» Articles » PMID: 32962762

The Application of Texture Quantification in Hepatocellular Carcinoma Using CT and MRI: a Review of Perspectives and Challenges

Overview
Journal Cancer Imaging
Publisher Springer Nature
Specialties Oncology
Radiology
Date 2020 Sep 23
PMID 32962762
Citations 16
Authors
Affiliations
Soon will be listed here.
Abstract

Recently, radiomic texture quantification of tumors has received much attention from radiologists, scientists, and stakeholders because several results have shown the feasibility of using the technique to diagnose and manage oncological conditions. In patients with hepatocellular carcinoma, radiomics has been applied in all stages of tumor evaluation, including diagnosis and characterization of the genotypic behavior of the tumor, monitoring of treatment responses and prediction of various clinical endpoints. It is also useful in selecting suitable candidates for specific treatment strategies. However, the clinical validation of hepatocellular carcinoma radiomics is limited by challenges in imaging protocol and data acquisition parameters, challenges in segmentation techniques, dimensionality reduction, and modeling methods. Identification of the best segmentation and optimal modeling methods, as well as texture features most stable to imaging protocol variability would go a long way in harmonizing HCC radiomics for personalized patient care. This article reviews the process of HCC radiomics, its clinical applications, associated challenges, and current optimization strategies.

Citing Articles

Integrated single-cell and bulk RNA sequencing reveals immune-related SPP1+ macrophages as a potential strategy for predicting the prognosis and treatment of liver fibrosis and hepatocellular carcinoma.

Li B, Hu J, Xu H Front Immunol. 2024; 15:1455383.

PMID: 39635536 PMC: 11615077. DOI: 10.3389/fimmu.2024.1455383.


Prognostic role of radiomics-based body composition analysis for the 1-year survival for hepatocellular carcinoma patients.

Saalfeld S, Kreher R, Hille G, Niemann U, Hinnerichs M, Ocal O J Cachexia Sarcopenia Muscle. 2023; 14(5):2301-2309.

PMID: 37592827 PMC: 10570090. DOI: 10.1002/jcsm.13315.


MRI texture analysis of acetabular cancellous bone can discriminate between normal, cam positive, and cam-FAI hips.

Hodgdon T, Thornhill R, James N, Melkus G, Beaule P, Rakhra K Eur Radiol. 2023; 33(11):8324-8332.

PMID: 37231069 DOI: 10.1007/s00330-023-09748-0.


Development and validation of MRI-based model for the preoperative prediction of macrotrabecular hepatocellular carcinoma subtype.

Masokano I, Pei Y, Chen J, Liu W, Xie S, Liu H Insights Imaging. 2022; 13(1):201.

PMID: 36544029 PMC: 9772375. DOI: 10.1186/s13244-022-01333-1.


Essential amino acids as diagnostic biomarkers of hepatocellular carcinoma based on metabolic analysis.

Morine Y, Utsunomiya T, Yamanaka-Okumura H, Saito Y, Yamada S, Ikemoto T Oncotarget. 2022; 13:1286-1298.

PMID: 36441784 PMC: 11623405. DOI: 10.18632/oncotarget.28306.


References
1.
Mackin D, Ger R, Dodge C, Fave X, Chi P, Zhang L . Effect of tube current on computed tomography radiomic features. Sci Rep. 2018; 8(1):2354. PMC: 5799381. DOI: 10.1038/s41598-018-20713-6. View

2.
Zheng B, Liu L, Zhang Z, Shi J, Dong L, Tian L . Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients. BMC Cancer. 2018; 18(1):1148. PMC: 6249916. DOI: 10.1186/s12885-018-5024-z. View

3.
Castellano G, Bonilha L, Li L, Cendes F . Texture analysis of medical images. Clin Radiol. 2004; 59(12):1061-9. DOI: 10.1016/j.crad.2004.07.008. View

4.
Yan J, Schwartz L, Zhao B . Semiautomatic segmentation of liver metastases on volumetric CT images. Med Phys. 2015; 42(11):6283-93. PMC: 4600084. DOI: 10.1118/1.4932365. View

5.
Kim J, Choi S, Lee S, Lee H, Park H . Predicting Survival Using Pretreatment CT for Patients With Hepatocellular Carcinoma Treated With Transarterial Chemoembolization: Comparison of Models Using Radiomics. AJR Am J Roentgenol. 2018; 211(5):1026-1034. DOI: 10.2214/AJR.18.19507. View