Linearity and Bias of Proton Density Fat Fraction as a Quantitative                    Imaging Biomarker: A Multicenter, Multiplatform, Multivendor Phantom                    Study

Background/aims Quantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected. Methods Monte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease. Results Incorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (<95%); the coverage is especially reduced when the quantitative imaging biomarker measurements have good precision and/or there is a large treatment effect. Estimates of the measurement bias from technical performance validation studies can be used to correct the confidence intervals for the treatment effect. Conclusion Technical performance validation studies of quantitative imaging biomarkers are needed to supplement clinical trial data to provide unbiased estimates of the treatment effect.

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Repeatability and accuracy of various region-of-interest sampling strategies for hepatic MRI proton density fat fraction quantification

Abdominal Radiology ◽

10.1007/s00261-021-02965-5 ◽

2021 ◽

Author(s):

Cheng William Hong ◽

Jennifer Y. Cui ◽

Danielle Batakis ◽

Yang Xu ◽

Tanya Wolfson ◽

...

Keyword(s):

Region Of Interest ◽

Proton Density ◽

Sampling Strategies ◽

Proton Density Fat Fraction ◽

Fat Fraction

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Association of Thigh Muscle Strength with Texture Features Based on Proton Density Fat Fraction Maps Derived from Chemical Shift Encoding-Based Water–Fat MRI

Diagnostics ◽

10.3390/diagnostics11020302 ◽

2021 ◽

Vol 11 (2) ◽

pp. 302

Author(s):

Michael Dieckmeyer ◽

Stephanie Inhuber ◽

Sarah Schläger ◽

Dominik Weidlich ◽

Muthu R. K. Mookiah ◽

...

Keyword(s):

Chemical Shift ◽

Muscle Strength ◽

Neuromuscular Disorders ◽

Texture Features ◽

Thigh Muscle ◽

Proton Density ◽

Proton Density Fat Fraction ◽

Thigh Muscles ◽

Fat Fraction ◽

Flexion Strength

Purpose: Based on conventional and quantitative magnetic resonance imaging (MRI), texture analysis (TA) has shown encouraging results as a biomarker for tissue structure. Chemical shift encoding-based water–fat MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of thigh muscles has been associated with musculoskeletal, metabolic, and neuromuscular disorders and was demonstrated to predict muscle strength. The purpose of this study was to investigate PDFF-based TA of thigh muscles as a predictor of thigh muscle strength in comparison to mean PDFF. Methods: 30 healthy subjects (age = 30 ± 6 years; 15 females) underwent CSE-MRI of the lumbar spine at 3T, using a six-echo 3D spoiled gradient echo sequence. Quadriceps (EXT) and ischiocrural (FLEX) muscles were segmented to extract mean PDFF and texture features. Muscle flexion and extension strength were measured with an isokinetic dynamometer. Results: Of the eleven extracted texture features, Variance(global) showed the highest significant correlation with extension strength (p < 0.001, R2adj = 0.712), and Correlation showed the highest significant correlation with flexion strength (p = 0.016, R2adj = 0.658). Multivariate linear regression models identified Variance(global) and sex, but not PDFF, as significant predictors of extension strength (R2adj = 0.709; p < 0.001), while mean PDFF, sex, and BMI, but none of the texture features, were identified as significant predictors of flexion strength (R2adj = 0.674; p < 0.001). Conclusions: Prediction of quadriceps muscle strength can be improved beyond mean PDFF by means of TA, indicating the capability to quantify muscular fat infiltration patterns.

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