Prostate Cancers Invisible on Multiparametric MRI: Pathologic Features in Correlation with Whole-Mount Prostatectomy.

We investigated why some prostate cancers (PCas) are not identified on multiparametric MRI (mpMRI) by using ground truth reference from whole-mount prostatectomy specimens. A total of 61 patients with biopsy-confirmed PCa underwent 3T mpMRI followed by prostatectomy. Lesions visible on MRI prospectively or retrospectively identified after correlating with histology were considered "identified cancers" (ICs). Lesions that could not be identified on mpMRI were considered "unidentified cancers" (UCs). Pathologists marked the Gleason score, stage, size, and density of the cancer glands and performed quantitative histology to calculate the tissue composition. Out of 115 cancers, 19 were unidentified on MRI. The UCs were significantly smaller and had lower Gleason scores and clinical stage lesions compared with the ICs. The UCs had significantly (p < 0.05) higher ADC (1.34 ± 0.38 vs. 1.02 ± 0.30 µm2/ms) and T2 (117.0 ± 31.1 vs. 97.1 ± 25.1 ms) compared with the ICs. The density of the cancer glands was significantly (p = 0.04) lower in the UCs. The percentage of the Gleason 4 component in Gleason 3 + 4 lesions was nominally (p = 0.15) higher in the ICs (20 ± 12%) compared with the UCs (15 ± 8%). The UCs had a significantly lower epithelium (32.9 ± 21.5 vs. 47.6 ± 13.1%, p = 0.034) and higher lumen volume (20.4 ± 10.0 vs. 13.3 ± 4.1%, p = 0.021) compared with the ICs. Independent from size and Gleason score, the tissue composition differences, specifically, the higher lumen and lower epithelium in UCs, can explain why some of the prostate cancers cannot be identified on mpMRI.

Multi-model sequential analysis of MRI data for microstructure prediction in heterogeneous tissue.

We propose a general method for combining multiple models to predict tissue microstructure, with an exemplar using in vivo diffusion-relaxation MRI data. The proposed method obviates the need to select a single 'optimum' structure model for data analysis in heterogeneous tissues where the best model varies according to local environment. We break signal interpretation into a three-stage sequence: (1) application of multiple semi-phenomenological models to predict the physical properties of tissue water pools contributing to the observed signal; (2) from each Stage-1 semi-phenomenological model, application of a tissue microstructure model to predict the relative volumes of tissue structure components that make up each water pool; and (3) aggregation of the predictions of tissue structure, with weightings based on model likelihood and fractional volumes of the water pools from Stage-1. The multiple model approach is expected to reduce prediction variance in tissue regions where a complex model is overparameterised, and bias where a model is underparameterised. The separation of signal characterisation (Stage-1) from biological assignment (Stage-2) enables alternative biological interpretations of the observed physical properties of the system, by application of different tissue structure models. The proposed method is exemplified with human prostate diffusion-relaxation MRI data, but has potential application to a wide range of analyses where a single model may not be optimal throughout the sampled domain.

Histological Validation of MRI: A Review of Challenges in Registration of Imaging and Whole-Mount Histopathology.

Rigorous validation with ground truth information such as histology is needed to reliably assess the current and potential value of MRI techniques to characterize tissue and identify disease-related tissue alterations. Commonly used methods that aim to directly correlate histology and MRI data generally fall short of this goal due to spatial errors that preclude direct matching. Errors result from tissue deformation, differences in spatial resolution and slice thickness, non-coplanar and/or nonintersecting plane orientations, and different image contrast mechanisms. Some of these problems arise from limitations in standard protocols for clinical tissue processing and histology-based pathology reporting, and to some extent can be addressed by modifications to standard protocols without compromising the clinical process. Typical modifications include ex vivo specimen MRI, block-face photography, addition of fiducial markers, and 3D printed molds to constrain tissue deformation and guide sectioning. This review summarizes the advantages and limitations of MRI validation techniques based on coregistration of MRI with whole-mount histology of tissue specimens. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 1.

Validation of Prostate Tissue Composition by Using Hybrid Multidimensional MRI: Correlation with Histologic Findings.

Background Tissue estimates obtained by using microstructure imaging techniques, such as hybrid multidimensional (HM) MRI, may improve prostate cancer diagnosis but require histologic validation. Purpose To validate prostate tissue composition measured by using HM MRI, with quantitative histologic evaluation from whole-mount prostatectomy as the reference standard. Materials and Methods In this HIPAA-compliant study, from December 2016 to July 2018, prospective participants with biopsy-confirmed prostate cancer underwent 3-T MRI before radical prostatectomy. Axial HM MRI was performed with all combinations of echo times (57, 70, 150, and 200 msec) and b values (0, 150, 750, and 1500 sec/mm2). Data were fitted by using a three-compartment signal model to generate volumes for each tissue component (stroma, epithelium, lumen). Quantitative histologic evaluation was performed to calculate volume fractions for each tissue component for regions of interest corresponding to MRI. Tissue composition measured by using HM MRI and quantitative histologic evaluation were compared (paired t test) and correlated (Pearson correlation coefficient), and agreement (concordance correlation) was assessed. Receiver operating characteristic curve analysis for cancer diagnosis was performed. Results Twenty-five participants (mean age, 60 years ± 7 [standard deviation]; 30 cancers and 45 benign regions of interest) were included. Prostate tissue composition measured with HM MRI and quantitative histologic evaluation did not differ (stroma, 45% ± 11 vs 44% ± 11 [P = .23]; epithelium, 31% ± 15 vs 34% ± 15 [P = .08]; and lumen, 24% ± 13 vs 22% ± 11 [P = .80]). Between HM MRI and histologic evaluation, there was excellent correlation (Pearson r: overall, 0.91; stroma, 0.82; epithelium, 0.93; lumen, 0.90 [all P < .05]) and agreement (concordance correlation coefficient: overall, 0.91; stroma, 0.81; epithelium, 0.90; and lumen, 0.87). High areas under the receiver operating characteristic curve obtained with HM MRI (0.96 for epithelium and 0.94 for lumen, P < .001) and histologic evaluation (0.94 for epithelium and 0.88 for lumen, P < .001) were found for differentiation between benign tissue and prostate cancer. Conclusion Tissue composition measured by using hybrid multidimensional MRI had excellent correlation with quantitative histologic evaluation as the reference standard. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Muglia in this issue.

The Effect of Visual Hindsight Bias on Radiologist Perception.

RATIONALE AND OBJECTIVES: To measure the effect of visual hindsight bias on radiologists' perception during chest radiograph pulmonary nodule detection. MATERIALS AND METHODS: This was a prospective multi-observer study to assess the effect of hindsight bias on radiologists' perception. Sixteen radiologists were asked to interpret 15 postero-anterior chest images containing a solitary lung nodule each consisting of 25 incremental levels of blur. Participants were requested initially to detect the nodule by reducing the blur of the images (foresight). They were then asked to increase the blur until the identified nodule was undetectable (hindsight). Participants then repeated the experiment, after being informed of the potential effects of hindsight bias and asked to counteract these effects. Participants were divided into two groups (experienced and less experienced) and the nodules were given different conspicuity ratings to determine the effect of expertise and task difficulty. Eye tracking technology was also utilised to capture visual search. RESULTS: Wilcoxon analysis demonstrated significant differences between foresight and hindsight values of the radiologists (p = 0.02). However, after being informed of hindsight bias, these differences were no longer significant (p = 0.97). Friedman analysis also determined overall significance in the hindsight ratios between nodule conspicuities for both phases (phase 1: p = 0.02; phase 2: p = 0.02). There was no significance difference between the experienced and less experienced groups. CONCLUSION: This study demonstrated that radiologists exhibit hindsight bias but appeared to be able to compensate for this phenomenon once its effects were considered. Also, visual hindsight bias appears to be affected by task difficulty with a greater effect occurring with less conspicuous nodules.

VERDICT MRI validation in fresh and fixed prostate specimens using patient-specific moulds for histological and MR alignment.

The VERDICT framework for modelling diffusion MRI data aims to relate parameters from a biophysical model to histological features used for tumour grading in prostate cancer. Validation of the VERDICT model is necessary for clinical use. This study compared VERDICT parameters obtained ex vivo with histology in five specimens from radical prostatectomy. A patient-specific 3D-printed mould was used to investigate the effects of fixation on VERDICT parameters and to aid registration to histology. A rich diffusion data set was acquired in each ex vivo prostate before and after fixation. At both time points, data were best described by a two-compartment model: the model assumes that an anisotropic tensor compartment represents the extracellular space and a restricted sphere compartment models the intracellular space. The effect of fixation on model parameters associated with tissue microstructure was small. The patient-specific mould minimized tissue deformations and co-localized slices, so that rigid registration of MRI to histology images allowed region-based comparison with histology. The VERDICT estimate of the intracellular volume fraction corresponded to histological indicators of cellular fraction, including high values in tumour regions. The average sphere radius from VERDICT, representing the average cell size, was relatively uniform across samples. The primary diffusion direction from the extracellular compartment of the VERDICT model aligned with collagen fibre patterns in the stroma obtained by structure tensor analysis. This confirmed the biophysical relationship between ex vivo VERDICT parameters and tissue microstructure from histology.

Diagnosis of Prostate Cancer with Noninvasive Estimation of Prostate Tissue Composition by Using Hybrid Multidimensional MR Imaging: A Feasibility Study.

Purpose To evaluate whether compartmental analysis by using hybrid multidimensional magnetic resonance (MR) imaging can be used to diagnose prostate cancer and determine its aggressiveness. Materials and Methods Twenty-two patients with prostate cancer underwent preoperative 3.0-T MR imaging. Axial images were obtained with hybrid multidimensional MR imaging by using all combinations of echo times (47, 75, 100 msec) and b values of 0, 750, 1500 sec/mm2, resulting in a 3 × 3 array of data associated with each voxel. Volumes of the tissue components stroma, epithelium, and lumen were calculated by fitting the hybrid data to a three-compartment signal model, with distinct, paired apparent diffusion coefficient (ADC) and T2 values associated with each compartment. Volume fractions and conventional ADC and T2 were measured for regions of interest in sites of prostatectomy-verified malignancy (n = 28) and normal tissue (n = 71). Receiver operating characteristic (ROC) analysis was used to evaluate the performance of various parameters in differentiating prostate cancer from benign tissue. Results Compared with normal tissue, prostate cancer showed significantly increased fractional volumes of epithelium (23.2% ± 7.1 vs 48.8% ± 9.2, respectively) and reduced fractional volumes of lumen (26.4% ± 14.1 vs 14.0% ± 5.2) and stroma (50.5% ± 15.7 vs 37.2% ± 9.1) by using hybrid multidimensional MR imaging. The fractional volumes of tissue components show a significantly higher Spearman correlation coefficient with Gleason score (epithelium: ρ = 0.652, P = .0001; stroma: ρ = -0.439, P = .020; lumen: ρ = -0.390, P = .040) compared with traditional T2 values (ρ = -0.292, P = .132) and ADCs (ρ = -0.315, P = .102). The area under the ROC curve for differentiation of cancer from normal prostate was highest for fractional volume of epithelium (0.991), followed by fractional volumes of lumen (0.800) and stroma (0.789). Conclusion Fractional volumes of prostatic lumen, stroma, and epithelium change significantly when cancer is present. These parameters can be measured noninvasively by using hybrid multidimensional MR imaging and have the potential to improve the diagnosis of prostate cancer and determine its aggressiveness. © RSNA, 2018 Online supplemental material is available for this article.

Measurement and modeling of diffusion time dependence of apparent diffusion coefficient and fractional anisotropy in prostate tissue ex vivo.

The purpose of this study was to measure and model the diffusion time dependence of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) derived from conventional prostate diffusion-weighted imaging methods as used in recommended multiparametric MRI protocols. Diffusion tensor imaging (DTI) was performed at 9.4 T with three radical prostatectomy specimens, with diffusion times in the range 10-120 ms and b-values 0-3000 s/mm2 . ADC and FA were calculated from DTI measurements at b-values of 800 and 1600 s/mm2 . Independently, a two-component model (restricted isotropic plus Gaussian anisotropic) was used to synthesize DTI data, from which ADC and FA were predicted and compared with the measured values. Measured ADC and FA exhibited a diffusion time dependence, which was closely predicted by the two-component model. ADC decreased by about 0.10-0.15 µm2 /ms as diffusion time increased from 10 to 120 ms. FA increased with diffusion time at b-values of 800 and 1600 s/mm2 but was predicted to be independent of diffusion time at b = 3000 s/mm2 . Both ADC and FA exhibited diffusion time dependence that could be modeled as two unmixed water pools - one having isotropic restricted dynamics, and the other unrestricted anisotropic dynamics. These results highlight the importance of considering and reporting diffusion times in conventional ADC and FA calculations and protocol recommendations, and inform the development of improved diffusion methods for prostate cancer imaging.

Apparatus for Histological Validation of In Vivo and Ex Vivo Magnetic Resonance Imaging of the Human Prostate.

This article describes apparatus to aid histological validation of magnetic resonance imaging studies of the human prostate. The apparatus includes a 3D-printed patient-specific mold that facilitates aligned in vivo and ex vivo imaging, in situ tissue fixation, and tissue sectioning with minimal organ deformation. The mold and a dedicated container include MRI-visible landmarks to enable consistent tissue positioning and minimize image registration complexity. The inclusion of high spatial resolution ex vivo imaging aids in registration of in vivo MRI and histopathology data.

RELATIONSHIP BETWEEN RADIATION DOSE AND IMAGE QUALITY IN DIGITAL BREAST TOMOSYNTHESIS.

This phantom-based study aimed to examine radiation dose from digital breast tomosynthesis (DBT) and digital mammography (DM) and to assess the potential for dose reductions for each modality. Images were acquired at 10-60 mm thicknesses and four dose levels and mean glandular dose was determined using a solid-state dosemeter. Eleven readers assessed image quality and compared simulated lesions with those on a reference image, and the data produced was analysed with the Friedman and Wilcoxon signed-rank tests. For a phantom thickness of 50 mm (typical breast thickness), DBT dose was 13 % higher than DM, but this differential is highly dependent on thickness. Visibility of masses was equal to a reference image (produced at 100 % dose) when dose was reduced by 75 and 50 % for DBT and DM. For microcalcifications, visibility was comparable with the reference image for both modalities at 50 % dose. This study highlighted the potential for reducing dose with DBT.

Limitations and Prospects for Diffusion-Weighted MRI of the Prostate.

Diffusion-weighted imaging (DWI) is the most effective component of the modern multi-parametric magnetic resonance imaging (mpMRI) scan for prostate pathology. DWI provides the strongest prediction of cancer volume, and the apparent diffusion coefficient (ADC) correlates moderately with Gleason grade. Notwithstanding the demonstrated cancer assessment value of DWI, the standard measurement and signal analysis methods are based on a model of water diffusion dynamics that is well known to be invalid in human tissue. This review describes the biophysical limitations of the DWI component of the current standard mpMRI protocol and the potential for significantly improved cancer assessment performance based on more sophisticated measurement and signal modeling techniques.

Information-based ranking of 10 compartment models of diffusion-weighted signal attenuation in fixed prostate tissue.

This study compares the theoretical information content of single- and multi-compartment models of diffusion-weighted signal attenuation in prostate tissue. Diffusion-weighted imaging (DWI) was performed at 9.4 T with multiple diffusion times and an extended range of b values in four whole formalin-fixed prostates. Ten models, including different combinations of isotropic, anisotropic and restricted components, were tested. Models were ranked using the Akaike information criterion. In all four prostates, two-component models, comprising an anisotropic Gaussian component and an isotropic restricted component, ranked highest in the majority of voxels. Single-component models, whether isotropic (apparent diffusion coefficient, ADC) or anisotropic (diffusion tensor imaging, DTI), consistently ranked lower than multi-component models. Model ranking trends were independent of voxel size and maximum b value in the range tested (1.6-16 mm(3) and 3000-10,000 s/mm(2)). This study characterizes the two major water components previously identified by biexponential models and shows that models incorporating both anisotropic and restricted components provide more information-rich descriptions of DWI signals in prostate tissue than single- or multi-component anisotropic models and models that do not account for restricted diffusion.

Diffusion anisotropy in fresh and fixed prostate tissue ex vivo.

PURPOSE: To investigate diffusion anisotropy in whole human prostate specimens METHODS: Seven whole radical prostatectomy specimens were obtained with informed patient consent and institutional ethics approval. Diffusion tensor imaging was performed at 9.4 Tesla. Diffusion tensors were calculated from the native acquired data and after progressive downsampling RESULTS: Fractional anisotropy (FA) decreased as voxel volume increased, and differed widely between prostates. Fixation decreased mean FA by ∼0.05-0.08 at all voxel volumes but did not alter principle eigenvector orientation. In unfixed tissue high FA (> 0.6) was found only in voxels of volume <0.5 mm(3) , and then only in a small fraction of all voxels. At typical clinical voxel volumes (4-16 mm(3) ) less than 50% of voxels had FA > 0.25. FA decreased at longer diffusion times (Δ = 60 or 80 ms compared with 20 ms), but only by ∼0.02 at typical clinical voxel volume. Peripheral zone FA was significantly lower than transition zone FA in five of the seven prostates CONCLUSION: FA varies widely between prostates. The very small proportion of clinical size voxels with high FA suggests that in clinical DWI studies ADC based on three-direction measurements will be minimally affected by anisotropy. Magn Reson Med 76:626-634, 2016. © 2015 Wiley Periodicals, Inc.

Changes in Epithelium, Stroma, and Lumen Space Correlate More Strongly with Gleason Pattern and Are Stronger Predictors of Prostate ADC Changes than Cellularity Metrics.

PURPOSE: To investigate the hypothesis that the clinically observed decrease in apparent diffusion coefficient (ADC) at diffusion-weighted magnetic resonance imaging with increasing prostate cancer Gleason grade can be attributed to an increasing volume of low-diffusivity epithelial cells and corresponding decreasing volumes of higher-diffusivity stroma and lumen space rather than to increased cell density. MATERIALS AND METHODS: Tissue samples were acquired after institutional ethics review committee approval and informed consent from patients were obtained. Nuclear count, nuclear area, and gland component volumes (epithelium, stroma, lumen space) were measured in tissue from 14 patients. Gland component volumes and cellularity metrics were correlated with Gleason pattern (Spearman rank correlation coefficient) and measured ADC (Pearson correlation coefficient) in six prostates ex vivo. Differences between metrics for cancerous tissue and those for normal tissue were assessed by using a two-tailed two-sample t test. Linear mixed models with a post hoc Fisher least significant difference test were used to assess differences between gland component volumes and cellularity metrics for multiple groups. To adjust for a clustering effect due to repeated measures, the organ mean value of the measured metric for each tissue type was used in the analysis. RESULTS: There were significant differences between Gleason patterns for gland component volumes (P < .05) but not nuclear count (P = .100) or area (P = .141). There was a stronger correlation of Gleason pattern with gland component volumes (n = 553) of epithelium (Spearman ρ = 0.898, P < .001), stroma (ρ = -0.651, P < .001), and lumen space (ρ = -0.912, P = .007) than with the cellularity metrics (n = 288) nuclear area (ρ = 0.422, P = .133) or nuclear count (ρ = 0.082, P = .780). There was a stronger correlation between measured ADC and lumen volume (r = 0.688, P < .001) and epithelium volume (r = -0.647, P < .001) than between ADC and nuclear count (r = -0.598, P < .001) or nuclear area (r = -0.569, P < .001) (n = 57). CONCLUSION: Differences in the gland compartment volumes of prostate tissue having distinct diffusivities, rather than changes in the conventionally cited "cellularity" metrics, are likely to be the major contributor to clinically observed variations of ADC in prostate tissue.

Assessment of non-Gaussian diffusion with singly and doubly stretched biexponential models of diffusion-weighted MRI (DWI) signal attenuation in prostate tissue.

Non-Gaussian diffusion dynamics was investigated in the two distinct water populations identified by a biexponential model of diffusion in prostate tissue. Diffusion-weighted MRI (DWI) signal attenuation was measured ex vivo in two formalin-fixed prostates at 9.4 T with diffusion times Δ = 10, 20 and 40 ms, and b values in the range 0.017-8.2 ms/µm(2) . A conventional biexponential model was compared with models in which either the lower diffusivity component or both of the components of the biexponential were stretched. Models were compared using Akaike's Information Criterion (AIC) and a leave-one-out (LOO) test of model prediction accuracy. The doubly stretched (SS) model had the highest LOO prediction accuracy and lowest AIC (highest information content) in the majority of voxels at Δ = 10 and 20 ms. The lower diffusivity stretching factor (α2 ) of the SS model was consistently lower (range ~0.3-0.9) than the higher diffusivity stretching factor (α1 , range ~0.7-1.1), indicating a high degree of diffusion heterogeneity in the lower diffusivity environment, and nearly Gaussian diffusion in the higher diffusivity environment. Stretched biexponential models demonstrate that, in prostate tissue, the two distinct water populations identified by the simple biexponential model individually exhibit non-Gaussian diffusion dynamics.

Effect of radiologists' experience on breast cancer detection and localization using digital breast tomosynthesis.

OBJECTIVES: The objectives are To to compare the diagnostic performance of combined digital breast tomosynthesis (DBT) and digital mammography (DM) with that of DM alone, as a function of radiologists' experience with DBT. METHODS: Ethical committee approval was obtained. Fifty cases (27 cancer, 23 normal), each containing both digital mammography (DM) and digital breast tomosynthesis (DBT) images, were reviewed by 26 radiologists, divided into three groups according to level of experience with DBT (none, workshop experience, and clinical experience). The radiologists' diagnostic performance using DM was compared with that using DM + DBT, and evaluated by area under receiver-operating characteristic curve (AUC), jackknife free-response receiver-operator characteristics figure of metric (JAFROC FOM), sensitivity, location sensitivity, and specificity. RESULTS: For all readers combined, performance using DM + DBT was significantly higher than for DM alone by both AUC (0.788 vs 0.681, p < 0.001) and JAFROC FOM (0.745 vs 0.621, p < 0.001). Similar results were obtained for readers with no DBT experience (AUC 0.775 vs 0.682, p = 0.004; JAFROC FOM 0.695 vs 0.603, p = 0.016) and with clinical DBT experience (AUC 0.789 vs 0.681, p = 0.042; and JAFROC FOM 0.764 vs 0.632, p = 0.031). CONCLUSIONS: Addition of DBT to DM significantly improves radiologists' diagnostic performance whether or not they have prior DBT experience. KEY POINTS: • Adding DBT to DM increased the number of detected cancers • DBT + DM led to more accurate localization of breast cancers than DM • Addition of DBT improved radiologists' performance regardless of prior DBT experience • High-volume radiologists with different DBT experience levels performed similarly on DM + DBT.

Microscopic diffusion properties of fixed breast tissue: Preliminary findings.

PURPOSE: To investigate the microscopic diffusion properties of formalin-fixed breast tissue. METHODS: Diffusion microimaging was performed at 16.4T with 40-µm isotropic voxels on two normal and two cancer tissue samples from four patients. Results were correlated with histology of the samples. RESULTS: Diffusion-weighted images and mean diffusivity maps demonstrated distinct diffusivity differences between breast tissue components. Mean diffusivity (MD) in normal tissue was 0.59 ± 0.24 µm(2) /ms for gland lobule (voxels containing epithelium and intralobular stroma) and 1.23 ± 0.34 µm(2) /ms for interlobular fibrous stroma. In the cancer samples, MD = 0.45 ± 0.23 µm(2) /ms for invasive ductal carcinoma (voxels contain epithelium and intralobular stroma) and 0.61 ± 0.35 µm(2) /ms for ductal carcinoma in situ. There were significant MD differences between all tissue components (P < 0.005), except between gland lobule and ductal carcinoma in situ (P = 0.71). The low diffusivity of epithelium-rich cancer tissue and of normal epithelium relative to its supporting fibrous stroma was similar to that reported for prostate tissue and the esophageal wall. CONCLUSION: Diffusion microimaging demonstrates distinct diffusivity differences between breast tissue glandular structures. Low diffusivity may be a distinctive feature of mammalian epithelia.

Responsiveness of quantitative cartilage measures over one year in knee osteoarthritis: comparison of radiography and MRI assessments.

PURPOSE: To directly compare the responsiveness of quantitative imaging measures of disease progression in knee osteoarthritis (OA). In the medial compartment of the knee comparison was made between: 1) radiographic joint space narrowing (JSN); 2) global quantitative magnetic resonance imaging (qMRI) of cartilage volume; 3) regional qMRI of cartilage thickness; and 4) regional analysis using an ordered value (OV) methodology. MATERIALS AND METHODS: 3T MRI and weight-bearing radiography of the knees were performed at baseline and 1-year timepoints in 23 subjects (mean age 63 years) with symptomatic knee OA. Standardized response means (SRM) were calculated for each measure. Statistical analysis to determine significance of change between timepoints was performed with a two-tailed Student's t-test (JSN, global, regional analysis) and nonparametric Mann-Whitney test (ordered values). RESULTS: At 1 year, global cartilage volume losses of 2.3% (SRM -0.44) in the medial tibia and 6.9% in the medial femur (SRM -0.74) were recorded. SRM for JSN was -0.46. Regional analysis revealed largest reductions in cartilage thickness in the external (SRM -0.84) weight-bearing subregion of the medial femur and in the posterior subregion of the medial tibia (SRM -0.79). OV analysis in the medial compartment revealed areas of cartilage thinning (four ranked OV) and cartilage thickening (two ranked OV). CONCLUSION: The MRI OV approach proved to be a superior analysis tool for detecting changes in cartilage morphology over a 1-year period. Radiographically defined JSN was found to be the least responsive measurement method of knee OA disease progression.