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Acad Radiol. 2015 Jul;22(7):840-5. doi: 10.1016/j.acra.2015.03.001. Epub 2015 Apr 8.

Multimodality 3D Tumor Segmentation in HCC Patients Treated with TACE.

Author information

1
Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Sheikh Zayed Tower, Ste 7203, 1800 Orleans St, Baltimore, MD 21287; Interventional Radiology Department, Chinese PLA General Hospital, Beijing, China.
2
Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Sheikh Zayed Tower, Ste 7203, 1800 Orleans St, Baltimore, MD 21287.
3
Division of Vascular and Interventional Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Sheikh Zayed Tower, Ste 7203, 1800 Orleans St, Baltimore, MD 21287. Electronic address: jfg@jhmi.edu.
4
U/S Imaging and Interventions (UII), Philips Research North America, Briarcliff Manor, New York.

Abstract

RATIONALE AND OBJECTIVES:

To validate the concordance of a semiautomated multimodality lesion segmentation technique between contrast-enhanced magnetic resonance imaging (CE-MRI), cone-beam computed tomography (CBCT), and multidetector CT (MDCT) in patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE).

MATERIALS AND METHODS:

This retrospective analysis included 45 patients with unresectable HCC who underwent baseline CE-MRI within 1 month before the treatment, intraprocedural CBCT during conventional TACE, and MDCT within 24 hours after TACE. Fourteen patients were excluded because of atypical lesion morphology, portal vein invasion, or small lesion size which precluded sufficient lesion visualization. Thirty-one patients with a total of 40 target lesions were included into the analysis. A tumor segmentation software, based on non-Euclidean geometry and theory of radial basis functions, was used to allow for the segmentation of target lesions in 3D on all three modalities. The algorithm created image-based masks located in a 3D region whose center and size were defined by the user, yielding the nomenclature "semiautomatic". On the basis of that, tumor volumes on all three modalities were calculated and compared using a linear regression model (R(2) values). Residual plots were used to analyze drift and variance of the values.

RESULTS:

The mean value of tumor volumes was 18.72 ± 19.13 cm(3) (range, 0.41-59.16 cm(3)) on CE-MRI, 21.26 ± 21.99 cm(3) (range, 0.62-86.82 cm(3)) on CBCT, and 19.88 ± 20.88 cm(3) (range, 0.45-75.24 cm(3)) on MDCT. The average volumes of the tumor were not significantly different between CE-MRI and DP-CBCT, DP-CBCT and MDCT, MDCT and CE-MRI (P = .577, .770, and .794, respectively). A strong correlation between volumes on CE-MRI and CBCT, CBCT and MDCT, MDCT and CE-MRI was observed (R(2) = 0.974, 0.992 and 0.983, respectively). When plotting the residuals, no drift was observed for all methods showing deviations of no >10% of absolute volumes (in cm(3)).

CONCLUSIONS:

A semiautomated 3D segmentation of HCC lesions treated with TACE provides high volumetric concordance across all tested imaging modalities.

KEYWORDS:

C-arm cone-beam CT; MDCT; MRI; TACE; Tumor segmentation; hepatocellular carcinoma

PMID:
25863795
PMCID:
PMC4464945
DOI:
10.1016/j.acra.2015.03.001
[Indexed for MEDLINE]
Free PMC Article
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