Methods and Challenges in Quantitative Imaging Biomarker Development

Abramson, Richard G.; Burton, Kirsteen R.; Yu, John‐Paul J.; Scalzetti, Ernest M.; Yankeelov, Thomas E.; Rosenkrantz, Andrew B.; Mendiratta‐Lala, Mishal; Bartholmai, Brian J.; Ganeshan, Dhakshina Moorthy; Lenchik, Leon; Subramaniam, Rathan M.

doi:10.1016/j.acra.2014.09.001

Cited by 85 publications

(72 citation statements)

References 78 publications

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“…(2) and (3) into Eq. (1) shows that the precision ω k is equal to the standard deviation term ðσ k Þ. Intuitively also, as illustrated in Fig.…”

Section: No-gold-standard Evaluation Technique: Theorymentioning

confidence: 79%

“…However, having the search space too large would increase the possibility of the optimization routine being trapped in local minima. Based on these considerations, the search ranges for the α and β parameters were chosen to lie in [1,20]. This allowed modeling a wide variety of shapes of the FPBD distribution, as described previously.…”

Section: Application Of the No-gold-standard Techniquementioning

confidence: 99%

“…Similarly, the maximum measured MTV values were between 90 and 170 cc. Using this information, the search ranges for the upper and lower value of the GBD were set to [2.4, 3.0] cc and [90, 170] cc, respectively, and the search ranges for the bias and standard deviation of the noise terms were set to [−10; 10] cc and [1,10] cc, respectively.…”

Section: Application Of the No-gold-standard Techniquementioning

confidence: 99%

“…Quantitative imaging, i.e., the measurement and use of numerical or statistical features from medical images to facilitate clinical decision making, 1 is finding applications in many diagnostic and therapeutic procedures. A particularly important application is quantitative imaging biomarkers (QIBs).…”

Section: Introductionmentioning

confidence: 99%

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Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

et al. 2017

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Abstract. Recently, a class of no-gold-standard (NGS) techniques have been proposed to evaluate quantitative imaging methods using patient data. These techniques provide figures of merit (FoMs) quantifying the precision of the estimated quantitative value without requiring repeated measurements and without requiring a gold standard. However, applying these techniques to patient data presents several practical difficulties including assessing the underlying assumptions, accounting for patient-sampling-related uncertainty, and assessing the reliability of the estimated FoMs. To address these issues, we propose statistical tests that provide confidence in the underlying assumptions and in the reliability of the estimated FoMs. Furthermore, the NGS technique is integrated within a bootstrap-based methodology to account for patient-sampling-related uncertainty. The developed NGS framework was applied to evaluate four methods for segmenting lesions from F-Fluoro-2-deoxyglucose positron emission tomography images of patients with head-and-neck cancer on the task of precisely measuring the metabolic tumor volume. The NGS technique consistently predicted the same segmentation method as the most precise method. The proposed framework provided confidence in these results, even when gold-standard data were not available. The bootstrap-based methodology indicated improved performance of the NGS technique with larger numbers of patient studies, as was expected, and yielded consistent results as long as data from more than 80 lesions were available for the analysis.

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“…(2) and (3) into Eq. (1) shows that the precision ω k is equal to the standard deviation term ðσ k Þ. Intuitively also, as illustrated in Fig.…”

Section: No-gold-standard Evaluation Technique: Theorymentioning

confidence: 79%

Section: Application Of the No-gold-standard Techniquementioning

confidence: 99%

Section: Application Of the No-gold-standard Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

et al. 2017

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“…This way, QI contributes to the radiological interpretation by assessing the degree of a given condition (Buckler et al, 2011; Abramson et al, 2015). QI has been adopted in clinical studies and trials to obtain more sensitive and precise endpoints.…”

Section: Introductionmentioning

confidence: 99%

Statistical characterization of noise for spatial standardization of CT scans: Enabling comparison with multiple kernels and doses

Vegas-Sánchez-Ferrero

Ledesma-Carbayo

Washko

et al. 2017

Medical Image Analysis

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Computerized tomography (CT) is a widely adopted modality for analyzing directly or indirectly functional, biological and morphological processes by means of the image characteristics. However, the potential utilization of the information obtained from CT images is often limited when considering the analysis of quantitative information involving different devices, acquisition protocols or reconstruction algorithms. Although CT scanners are calibrated as a part of the imaging workflow, the calibration is circumscribed to global reference values and does not circumvent problems that are inherent to the imaging modality. One of them is the lack of noise stationarity, which makes quantitative biomarkers extracted from the images less robust and stable. Some methodologies have been proposed for the assessment of non-stationary noise in reconstructed CT scans. However, those methods focused on the non-stationarity only due to the reconstruction geometry and are mainly based on the propagation of the variance of noise throughout the whole reconstruction process. Additionally, the philosophy followed in the state-of-the-art methods is based on the reduction of noise, but not in the standardization of it. This means that, even if the noise is reduced, the statistics of the signal remain non-stationary, which is insufficient to enable comparisons between different acquisitions with different statistical characteristics. In this work, we propose a statistical characterization of noise in reconstructed CT scans that leads to a versatile statistical model that effectively characterizes different doses, reconstruction kernels, and devices. The statistical model is generalized to deal with the partial volume effect via a localized mixture model that also describes the non-stationarity of noise. Finally, we propose a stabilization scheme to achieve stationary variance. The validation of the proposed methodology was performed with a physical phantom and clinical CT scans acquired with different configurations (kernels, doses, algorithms including iterative reconstruction). The results confirmed its suitability to enable comparisons with different doses, and acquisition protocols.

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Parameter Space and Potential for Biomarker Development in 25 Years of fMRI Drug Cue Reactivity

Sangchooli,

Zare-Bidoky,

Fathi Jouzdani

et al. 2024

JAMA Psychiatry

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ImportanceIn the last 25 years, functional magnetic resonance imaging drug cue reactivity (FDCR) studies have characterized some core aspects in the neurobiology of drug addiction. However, no FDCR-derived biomarkers have been approved for treatment development or clinical adoption. Traversing this translational gap requires a systematic assessment of the FDCR literature evidence, its heterogeneity, and an evaluation of possible clinical uses of FDCR-derived biomarkers.ObjectiveTo summarize the state of the field of FDCR, assess their potential for biomarker development, and outline a clear process for biomarker qualification to guide future research and validation efforts.Evidence ReviewThe PubMed and Medline databases were searched for every original FDCR investigation published from database inception until December 2022. Collected data covered study design, participant characteristics, FDCR task design, and whether each study provided evidence that might potentially help develop susceptibility, diagnostic, response, prognostic, predictive, or severity biomarkers for 1 or more addictive disorders.FindingsThere were 415 FDCR studies published between 1998 and 2022. Most focused on nicotine (122 [29.6%]), alcohol (120 [29.2%]), or cocaine (46 [11.1%]), and most used visual cues (354 [85.3%]). Together, these studies recruited 19 311 participants, including 13 812 individuals with past or current substance use disorders. Most studies could potentially support biomarker development, including diagnostic (143 [32.7%]), treatment response (141 [32.3%]), severity (84 [19.2%]), prognostic (30 [6.9%]), predictive (25 [5.7%]), monitoring (12 [2.7%]), and susceptibility (2 [0.5%]) biomarkers. A total of 155 interventional studies used FDCR, mostly to investigate pharmacological (67 [43.2%]) or cognitive/behavioral (51 [32.9%]) interventions; 141 studies used FDCR as a response measure, of which 125 (88.7%) reported significant interventional FDCR alterations; and 25 studies used FDCR as an intervention outcome predictor, with 24 (96%) finding significant associations between FDCR markers and treatment outcomes.Conclusions and RelevanceBased on this systematic review and the proposed biomarker development framework, there is a pathway for the development and regulatory qualification of FDCR-based biomarkers of addiction and recovery. Further validation could support the use of FDCR-derived measures, potentially accelerating treatment development and improving diagnostic, prognostic, and predictive clinical judgments.

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Methods and Challenges in Quantitative Imaging Biomarker Development

Cited by 85 publications

References 78 publications

Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

Statistical characterization of noise for spatial standardization of CT scans: Enabling comparison with multiple kernels and doses

Parameter Space and Potential for Biomarker Development in 25 Years of fMRI Drug Cue Reactivity

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