Characterization of Microvascular Invasion in Hepatocellular Carcinoma Using Computational Modeling of Interstitial Fluid Pressure and Velocity.
hepatocellular carcinoma
interstitial fluid pressure and velocity
microvascular invasion
Journal
Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850
Informations de publication
Date de publication:
11 2023
11 2023
Historique:
revised:
28
01
2023
received:
13
11
2022
accepted:
30
01
2023
medline:
23
10
2023
pubmed:
11
2
2023
entrez:
10
2
2023
Statut:
ppublish
Résumé
Most solid tumors show increased interstitial fluid pressure (IFP), and this increased IFP is an obstacle to treatment. A noninvasive model for measuring IFP in hepatocellular carcinoma (HCC) is an unresolved issue. To develop a noninvasive model to measure IFP and interstitial fluid velocity (IFV) in HCC and to characterize the microvascular invasion (MVI) status by using this model. Retrospective. A total of 97 HCC patients (mean age 57.6 ± 10.9 years, 77.3% males), 53 of them with MVI and 44 of them without MVI. A 3-T, three-dimensional spoiled gradient-recalled echo. MVI was defined as microscopic vascular invasion of small vessels within the peritumoral liver tissue. The volumes of interest (VOIs) were manually delineated and enclosed the tumor lesion and healthy liver parenchyma, respectively. The extended Tofts model (ETM) was used to estimate permeability parameters from all the VOIs. Subsequently, the continuity partial differential equation (PDE) was implemented and IFP and IFV were acquired. Wilcoxon signed-ranks tests, histogram analysis, Mann-Whitney U test, Fisher's exact test, least absolute shrinkage and selection operator (LASSO) logistic regression, receiver operating characteristic (ROC) curve analysis with the area under the curve (AUC), Youden index, DeLong test, and Benjamini-Hochberg correction. A P value <0.05 was considered statistically significant. The HCC lesions exhibited elevated IFP and reduced IFV. There were no significant differences in any measured demographic and clinical features between the MVI-positive and MVI-negative groups, except for tumor size. Nine IFP histogram analysis-derived parameters and seven IFV histogram analysis-derived parameters could be used to characterize the MVI status. LASSO regression selected five features: IFP maximum, IFP 10th percentile, IFP 90th percentile, IFV SD, and IFV 10th percentile. The combination of these features showed the highest AUC (0.781) and specificity (77.3%). A noninvasive IFP and IFV measurement model for HCC was developed. Specific IFP- and IFV-derived parameters exhibited significant association with the MVI status. 3. Stage 2.
Sections du résumé
BACKGROUND
Most solid tumors show increased interstitial fluid pressure (IFP), and this increased IFP is an obstacle to treatment. A noninvasive model for measuring IFP in hepatocellular carcinoma (HCC) is an unresolved issue.
PURPOSE
To develop a noninvasive model to measure IFP and interstitial fluid velocity (IFV) in HCC and to characterize the microvascular invasion (MVI) status by using this model.
STUDY TYPE
Retrospective.
POPULATION
A total of 97 HCC patients (mean age 57.6 ± 10.9 years, 77.3% males), 53 of them with MVI and 44 of them without MVI.
FIELD STRENGTH/SEQUENCE
A 3-T, three-dimensional spoiled gradient-recalled echo.
ASSESSMENT
MVI was defined as microscopic vascular invasion of small vessels within the peritumoral liver tissue. The volumes of interest (VOIs) were manually delineated and enclosed the tumor lesion and healthy liver parenchyma, respectively. The extended Tofts model (ETM) was used to estimate permeability parameters from all the VOIs. Subsequently, the continuity partial differential equation (PDE) was implemented and IFP and IFV were acquired.
STATISTICAL TESTS
Wilcoxon signed-ranks tests, histogram analysis, Mann-Whitney U test, Fisher's exact test, least absolute shrinkage and selection operator (LASSO) logistic regression, receiver operating characteristic (ROC) curve analysis with the area under the curve (AUC), Youden index, DeLong test, and Benjamini-Hochberg correction. A P value <0.05 was considered statistically significant.
RESULTS
The HCC lesions exhibited elevated IFP and reduced IFV. There were no significant differences in any measured demographic and clinical features between the MVI-positive and MVI-negative groups, except for tumor size. Nine IFP histogram analysis-derived parameters and seven IFV histogram analysis-derived parameters could be used to characterize the MVI status. LASSO regression selected five features: IFP maximum, IFP 10th percentile, IFP 90th percentile, IFV SD, and IFV 10th percentile. The combination of these features showed the highest AUC (0.781) and specificity (77.3%).
DATA CONCLUSION
A noninvasive IFP and IFV measurement model for HCC was developed. Specific IFP- and IFV-derived parameters exhibited significant association with the MVI status.
EVIDENCE LEVEL
3.
TECHNICAL EFFICACY
Stage 2.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1366-1374Informations de copyright
© 2023 International Society for Magnetic Resonance in Medicine.
Références
Iguchi T, Shirabe K, Aishima S, et al. New pathologic stratification of microvascular invasion in hepatocellular carcinoma: Predicting prognosis after living-donor liver transplantation. Transplantation 2015;99(6):1236-1242.
Lim K-C, Chow PK-H, Allen JC, et al. Microvascular invasion is a better predictor of tumor recurrence and overall survival following surgical resection for hepatocellular carcinoma compared to the Milan criteria. Ann Surg 2011;254(1):108-113.
Guerrero-Misas M, Rodríguez-Perálvarez M, De la Mata M. Strategies to improve outcome of patients with hepatocellular carcinoma receiving a liver transplantation. World J Hepatol 2015;7(4):649-661.
Feng L-H, Dong H, Lau W-Y, et al. Novel microvascular invasion-based prognostic nomograms to predict survival outcomes in patients after R0 resection for hepatocellular carcinoma. J Cancer Res Clin Oncol 2017;143(2):293-303.
Sheng X, Ji Y, Ren G-P, et al. A standardized pathological proposal for evaluating microvascular invasion of hepatocellular carcinoma: A multicenter study by LCPGC. Hepatol Int 2020;14(6):1034-1047.
Roayaie S, Blume IN, Thung SN, et al. A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma. Gastroenterology 2009;137(3):850-855.
Banerjee S, Wang DS, Kim HJ, et al. A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma. Hepatology 2015;62(3):792-800.
Ahn SY, Lee JM, Joo I, et al. Prediction of microvascular invasion of hepatocellular carcinoma using gadoxetic acid-enhanced MR and 18 F-FDG PET/CT. Abdom Imaging 2015;40(4):843-851.
Zhang L, Yu X, Wei W, et al. Prediction of HCC microvascular invasion with gadobenate-enhanced MRI: Correlation with pathology. Eur Radiol 2020;30(10):5327-5336.
Yang C, Wang H, Sheng R, Ji Y, Rao S, Zeng M. Microvascular invasion in hepatocellular carcinoma: Is it predictable with a new, preoperative application of diffusion-weighted imaging? Clin Imaging 2017;41:101-105.
Lei Z, Li J, Wu D, et al. Nomogram for preoperative estimation of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma within the Milan criteria. JAMA Surg 2016;151(4):356-363.
Rhee H, An C, Kim H-Y, Yoo JE, Park YN, Kim M-J. Hepatocellular carcinoma with irregular rim-like arterial phase hyperenhancement: More aggressive pathologic features. Liver Cancer 2019;8(1):24-40.
Song Q, Guo Y, Yao X, et al. Comparative study of evaluating the microcirculatory function status of primary small HCC between the CE (DCE-MRI) and non-CE (IVIM-DWI) MR perfusion imaging. Abdom Radiol. 2021;46(6):2575-2583.
Wu L, Yang C, Halim A, et al. Contrast-enhanced magnetic resonance imaging perfusion can predict microvascular invasion in patients with hepatocellular carcinoma (between 1 and 5 cm). Abdom Radiol 2022;47(9):3264-3275.
Gao W, Wang W, Song D, et al. A multiparametric fusion deep learning model based on DCE-MRI for preoperative prediction of microvascular invasion in intrahepatic cholangiocarcinoma. J Magn Reson Imaging 2022;56:1029-1039.
Song D, Wang Y, Wang W, et al. Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters. J Cancer Res Clin Oncol 2021;147(12):3757-3767.
Li C-H, Chen F-H, Schellingerhout D, Lin Y-S, Hong J-H, Liu H-L. Flow versus permeability weighting in estimating the forward volumetric transfer constant (Ktrans) obtained by DCE-MRI with contrast agents of differing molecular sizes. Magn Reson Imaging 2017;36:105-111.
Chen J, Chen C, Xia C, et al. Quantitative free-breathing dynamic contrast-enhanced MRI in hepatocellular carcinoma using gadoxetic acid: Correlations with Ki67 proliferation status, histological grades, and microvascular density. Abdom Radiol 2018;43(6):1393-1403.
Paudyal R, LoCastro E, Reyngold M, et al. Longitudinal monitoring of simulated interstitial fluid pressure for pancreatic ductal adenocarcinoma patients treated with stereotactic body radiotherapy. Cancer 2021;13(17):4319.
Wiig H, Noddeland H. Interstitial fluid pressure in human skin measured by micropuncture and wick-in-needle. Scand J Clin Lab Invest 1983;43(3):255-260.
Quaglia A, Etessami N, Sim R, Difford J, Dhillon A. Vascular invasion and herniation by hepatocellular carcinoma in cirrhosis: A wolf in sheep's clothing? Arch Pathol Lab Med 2005;129(5):639-644.
LoCastro E, Paudyal R, Mazaheri Y, et al. Computational modeling of interstitial fluid pressure and velocity in head and neck cancer based on dynamic contrast-enhanced magnetic resonance imaging: Feasibility analysis. Tomography 2020;6(2):129-138.
Harrell FE. Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis: New York: Springer; 2015.
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 1988;44(3):837-845.
Llovet JM, Bruix J. Novel advancements in the management of hepatocellular carcinoma in 2008. J Hepatol 2008;48:S20-S37.
Pawlik TM, Gleisner AL, Anders RA, Assumpcao L, Maley W, Choti MA. Preoperative assessment of hepatocellular carcinoma tumor grade using needle biopsy: Implications for transplant eligibility. Ann Surg 2007;245(3):435-442.
Chen Z-H, Zhang X-P, Wang H, et al. Effect of microvascular invasion on the postoperative long-term prognosis of solitary small HCC: A systematic review and meta-analysis. HPB 2019;21(8):935-944.
Hong SY, Wang HJ, Kim BW, Kim M, Shen XY. Prediction of microvascular invasion of hepatocellular carcinoma by preoperative assessment and pathological findings: Ajou University Hospital experience. HPB 2019;21:S373.
Song L, Li J, Luo Y. The importance of a nonsmooth tumor margin and incomplete tumor capsule in predicting HCC microvascular invasion on preoperative imaging examination: A systematic review and meta-analysis. Clin Imaging 2021;76:77-82.
Kaibori M, Ishizaki M, Matsui K, Kwon AH. Predictors of microvascular invasion before hepatectomy for hepatocellular carcinoma. J Surg Oncol 2010;102(5):462-468.
Jhaveri KS, Cleary SP, Fischer S, et al. Blood oxygen level-dependent liver MRI: Can it predict microvascular invasion in HCC? J Magn Reson Imaging 2013;37(3):692-699.
Salavati H, Debbaut C, Pullens P, Ceelen W. Interstitial fluid pressure as an emerging biomarker in solid tumors. Biochim Biophys Acta Rev Cancer 2022;1877(5):188792.
Heldin C-H, Rubin K, Pietras K, Östman A. High interstitial fluid pressure-An obstacle in cancer therapy. Nat Rev Cancer 2004;4(10):806-813.
Hori K, Suzuki M, Abe I, Saito S. Increased tumor tissue pressure in association with the growth of rat tumors. Jpn. J. Cancer Res 1986;77(1):65-73.
Mohammadi M, Chen P. Effect of microvascular distribution and its density on interstitial fluid pressure in solid tumors: A computational model. Microvasc Res 2015;101:26-32.
Wang Y-D, Wu P, Mao J-D, Huang H, Zhang F. Relationship between vascular invasion and microvessel density and micrometastasis. World J Gastroenterol: WJG 2007;13(46):6269-6273.
Regge D, Cappello G, Pisacane A. Mechanism of tumour dissemination in hepatobiliary and pancreatic tumours. Hepatobiliary and pancreatic cancer. Cham: Springer; 2018. p 1-12.
Just N. Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer 2014;111(12):2205-2213.
Suo S, Zhang K, Cao M, et al. Characterization of breast masses as benign or malignant at 3.0 T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient. J Magn Reson Imaging 2016;43(4):894-902.
Zhu YJ, Feng B, Wang S, et al. Model-based three-dimensional texture analysis of contrast-enhanced magnetic resonance imaging as a potential tool for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Oncol Lett 2019;18(1):720-732.