Feasibility and performance of novel software to quantify metabolically active volumes and 3D partial volume corrected SUV and metabolic volumetric products of spinal bone marrow metastases on 18F-FDG-PET/CT.

Our aim was to assess feasibility and performance of novel semi-automated image analysis software called ROVER to quantify metabolically active volume (MAV), maximum standardized uptake value-maximum (SUV(max)), 3D partial volume corrected mean SUV (cSUV(mean)), and 3D partial volume corrected mean MVP (cMVP(mean)) of spinal bone marrow metastases on fluorine-18 fluorodeoxyglucose-positron emission tomography/computerized tomography ((18)F-FDG-PET/CT). We retrospectively studied 16 subjects with 31 spinal metastases on FDG-PET/CT and MRI. Manual and ROVER determinations of lesional MAV and SUV(max), and repeated ROVER measurements of MAV, SUV(max), cSUV(mean) and cMVP(mean) were made. Bland-Altman and correlation analyses were performed to assess reproducibility and agreement. Our results showed that analyses of repeated ROVER measurements revealed MAV mean difference (D)=-0.03±0.53cc (95% CI(-0.22, 0.16)), lower limit of agreement (LLOA)=-1.07cc, and upper limit of agreement (ULOA)=1.01cc; SUV(max) D=0.00±0.00 with LOAs=0.00; cSUV(mean) D=-0.01±0.39 (95% CI(-0.15, 0.13)), LLOA=-0.76, and ULOA=0.75; cMVP(mean) D=-0.52±4.78cc (95% CI(-2.23, 1.23)), LLOA=-9.89cc, and ULOA=8.86cc. Comparisons between ROVER and manual measurements revealed volume D= -0.39±1.37cc (95% CI (-0.89, 0.11)), LLOA=-3.08cc, and ULOA=2.30cc; SUV(max) D=0.00±0.00 with LOAs=0.00. Mean percent increase in lesional SUV(mean) and MVP(mean) following partial volume correction using ROVER was 84.25±36.00% and 84.45±35.94% , respectively. In conclusion, it is feasible to estimate MAV, SUV(max), cSUV(mean), and cMVP(mean) of spinal bone marrow metastases from (18)F-FDG-PET/CT quickly and easily with good reproducibility via ROVER software. Partial volume correction is imperative, as uncorrected SUV(mean) and MVP(mean) are significantly underestimated, even for large lesions. This novel approach has great potential for practical, accurate, and precise combined structural-functional PET quantification of disease before and after therapeutic intervention.

[1]  S C Huang,et al.  Anatomy of SUV. Standardized uptake value. , 2000, Nuclear medicine and biology.

[2]  I. Buvat,et al.  Partial-Volume Effect in PET Tumor Imaging* , 2007, Journal of Nuclear Medicine.

[3]  S M Larson,et al.  Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding , 1997, Cancer.

[4]  A. Alavi,et al.  Quantitative analysis of PET and MRI data in normal aging and Alzheimer's disease: atrophy weighted total brain metabolism and absolute whole brain metabolism as reliable discriminators. , 1993, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[5]  Suleman Surti,et al.  A recovery coefficient method for partial volume correction of PET images , 2009, Annals of nuclear medicine.

[6]  F Hofheinz,et al.  Effects of cold sphere walls in PET phantom measurements on the volume reproducing threshold , 2010, Physics in medicine and biology.

[7]  S. Larson,et al.  Sequential preoperative fluorodeoxyglucose-positron emission tomography assessment of response to preoperative chemoradiation: a means for determining longterm outcomes of rectal cancer. , 2004, Journal of the American College of Surgeons.

[8]  D. Altman,et al.  Measuring agreement in method comparison studies , 1999, Statistical methods in medical research.

[9]  Abass Alavi,et al.  Feasibility of Automated Partial-Volume Correction of SUVs in Current PET/CT Scanners: Can Manufacturers Provide Integrated, Ready-to-Use Software? , 2008, Journal of Nuclear Medicine.

[10]  Michael E Phelps,et al.  Combined Assessment of Metabolic and Volumetric Changes for Assessment of Tumor Response in Patients with Soft-Tissue Sarcomas , 2008, Journal of Nuclear Medicine.

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

[12]  A. Alavi,et al.  Positron emission tomography in aging and dementia: effect of cerebral atrophy. , 1987, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[13]  Jerry L Prince,et al.  Measurement of Radiotracer Concentration in Brain Gray Matter Using Positron Emission Tomography: MRI-Based Correction for Partial Volume Effects , 1992, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[14]  Roslyn J. Francis,et al.  Early Prediction of Response to Chemotherapy and Survival in Malignant Pleural Mesothelioma Using a Novel Semiautomated 3-Dimensional Volume-Based Analysis of Serial 18F-FDG PET Scans , 2007, Journal of Nuclear Medicine.

[15]  John L. Humm,et al.  Tumor Treatment Response Based on Visual and Quantitative Changes in Global Tumor Glycolysis Using PET-FDG Imaging. The Visual Response Score and the Change in Total Lesion Glycolysis. , 1999, Clinical positron imaging : official journal of the Institute for Clinical P.E.T.

[16]  A. Alavi,et al.  Use of a corrected standardized uptake value based on the lesion size on CT permits accurate characterization of lung nodules on FDG-PET , 2002, European Journal of Nuclear Medicine and Molecular Imaging.

[17]  Larson,et al.  Tumor Burden Assessment with Positron Emission Tomography with , 2000, Clinical positron imaging : official journal of the Institute for Clinical P.E.T.

[18]  C. Svarer,et al.  Integrated software for the analysis of brain PET/SPECT studies with partial-volume-effect correction. , 2004, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[19]  Abass Alavi,et al.  Quantitative assessment of the atherosclerotic burden of the aorta by combined FDG-PET and CT image analysis: a new concept. , 2006, Nuclear medicine and biology.

[20]  Habib Zaidi,et al.  Novel quantitative techniques for assessing regional and global function and structure based on modern imaging modalities: implications for normal variation, aging and diseased states. , 2007, Seminars in nuclear medicine.

[21]  A. Evans,et al.  Correction for partial volume effects in PET: principle and validation. , 1998, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[22]  D Visvikis,et al.  A multiresolution image based approach for correction of partial volume effects in emission tomography , 2006, Physics in medicine and biology.

[23]  E. Hoffman,et al.  Quantitation in Positron Emission Computed Tomography: 1. Effect of Object Size , 1979, Journal of computer assisted tomography.

[24]  A. Alavi,et al.  Evolving concept of imaging bone marrow metastasis in the twenty-first century: critical role of FDG-PET , 2008, European Journal of Nuclear Medicine and Molecular Imaging.

[25]  Abass Alavi,et al.  Functional Imaging of Cancer with Emphasis on Molecular Techniques , 2007, CA: a cancer journal for clinicians.

[26]  A. Alavi,et al.  Bone marrow and not bone is the primary site for skeletal metastasis: critical role of [18F]fluorodeoxyglucose positron emission tomography in this setting. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[27]  A. Alavi,et al.  Determination of whole-body metabolic burden as a quantitative measure of disease activity in lymphoma: a novel approach with fluorodeoxyglucose-PET , 2008, Nuclear medicine communications.

[28]  Abass Alavi,et al.  PET: a revolution in medical imaging. , 2004, Radiologic clinics of North America.