Role of baseline volumetric functional MRI in predicting histopathologic grade and patients' survival in hepatocellular carcinoma.


Journal

European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774

Informations de publication

Date de publication:
Jul 2020
Historique:
received: 26 09 2019
accepted: 12 02 2020
revised: 06 02 2020
pubmed: 8 3 2020
medline: 15 12 2020
entrez: 8 3 2020
Statut: ppublish

Résumé

We aimed to evaluate the role of volumetric ADC (vADC) and volumetric venous enhancement (vVE) in predicting the grade of tumor differentiation in hepatocellular carcinoma (HCC). The study population included 136 HCC patients (188 lesions) who had baseline MR imaging and histopathological report. Measurements of vVE and vADC were performed on baseline MRI. Tumors were histologically classified into low-grade and high-grade groups. The parameters between the two groups were compared using Mann-Whitney U and chi-square tests for continuous and categorical parameters, respectively. Area under receiver operating characteristic (AUROC) was calculated to investigate the accuracy of vADC and vVE. Logistic regression and multivariable Cox regression were used to unveil the potential parameters associated with high-grade HCC and patient's survival, respectively. Lesions with higher vADC values and a higher absolute vADC skewness were more likely to be high grade on histopathology assessment (p = 0.001 and p = 0.0291, respectively). Also, vVE showed a trend to be higher in low-grade lesions (p = 0.079). Adjusted multivariable model including vADC, vVE, and vADC skewness could strongly predict HCC degree of differentiation (AUROC = 83%). Additionally, a higher Child-Pugh score (HR = 2.39 [p = 0.02] for score 2 and HR = 3.47 [p = 0.001] for score 3), vADC skewness (HR = 1.52, p = 0.02; per increments in skewness), and tumor volume (HR = 1.1, p = 0.001; per 100 cm vADC and vVE have the potential to accurately predict HCC differentiation. Additionally, some imaging features in combination with patients' clinical characteristics can predict patient survival. • Volumetric functional MRI metrics can be considered as non-invasive measures for determining tumor histopathology in HCC. • Estimating patient survival based on clinical and imaging parameters can be used for modifying management approach and preventing unnecessary adverse events.

Identifiants

pubmed: 32144459
doi: 10.1007/s00330-020-06742-8
pii: 10.1007/s00330-020-06742-8
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

3748-3758

Auteurs

Sanaz Ameli (S)

Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA.

Mohammadreza Shaghaghi (M)

Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA.

Mounes Aliyari Ghasabeh (M)

Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA.

Pallavi Pandey (P)

Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA.

Bita Hazhirkarzar (B)

Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA.

Maryam Ghadimi (M)

Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA.

Roya Rezvani Habibabadi (R)

Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA.

Pegah Khoshpouri (P)

Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA.

Ankur Pandey (A)

Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA.

Robert A Anders (RA)

Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA.

Ihab R Kamel (IR)

Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Baltimore, MD, USA. ikamel@jhmi.edu.
School of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA. ikamel@jhmi.edu.

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