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Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.

ABSTRACT: Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.

SUBMITTER: Raunig DL

PROVIDER: S-EPMC9825667 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.

Raunig David L DL Pennello Gene A GA Delfino Jana G JG Buckler Andrew J AJ Hall Timothy J TJ Guimaraes Alexander R AR Wang Xiaofeng X Huang Erich P EP Barnhart Huiman X HX deSouza Nandita N Obuchowski Nancy N

Academic radiology 20221201 2

Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs a ...[more]

PMID: 36464548

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Project description:Purpose To develop multiparametric magnetic resonance (MR) imaging models to generate a quantitative, user-independent, voxel-wise composite biomarker score (CBS) for detection of prostate cancer by using coregistered correlative histopathologic results, and to compare performance of CBS-based detection with that of single quantitative MR imaging parameters. Materials and Methods Institutional review board approval and informed consent were obtained. Patients with a diagnosis of prostate cancer underwent multiparametric MR imaging before surgery for treatment. All MR imaging voxels in the prostate were classified as cancer or noncancer on the basis of coregistered histopathologic data. Predictive models were developed by using more than one quantitative MR imaging parameter to generate CBS maps. Model development and evaluation of quantitative MR imaging parameters and CBS were performed separately for the peripheral zone and the whole gland. Model accuracy was evaluated by using the area under the receiver operating characteristic curve (AUC), and confidence intervals were calculated with the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for the multiparametric model and the single best-performing quantitative MR imaging parameter at the individual level and in aggregate. Results Quantitative T2, apparent diffusion coefficient (ADC), volume transfer constant (K(trans)), reflux rate constant (kep), and area under the gadolinium concentration curve at 90 seconds (AUGC90) were significantly different between cancer and noncancer voxels (P < .001), with ADC showing the best accuracy (peripheral zone AUC, 0.82; whole gland AUC, 0.74). Four-parameter models demonstrated the best performance in both the peripheral zone (AUC, 0.85; P = .010 vs ADC alone) and whole gland (AUC, 0.77; P = .043 vs ADC alone). Individual-level analysis showed statistically significant improvement in AUC in 82% (23 of 28) and 71% (24 of 34) of patients with peripheral-zone and whole-gland models, respectively, compared with ADC alone. Model-based CBS maps for cancer detection showed improved visualization of cancer location and extent. Conclusion Quantitative multiparametric MR imaging models developed by using coregistered correlative histopathologic data yielded a voxel-wise CBS that outperformed single quantitative MR imaging parameters for detection of prostate cancer, especially when the models were assessed at the individual level. (©) RSNA, 2016 Online supplemental material is available for this article.

Project description:ObjectivesIn human chronic kidney disease (CKD) the extent of renal tubulointerstitial fibrosis correlates with progressive loss of renal function. However, fibrosis can so far only be assessed by histology of kidney biopsies. Magnetic resonance imaging (MRI) can provide information about tissue architecture, but its potential to assess fibrosis and inflammation in diseased kidneys remains poorly defined.Materials and methodsWe evaluated excised kidneys in a murine adenine-induced nephropathy model for CKD by MRI and correlated quantitative MRI parameters (T1, T2, and T2* relaxation times, apparent diffusion coefficient and fractional anisotropy) with histological hallmarks of progressive CKD, including renal fibrosis, inflammation, and microvascular rarefaction. Furthermore, we analyzed the effects of paraformaldehyde fixation on MRI parameters by comparing kidney samples before and after fixation with paraformaldehyde.ResultsIn diseased kidneys T2 and T2* relaxation times, apparent diffusion coefficient and fractional anisotropy in the renal cortex and/or outer medulla were significantly different from those in control kidneys. In particular, T2 relaxation time was the best parameter to distinguish control and CKD groups and correlated very well with the extent of fibrosis, inflammatory infiltrates, tubular dilation, crystal deposition, and loss of peritubular capillaries and normal tubules in the renal cortex and outer medulla. Fixation with paraformaldehyde had no impact on T2 relaxation time and fractional anisotropy, whereas T1 times significantly decreased and T2* times and apparent diffusion coefficients increased in fixed kidney tissue.ConclusionsMRI parameters provide a promising approach to quantitatively assess renal fibrosis and inflammation in CKD. Especially T2 relaxation time correlates well with histological features of CKD and is not influenced by paraformaldehyde fixation of kidney samples. Thus, T2 relaxation time might be a candidate parameter for non-invasive assessment of renal fibrosis in human patients.

Dataset Information

Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.

Publications

Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.

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