Multiparameter quantitative imaging incorporates anatomical, functional, and/or behavioral
biomarkers to characterize tissue, detect disease, identify phenotypes, define longitudinal
change, or predict outcome. Multiple imaging parameters are sometimes considered separately
but ideally are evaluated collectively. Often, they are transformed as Likert interpretations,
ignoring the correlations of quantitative properties that may result in better reproducibility
or outcome prediction. In this paper we present three use cases of multiparameter
quantitative imaging: i) multidimensional descriptor, ii) phenotype classification,
and iii) risk prediction. A fourth application based on data-driven markers from radiomics
is also presented. We describe the technical performance characteristics and their
metrics common to all use cases, and provide a structure for the development, estimation,
and testing of multiparameter quantitative imaging. This paper serves as an overview
for a series of individual articles on the four applications, providing the statistical
framework for multiparameter imaging applications in medicine.
Key Words
Abbreviations:
QIBA (Quantitative Imaging Biomarker Alliance), QIB (Quantitative Imaging Biomarker)To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Academic RadiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- The Emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions.SMMR. 2015; 24: 9-f26
- Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment.SMMR. 2015; 24: 27-67
- Quantitative imaging biomarkers: a review of statistical methods for computer algorithm comparisons.SMMR. 2015; 24: 68-106
- Meta-analysis of the technical performance of an imaging procedure: guidelines and statistical methodology.SMMR. 2015; 24: 141-174
Raunig et al. Multidimensional quantitative imaging biomarkers as a multivariate descriptor of health. Acad Radiol.
Delfino et al. Multiparametric quantitative imaging biomarkers in phenotype classification. Acad Radiol.
Huang et al. Multiparametric quantitative imaging biomarkers in risk prediction: recommendations for data acquisition, technical performance assessment, and model development and validation. Acad Radiol.
Wang et al. Multiparametric data-driven imaging markers: guidelines for development, application and reporting of model outputs in radiomics. Acad Radiol.
- In vivo real-time freehand palpation imaging.Ultrason Med Biol. 2003; 29: 427-435
- Suspicious breast lesions: assessment of 3D Doppler US indexes for classification in a test population and fourfold cross-validation scheme.Radiology. 2008; 249: 463-470
- Frequency dependence of ultrasound attenuation and backscatter in breast tissue.Ultrason Med Biol. 1986; 12: 795-808
- Quantitative assessment of in vivo breast masses using ultrasound attenuation and backscatter.Ultrason Imaging. 2013; 35: 146-161
- Advanced spectral analysis methods for quantification of coherent ultrasound scattering: Applications in the breast.The University of Wisconsin-Madison;, 2014 (Doctoral dissertation)
- Correlation of computed tomography and positron emission tomography in patients with metastatic gastrointestinal stromal tumor treated at a single institution with imatinib mesylate: proposal of new computed tomography response criteria.J Clin Oncol. 2007; 25: 1753-1759
- Atherosclerotic plaque tissue: noninvasive quantitative assessment of characteristics with software-aided measurements from conventional CT angiography.Radiology. 2018; 286: 622-631
- Quantitative assessment of carotid plaque morphology (geometry and tissue composition) using computed tomography angiography.J Vasc Surg. 2019; 70: 858-868
- Evolution of translational omics: lessons learned and the path forward.The National Academies Press, Washington, DC:2012 (Report Brief)
- Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases.Nature Scientific Reports. 2015; 5: 1-15
- Breast DCE-MRI: lesion classification using dynamic and morphological features by means of a multiple classifier system.Eur Radiol Exp. 2017; 1: 10
- Elastography assessment of liver fibrosis: society of radiologists in ultrasound consensus conference statement.Radiology. 2015; 276: 845-861
- Multiparameter magnetic resonance imaging for quantitation of liver disease: a two-centre cross-sectional observational study.Sci Rep. 2018; 15: 8
- BEST (Biomarkers, EndpointS, and other Tools) Resource.Food and Drug Administration (US), Silver Spring, MD2016 (Available at:) (Accessed September 22, 2022)
- Does the application of diffusion weighted imaging improve the prediction of survival in patients with resected brain metastases? A retrospective multicenter study.Cancer Imaging. 2020; 20: 16
- Radiomics: images are more than pictures, they are data.Radiology. 2016; 278: 563-577
- Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically-driven quantitative biomarkers.Eur Radiology. 2020; 31: 6001-6012
- A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study.Lancet Oncol. 2018; 19: 1180-1191
- Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer.PLoS One. 2017; 12
- Radiomics in pulmonary lesion imaging.Am J Roentgenol. 2019; 212: 497-504
- Filtering for increased power for microarray data analysis.BMC Bioinformatics. 2009; 10: 1471-2105
- Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives.Korean J Radiol. 2019; 20: 1124-1137
- Linear combinations of multiple diagnostic markers.JASA. 1993; 88: 1350-1355
- Combining several screening tests: optimality of the risk score.Biometrics. 2002; 58: 657-664
- On linear combinations of biomarkers to improve diagnostic accuracy.Stat Med. 2005; 24: 37-47
- Combing predictors for classification using the area under the receiver operating characteristic curve.Biometrics. 2006; 62: 221-229
- Calibration of prognostic risk score.Wiley StatsRef: Statistics Reference Online. 2018; : 1-10
- Integrating the predictiveness of a marker with its performance as a classifier.Am J Epidemiol. 2008; 167: 362
- The optimal linear combination of multiple predictors under the generalized linear models.Stat Probab Lett. 2009; 79: 2321-2327
- Linear combination methods to improve diagnostic/prognostic accuracy on future observations.SMMR. 2013; 25: 1359-1380
- The linear combinations of biomarkers which maximize the partial area under the ROC curves.Comput Stat. 2013; 28: 647-666
- On the use of min-max combination of biomarkers to maximize the partial area under the ROC curve.Probab Stat. 2019; 2019: 1-13
- Methods for evaluating prediction performance of biomarkers and tests. Risk assessment and evaluation of predictions.Lecture Notes in Statistics. Springer Science + Business Media, New York2013
- Testing for improvement in prediction model performance.Stat Med. 2013; 32: 1467-1482
- Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.Stat Med. 2008; 27 (With comments by Pepe M, Feng Z, Gu J.): 157-172
- Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.Stat Med. 2011; 30: 11-21
- A parameter ROC model-based approach for evaluating the predictiveness of continuous markers in case-control studies.Biometrics. 2009; 65: 1133-1144
- Expression of Measurement Uncertainty in Laboratory Medicine.Clinical and Laboratory Standards Institute Wayne, PA:2012
Standardization, I.O.f., Guidance for the use of repeatability, reproducibility and trueness estimates in measurement uncertainty estimation, in (ISO Standard No 21748:2017(E)). Geneva, Switzerland; 2017.
Institute of Medicine, Board on Heath Care Services, Board on Health Sciences Policy, Committee on the review of omics-based tests for predicting patient outcomes in clinical trials. Evolution of Translational Omics: Lessons learned and the path forward. National Academies Press, Washington DC: 2012.
- Phases of biomarker development for early detection of cancer.J Natl Cancer Inst. 2001; 93: 1054-1061
- Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design.J Natl Cancer Inst. 2008; 100: 1432-1438
- The Elements of Statistical Learning.2nd edition. Springer Science+Business Media, New York2008
- The efficacy of diagnostic imaging.Med Decis Making. 1991; 11: 88-94
- Imaging biomarker roadmap for cancer studies.Nat Rev Clin Onc. 2017; 14: 169-186
Context of Use. Available at: https://www.fda.gov/drugs/biomarker-qualification-program/context-use. Accessed February 4, 2022.
- improving lung function in severe heterogenous emphysema with the spiration valve system (EMPROVE). A multicenter, open-label randomized controlled clinical trial.Am J of Respir Crit Care Med. 2019; 200: 1354-1362
- Use of archived specimens in evaluation of prognostic and predictive biomarkers.JNCI. 2009; 101: 1446-1452
- Applied predictive modeling.Springer, New York2013
- Uncertainty of measurements – Part 6: Developing and using measurement models.International Electrotechnical Commission (IEC), Switzerland2020 (ISO/IEC FDGuide 98-6:2020Available at:)
Dudoit S, Fridlyand J, Speed TP. Comparison of discrimination methods for the classification of tumors using gene expression data. JASA 2002; 97:77–87.
- Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nat Mach Intell. 2019; 1: 206-215
- What do we mean by validating a prognostic model?.Statist Med. 2000; 19: 453-473
- Randomised comparisons of medical tests: sometimes invalid, not always efficient.Lancet. 2000; 356: 1844-1847
- Beyond correlations, sensitivities, and specificities: a roadmap for demonstrating utility of advanced imaging in oncology treatment and clinical trial design.Acad Radiol. 2017; 24: 1036-1049
Article Info
Publication History
Published online: September 27, 2022
Accepted:
August 26,
2022
Received in revised form:
August 19,
2022
Received:
June 2,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
