How do statistical differences in matrix-sensitive magnetic resonance outcomes translate into clinical assignment rules?

Assessment of an in vitro model of rotator cuff degeneration using quantitative magnetic resonance and ultrasound imaging with biochemical and histological correlation

PURPOSE

Quantitative imaging methods could improve diagnosis of rotator cuff degeneration, but the capability of quantitative MR and US imaging parameters to detect alterations in collagen is unknown. The goal of this study was to assess quantitative MR and US imaging measures for detecting abnormalities in collagen using an in vitro model of tendinosis with biochemical and histological correlation.

METHOD

36 pieces of supraspinatus tendons from 6 cadaveric donors were equally distributed into 3 groups (2 subjected to different concentrations of collagenase and a control group). Ultrashort echo time MR and US imaging measures were performed to assess changes at baseline and after 24 h of enzymatic digestion. Biochemical and histological measures, including brightfield, fluorescence, and polarized microscopy, were used to verify the validity of the model and were compared with quantitative imaging parameters. Correlations between the imaging parameters and biochemically measured digestion were analyzed.

RESULTS

Among the imaging parameters, macromolecular fraction (MMF), adiabatic T1ρ, T2*, and backscatter coefficient (BSC) were useful in differentiating between the extent of degeneration among the 3 groups. MMF strongly correlated with collagen loss (r=-0.81; 95% confidence interval [CI]: -0.90,-0.66), while the adiabatic T1ρ (r = 0.66; CI: 0.42,0.81), T2* (r = 0.58; CI: 0.31,0.76), and BSC (r = 0.51; CI: 0.22,0.72) moderately correlated with collagen loss.

CONCLUSIONS

MMF, adiabatic T1ρ, and T2* measured and US BSC can detect alterations in collagen. Of the quantitative MR and US imaging measures evaluated, MMF showed the highest correlation with collagen loss and can be used to assess rotator cuff degeneration.

Rotator cuff tendon assessment using magic-angle insensitive 3D ultrashort echo time cones magnetization transfer (UTE-Cones-MT) imaging and modeling with histological correlation

BACKGROUND

Rotator cuff tendons (RCTs) are challenging to image due to the "magic angle effect" and their short T₂ .

PURPOSE

To assess the degree of magic angle sensitivity of human RCTs and to utilize a 3D ultrashort echo time Cones sequence with magnetization transfer preparation (UTE-Cones-MT) and two-pool quantitative MT modeling with histological correlation. We hypothesized that MT parameters would be less sensitive to the magic angle compared with conventional T₂ measurements.

STUDY TYPE

Prospective imaging pathologic correlation.

SPECIMEN

Twenty cadaveric rotator cuff tendons were imaged at five sample orientations ranging from 0-90° relative to the B₀ field.

FIELD STRENGTH/SEQUENCE

3T/3D UTE-Cones-MT and Carr-Purcell-Meiboom-Gill (CPMG).

ASSESSMENT

Two-pool quantitative MT modeling parameters and T₂ values were calculated in regions of interest drawn by a medical physicist. Histopathological analysis was performed and mild and severe tendinopathy groups were assigned by a histopathologist and histotechnician.

STATISTICAL TESTS

Coefficients of variations (CVs) were calculated for measures between the different orientations and group means were compared for each measure.

RESULTS

CVs of T₂ and macromolecular fractions between orientations were 26.14 ± 16.82% and 6.18 ± 2.77% (mean ± SD), respectively. T₂ measurements at 0°, 27°, 70°, and 90° showed significant differences between the two histological groups (P = 0.004, 0.008, 0.003, and 0.015, respectively), but not at 55° (P = 0.611). Mean T₂ value ranges between orientations for the mild and severe tendinopathy groups were 15.27-30.32 msec and 20.81-35.85 msec, respectively, showing overlap despite statistically significant differences (P = 0.003). Macromolecular fractions at all angles showed significant differences between the two groups (P < 0.0001). Mean fraction ranges between orientations for the mild and severe tendinopathy groups were 14.32-17.17% and 10.00-13.75% respectively (P < 0.0001) with no overlap.

DATA CONCLUSION

Compared with T₂ , macromolecular fraction obtained with the 3D UTE-Cones-MT technique is resistant to the magic angle effect and is more sensitive to RCT degeneration.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017.

MR Parametric Mapping as a Biomarker of Early Joint Degeneration

Context:

Osteoarthritis (OA) is a common, worldwide disorder. Magnetic resonance (MR) imaging can directly and noninvasively evaluate articular cartilage and has emerged as an essential tool in the study of OA.

Evidence Acquisition:

A PubMed search was performed using the keywords quantitative MRI and cartilage. No limits were set on the range of years searched. Articles were reviewed for relevance with an emphasis on in vivo studies performed at 3 tesla.

Study Design:

Clinical review.

Level of Evidence:

Level 4.

Results:

T2, T2*, T1 (particularly when measured after exogenous contrast administration, such as with the delayed gadolinium-enhanced MR imaging of cartilage [dGEMRIC] technique), and T1ρ are among the most widely utilized quantitative MR imaging techniques to evaluate cartilage and have been implemented in various patient cohorts. Existing challenges include reproducibility of results, insufficient consensus regarding optimal sequences and parameters, and interpretation of values.

Conclusion:

Quantitative assessment of cartilage using MR imaging techniques likely represents the best opportunity to identify early cartilage degeneration and to follow patients after treatment. Despite existing challenges, ongoing work and unique approaches have shown exciting and promising results.

Multiparametric Classification of Skin from Osteogenesis Imperfecta Patients and Controls by Quantitative Magnetic Resonance Microimaging

The purpose of this study is to evaluate the ability of quantitative magnetic resonance imaging (MRI) to discriminate between skin biopsies from individuals with osteogenesis imperfecta (OI) and skin biopsies from individuals without OI. Skin biopsies from nine controls (unaffected) and nine OI patients were imaged to generate maps of five separate MR parameters, T₁, T₂, k_m, MTR and ADC. Parameter values were calculated over the dermal region and used for univariate and multiparametric classification analysis. A substantial degree of overlap of individual MR parameters was observed between control and OI groups, which limited the sensitivity and specificity of univariate classification. Classification accuracies ranging between 39% and 67% were found depending on the variable of investigation, with T₂ yielding the best accuracy of 67%. When several MR parameters were considered simultaneously in a multivariate analysis, the classification accuracies improved up to 89% for specific combinations, including the combination of T₂ and k_m. These results indicate that multiparametric classification by quantitative MRI is able to detect differences between the skin of OI patients and of unaffected individuals, which motivates further study of quantitative MRI for the clinical diagnosis of OI.

Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging

OBJECTIVE

The purpose of this study is to evaluate the ability of machine learning to discriminate between magnetic resonance images (MRI) of normal and pathological human articular cartilage obtained under standard clinical conditions.

METHOD

An approach to MRI classification of cartilage degradation is proposed using pattern recognition and multivariable regression in which image features from MRIs of histologically scored human articular cartilage plugs were computed using weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). The WND-CHRM method was first applied to several clinically available MRI scan types to perform binary classification of normal and osteoarthritic osteochondral plugs based on the Osteoarthritis Research Society International (OARSI) histological system. In addition, the image features computed from WND-CHRM were used to develop a multiple linear least-squares regression model for classification and prediction of an OARSI score for each cartilage plug.

RESULTS

The binary classification of normal and osteoarthritic plugs yielded results of limited quality with accuracies between 36% and 70%. However, multiple linear least-squares regression successfully predicted OARSI scores and classified plugs with accuracies as high as 86%. The present results improve upon the previously-reported accuracy of classification using average MRI signal intensities and parameter values.

CONCLUSION

MRI features detected by WND-CHRM reflect cartilage degradation status as assessed by OARSI histologic grading. WND-CHRM is therefore of potential use in the clinical detection and grading of osteoarthritis.

Classification of histologically scored human knee osteochondral plugs by quantitative analysis of magnetic resonance images at 3T

This work evaluates the ability of quantitative MRI to discriminate between normal and pathological human osteochondral plugs characterized by the Osteoarthritis Research Society International (OARSI) histological system. Normal and osteoarthritic human osteochondral plugs were scored using the OARSI histological system and imaged at 3 T using MRI sequences producing T1 and T2 contrast and measuring T1, T2, and T2* relaxation times, magnetization transfer, and diffusion. The classification accuracies of quantitative MRI parameters and corresponding weighted image intensities were evaluated. Classification models based on the Mahalanobis distance metric for each MRI measurement were trained and validated using leave-one-out cross-validation with plugs grouped according to OARSI histological grade and score. MRI measurements used for classification were performed using a region-of-interest analysis which included superficial, deep, and full-thickness cartilage. The best classifiers based on OARSI grade and score were T1- and T2-weighted image intensities, which yielded accuracies of 0.68 and 0.75, respectively. Classification accuracies using OARSI score-based group membership were generally higher when compared with grade-based group membership. MRI-based classification--either using quantitative MRI parameters or weighted image intensities--is able to detect early osteoarthritic tissue changes as classified by the OARSI histological system. These findings suggest the benefit of incorporating quantitative MRI acquisitions in a comprehensive clinical evaluation of OA.

Quantitative bi-component T2* analysis of histologically normal Achilles tendons

INTRODUCTION

the aim of this pilot study was to implement ultrashort echo time (UTE) MRI with bi-component analysis on grossly normal Achilles tendons with histologic correlation.

MATERIALS AND METHODS

six tendon samples which were grossly normal on visual inspection and palpation were harvested. A 2D UTE pulse sequence was implemented on a 3T MR scanner and bi-component and single-component T2* analysis was performed. Tendon samples were histologically processed and evaluated.

RESULTS

mean short T2* fraction was 79.2% (95% confidence interval [CI], 70.1 - 88.3%), mean short T2* was 1.8 ms (95% CI, 1.3 - 2.3 ms), mean long T2* fraction was 20.8% (95% CI, 11.7 - 29.9%), mean long T2* was 9.2 ms (95% CI, 5.1 - 13.3 ms), and mean single-component T2* was 2.5 ms (95% CI, 1.8 - 3.1 ms).

DISCUSSION

2D UTE MRI with bi-component and single-component T2* analysis was successfully implemented. Inter-individual variation can be demonstrated in grossly and histologically normal Achilles tendons.

Sensitivity and specificity of univariate MRI analysis of experimentally degraded cartilage under clinical imaging conditions

BACKGROUND

To evaluate the sensitivity and specificity of classification of pathomimetically degraded bovine nasal cartilage at 3 Tesla and 37°C using univariate MRI measurements of both pure parameter values and intensities of parameter-weighted images.

METHODS

Pre- and posttrypsin degradation values of T1 , T2 , T2 *, magnetization transfer ratio (MTR), and apparent diffusion coefficient (ADC), and corresponding weighted images, were analyzed. Classification based on the Euclidean distance was performed and the quality of classification was assessed through sensitivity, specificity and accuracy (ACC).

RESULTS

The classifiers with the highest accuracy values were ADC (ACC = 0.82 ± 0.06), MTR (ACC = 0.78 ± 0.06), T1 (ACC = 0.99 ± 0.01), T2 derived from a three-dimensional (3D) spin-echo sequence (ACC = 0.74 ± 0.05), and T2 derived from a 2D spin-echo sequence (ACC = 0.77 ± 0.06), along with two of the diffusion-weighted signal intensities (b = 333 s/mm(2) : ACC = 0.80 ± 0.05; b = 666 s/mm(2) : ACC = 0.85 ± 0.04). In particular, T1 values differed substantially between the groups, resulting in atypically high classification accuracy. The second-best classifier, diffusion weighting with b = 666 s/mm(2) , as well as all other parameters evaluated, exhibited substantial overlap between pre- and postdegradation groups, resulting in decreased accuracies.

CONCLUSION

Classification according to T1 values showed excellent test characteristics (ACC = 0.99), with several other parameters also showing reasonable performance (ACC > 0.70). Of these, diffusion weighting is particularly promising as a potentially practical clinical modality. As in previous work, we again find that highly statistically significant group mean differences do not necessarily translate into accurate clinical classification rules.