9):1234-48. PMC: 3563280. DOI: 10.1016/j.mri.2012.06.010. View

12.

Longlong Z, Xinxiang L, Yaqiong G, Wei W . Predictive Value of the Texture Analysis of Enhanced Computed Tomographic Images for Preoperative Pancreatic Carcinoma Differentiation. Front Bioeng Biotechnol. 2020; 8:719. PMC: 7339088. DOI: 10.3389/fbioe.2020.00719. View

13.

Nitta H, Allard M, Sebagh M, Karam V, Ciacio O, Pittau G . Predictive model for microvascular invasion of hepatocellular carcinoma among candidates for either hepatic resection or liver transplantation. Surgery. 2019; 165(6):1168-1175. DOI: 10.1016/j.surg.2019.01.012. View

14.

Yasar S, Voyvoda N, Voyvoda B, Ozer T . Using texture analysis as a predictive factor of subtype, grade and stage of renal cell carcinoma. Abdom Radiol (NY). 2020; 45(11):3821-3830. DOI: 10.1007/s00261-020-02495-6. View

15.

Njei B, Rotman Y, Ditah I, Lim J . Emerging trends in hepatocellular carcinoma incidence and mortality. Hepatology. 2014; 61(1):191-9. PMC: 4823645. DOI: 10.1002/hep.27388. View

16.

Zhou L, Rui J, Zhou W, Wang S, Chen S, Qu Q . Edmondson-Steiner grade: A crucial predictor of recurrence and survival in hepatocellular carcinoma without microvascular invasio. Pathol Res Pract. 2017; 213(7):824-830. DOI: 10.1016/j.prp.2017.03.002. View

17.

Vamvakas A, Williams S, Theodorou K, Kapsalaki E, Fountas K, Kappas C . Imaging biomarker analysis of advanced multiparametric MRI for glioma grading. Phys Med. 2019; 60:188-198. DOI: 10.1016/j.ejmp.2019.03.014. View

18.

Huang P, Wang C, Lin C, Lu S, Wang J, Hung C . Predictive Effects of Inflammatory Scores in Patients with BCLC 0-A Hepatocellular Carcinoma after Hepatectomy. J Clin Med. 2019; 8(10). PMC: 6832545. DOI: 10.3390/jcm8101676. View

19.

Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M . Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2014; 136(5):E359-86. DOI: 10.1002/ijc.29210. View

20.

Zhou W, Zhang L, Wang K, Chen S, Wang G, Liu Z . Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images. J Magn Reson Imaging. 2016; 45(5):1476-1484. DOI: 10.1002/jmri.25454. View