Comparison of semi-automatic volumetric VX2 hepatic tumor segmentation from cone beam CT and multi-detector CT with histology in rabbit models.

RATIONALE AND OBJECTIVES The purpose of this study was to compare tumor volume in a VX2 rabbit model as calculated using semiautomatic tumor segmentation from C-arm cone-beam computed tomography (CBCT) and multidetector computed tomography (MDCT) to the actual tumor volume. MATERIALS AND METHODS Twenty VX2 tumors in 20 adult male New Zealand rabbits (one tumor per rabbit) were imaged with CBCT (using an intra-arterial contrast medium injection) and MDCT (using an intravenous contrast injection). All tumor volumes were measured using semiautomatic three-dimensional volumetric segmentation software. The software uses a region-growing method using non-Euclidean radial basis functions. After imaging, the tumors were excised for pathologic volume measurement. The imaging-based tumor volume measurements were compared to the pathologic volumes using linear regression, with Pearson's test, and correlated using Bland-Altman analysis. RESULTS Average tumor volumes were 3.5 ± 1.6 cm(3) (range, 1.4-7.2 cm(3)) on pathology, 3.8 ± 1.6 cm(3) (range, 1.3-7.3 cm(3)) on CBCT, and 3.9 ± 1.6 (range, 1.8-7.5 cm(3)) on MDCT (P < .001). A strong correlation between volumes on pathology and CBCT and also with MDCT was observed (Pearson's correlation coefficient = 0.993 and 0.996, P < .001, for CBCT and MDCT, respectively). Bland-Altman analysis showed that MDCT tended to overestimate tumor volume, and there was stronger agreement for tumor volume between CBCT and pathology than with MDCT, possibly because of the intra-arterial contrast injection. CONCLUSIONS Tumor volume as measured using semiautomatic tumor segmentation software showed a strong correlation with the "real volume" measured on pathology. The segmentation software on CBCT and MDCT can be a useful tool for volumetric hepatic tumor assessment.

[1]  L. Schwartz,et al.  New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). , 2009, European journal of cancer.

[2]  J. Geschwind,et al.  Percutaneous US-guided implantation of Vx-2 carcinoma into rabbit liver: a comparison with open surgical method. , 2009, The Journal of surgical research.

[3]  Lewis B. Sheiner,et al.  Some suggestions for measuring predictive performance , 1981, Journal of Pharmacokinetics and Biopharmaceutics.

[4]  Michael Sühling,et al.  Semi-automated measurement of hyperdense, hypodense and heterogeneous hepatic metastasis on standard MDCT slices. Comparison of semi-automated and manual measurement of RECIST and WHO criteria , 2008, European Radiology.

[5]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[6]  Binsheng Zhao,et al.  Shape-Constraint Region Growing for Delineation of Hepatic Metastases on Contrast-Enhanced Computed Tomograph Scans , 2006, Investigative radiology.

[7]  J. Han,et al.  Hepatocellular carcinoma: evaluation of therapeutic response to interventional procedures , 2002, Abdominal Imaging.

[8]  L. Schumm,et al.  Tumor size on computed tomography scans , 2001, Cancer.

[9]  E. Halpern,et al.  CT tumor measurement for therapeutic response assessment: comparison of unidimensional, bidimensional, and volumetric techniques initial observations. , 2002, Radiology.

[10]  A. Sundin,et al.  Interobserver and intraobserver variability in the response evaluation of cancer therapy according to RECIST and WHO-criteria , 2010, Acta oncologica.

[11]  Wei Xiong,et al.  Liver tumour segmentation using contrast-enhanced multi-detector CT data: performance benchmarking of three semiautomated methods , 2010, European Radiology.

[12]  Sohee Park,et al.  The validity of tumour diameter assessed by magnetic resonance imaging and gross specimen with regard to tumour volume in cervical cancer patients. , 2008, European journal of cancer.

[13]  Pamela Ohman-Strickland,et al.  Volumetric Analysis of Liver Metastases in Computed Tomography With the Fuzzy C-Means Algorithm , 2006, Journal of computer assisted tomography.

[14]  Laurel Beckett,et al.  Comparison of two-dimensional and three-dimensional iterative watershed segmentation methods in hepatic tumor volumetrics. , 2008, Medical physics.

[15]  W Schlegel,et al.  Usability of semiautomatic segmentation algorithms for tumor volume determination. , 1999, Investigative radiology.

[16]  K D Hopper,et al.  The impact of 2D versus 3D quantitation of tumor bulk determination on current methods of assessing response to treatment. , 1996, Journal of computer assisted tomography.

[17]  A. Beckett,et al.  AKUFO AND IBARAPA. , 1965, Lancet.

[18]  Jean-Philippe Thiran,et al.  Non-Euclidean image-adaptive Radial Basis Functions for 3D interactive segmentation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[19]  Alessandro Radaelli,et al.  Comparing the Detectability of Hepatocellular Carcinoma by C-Arm Dual-Phase Cone-Beam Computed Tomography During Hepatic Arteriography With Conventional Contrast-Enhanced Magnetic Resonance Imaging , 2012, CardioVascular and Interventional Radiology.