Quality control within the multicentre perfusion CT study of primary colorectal cancer (PROSPeCT): results of an iodine density phantom study

Lewis, Maria; Beggs, Shaun; Bridges, Andrew J; Clewer, Philip; Davis, Alyson; Foy, Trevelyan; Fuller, Karen; George, Jennifer; Higginson, Antony; Honey, Ian; Iball, Gareth; Mutch, Steve; Neil, Shellagh; Rivett, C.; Slater, Andrew; Sutton, D.; Weir, Nick; Wayte, Sarah

doi:10.1007/s00330-014-3258-y

Cited by 5 publications

(5 citation statements)

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“…Few in-human IB studies report bias because real values might not be possible to ascertain in a clinical setting. For some IBs, bias can be estimated by comparison with reference phantoms, as is the case when validating biomarkers on the basis of CT Hounsfield units86, and MRI longitudinal (R 1 ) and transverse (R 2 ) relaxation rates87. Imaging phantoms, however, seldom fully represent technical performance in animals or humans.…”

Section: The Imaging Biomarker Roadmapmentioning

confidence: 99%

“…Clinical validation requires demonstration that the biomarker merely relates to a clinical variable and, therefore, is less difficult to establish than clinical utility (but also less meaningful)86. Clinical utility is achieved when the biomarker leads to net improvement of health outcomes or provides information useful for diagnosis, treatment, management, or prevention of a disease10,88.…”

Section: The Imaging Biomarker Roadmapmentioning

confidence: 99%

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Imaging biomarker roadmap for cancer studies

O’Connor

Aboagye

Adams³

et al. 2016

Nat Rev Clin Oncol

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Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing ‘translational gaps’ through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored ‘roadmap’. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.

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Section: The Imaging Biomarker Roadmapmentioning

confidence: 99%

Section: The Imaging Biomarker Roadmapmentioning

confidence: 99%

Imaging biomarker roadmap for cancer studies

O’Connor

Aboagye

Adams³

et al. 2016

Nat Rev Clin Oncol

Self Cite

881

708

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“…A phantom calibration method [42] developed to convert signal intensity to gadolinium concentration could not account for in-vivo signal variations due to B 1 /B 0 field inhomogeneity at high magnetic fields [43]. Contrarily, the linear relationship between iodinated contrast media and CT number [27] allows accurate measurement of contrast dynamics. In line with this notion, DCE-CT offers lower variability in measured tissue perfusion parameters than DCE-MRI [44].…”

Section: Discussionmentioning

confidence: 99%

“…A linear interpolation was used to equalize the distribution of sampled data points with an interval of 0.5 sec. The increase in CT signal from baseline was used to represent the concentration of iodine [27]. Due to motion artifacts in later scans, only time attenuation curves from 0 to 120 sec were used in the model fitting.…”

Section: Methodsmentioning

confidence: 99%

A modified two-compartment model for measurement of renal function using dynamic contrast-enhanced computed tomography

et al. 2019

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Objectives To validate and adapt a modified two-compartment model, originally developed for magnetic resonance imaging, for measuring human single-kidney glomerular filtration rate (GFR) and perfusion using dynamic contrast-enhanced computed tomography (DCE-CT). Methods This prospective study was approved by the institutional review board, and written informed consent was obtained from all patients. Thirty-eight patients with essential hypertension (EH, n = 13) or atherosclerotic renal artery stenosis (ARAS, n = 25) underwent renal DCE-CT for GFR and perfusion measurement using a modified two-compartment model. Iothalamate clearance was used to measure reference total GFR, which was apportioned into single-kidney GFR by renal blood flow. Renal perfusion was also calculated using a conventional deconvolution algorithm. Validation of GFR and perfusion and inter-observer reproducibility, were conducted by using the Pearson correlation and Bland-Altman analysis. Results Both the two-compartment model and iothalamate clearance detected in ARAS patients lower GFR in the stenotic compared to the contralateral and EH kidneys. GFRs measured by DCE-CT and iothalamate clearance showed a close match (r = 0.94, P<0.001, and mean difference 2.5±12.2mL/min). Inter-observer bias and variation in model-derived GFR (r = 0.97, P<0.001; mean difference, 0.3±7.7mL/min) were minimal. Renal perfusion by deconvolution agreed well with that by the compartment model when the blood transit delay from abdominal aorta to kidney was negligible. Conclusion The proposed two-compartment model faithfully depicts contrast dynamics using DCE-CT and may provide a reliable tool for measuring human single-kidney GFR and perfusion.

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“…Therefore, iodinated contrast material is potentially a good tracer for measuring GFR, which, in this context, is equivalent to the renal clearance of the contrast material. Similarly, the multidetector CT scanner is a reliable tool for investigating renal filtration of the iodinated contrast material because of a good linear relationship between CT attenuation value and its concentration (11,12). The renal clearance of the contrast material, by definition, can be determined by dividing the amount of the contrast material filtration by its plasma concentration.…”

Section: Discussionmentioning

confidence: 99%

Determination of Glomerular Filtration Rate with CT Measurement of Renal Clearance of Iodinated Contrast Material versus ^99mTc-DTPA Dynamic Imaging “Gates” Method: A Validation Study in Asymmetrical Renal Disease

Yuan¹,

Zhang²,

Tang³

et al. 2017

Radiology

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Purpose To validate a computed tomographic (CT) glomerular filtration rate (GFR) measurement and compare it with renal dynamic imaging GFR obtained by using the "Gates" method, with dual plasma sampling technetium 99m (Tc) diethylenetriaminepenta-acetic acid (DTPA) clearance ("true GFR") as the reference standard. Materials and Methods This prospective study was approved by the institutional review board, and written informed consent was obtained from all patients. Forty-two patients with unilateral renal disease were included. Single-kidney CT GFR was calculated as excretory phase whole-kidney CT number enhancement divided by the area under the time-attenuation curve for the aorta, multiplied by (1 - hematocrit level). The CT GFR was then obtained by summing the result of the two sides. The true GFR and the Gates GFR were measured by using a single injection of Tc-DTPA. The CT GFR and Gates GFR were respectively compared with the true GFR by using a paired t test and linear regression analysis. Results The difference between CT GFR (mean ± standard deviation, 96.02 mL/min ± 23.11) and true GFR (90.50 mL/min ± 21.46) was 5.51 mL/min ± 6.96 (P< .001), demonstrating 6.09% systemic overestimation. The difference between Gates GFR (93.93 mL/min ± 26.97) and true GFR was 3.42 mL/min ± 16.10 (P = .176). Linear regression findings confirmed the association between CT GFR (y-axis) and true GFR (x-axis) and between Gates GFR (y-axis) and true GFR (x-axis) (P < .001 for both). Both regression lines paralleled the diagonal (intercept = 0 and slope = 1) (P = .599 and P = .945, respectively). The 95% confidence interval of the former was above the diagonal, confirming the systemic overestimation. The standard deviations of residuals of both linear regressions were 7.02 mL/min and 16.30 mL/min, respectively, demonstrating smaller deviation of the CT GFR (P < .001). Conclusion The proposed CT GFR measurement was validated in this study and was proved to be more accurate than the Gates method despite slight (6.09%) systemic overestimation. RSNA, 2016 Online supplemental material is available for this article.

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Quality control within the multicentre perfusion CT study of primary colorectal cancer (PROSPeCT): results of an iodine density phantom study

Abstract: Quality control is essential in a multicentre setting to enable CT quantification. CNRs in a body-sized phantom had the recommended value of at least 1.5. CTDIs and DLPs varied by factors of 1.8 and 2.4 respectively.

Cited by 5 publications

References 10 publications

Imaging biomarker roadmap for cancer studies

Imaging biomarker roadmap for cancer studies

A modified two-compartment model for measurement of renal function using dynamic contrast-enhanced computed tomography

Determination of Glomerular Filtration Rate with CT Measurement of Renal Clearance of Iodinated Contrast Material versus ^99mTc-DTPA Dynamic Imaging “Gates” Method: A Validation Study in Asymmetrical Renal Disease

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Quality control within the multicentre perfusion CT study of primary colorectal cancer (PROSPeCT): results of an iodine density phantom study

Abstract: Quality control is essential in a multicentre setting to enable CT quantification. CNRs in a body-sized phantom had the recommended value of at least 1.5. CTDIs and DLPs varied by factors of 1.8 and 2.4 respectively.

Cited by 5 publications

References 10 publications

Imaging biomarker roadmap for cancer studies

Imaging biomarker roadmap for cancer studies

A modified two-compartment model for measurement of renal function using dynamic contrast-enhanced computed tomography

Determination of Glomerular Filtration Rate with CT Measurement of Renal Clearance of Iodinated Contrast Material versus 99mTc-DTPA Dynamic Imaging “Gates” Method: A Validation Study in Asymmetrical Renal Disease

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Product

Resources

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Determination of Glomerular Filtration Rate with CT Measurement of Renal Clearance of Iodinated Contrast Material versus ^99mTc-DTPA Dynamic Imaging “Gates” Method: A Validation Study in Asymmetrical Renal Disease