Patellofemoral morphology is not related to pain using three-dimensional quantitative analysis in an older population: data from the Osteoarthritis Initiative

Objectives: Current structural associations of patellofemoral pain (PFP) are based on 2D imaging methodology with inherent measurement uncertainty due to positioning and rotation. This study employed novel technology to create 3D measures of commonly described patellofemoral joint imaging features and compared these features in people with and without PFP in a large cohort. Methods: We compared two groups from the Osteoarthritis Initiative: one with localized PFP and pain on stairs, and a control group with no knee pain; both groups had no radiographic OA. MRI bone surfaces were automatically segmented and aligned using active appearance models. We applied t-tests, logistic regression and linear discriminant analysis to compare 13 imaging features (including patella position, trochlear morphology, facet area and tilt) converted into 3D equivalents, and a measure of overall 3D shape. Results: One hundred and fifteen knees with PFP (mean age 59.7, BMI 27.5 kg/m², female 58.2%) and 438 without PFP (mean age 63.6, BMI 26.9 kg/m², female 52.9%) were included. After correction for multiple testing, no statistically significant differences were found between groups for any of the 3D imaging features or their combinations. A statistically significant discrimination was noted for overall 3D shape between genders, confirming the validity of the 3D measures. Conclusion: Challenging current perceptions, no differences in patellofemoral morphology were found between older people with and without PFP using 3D quantitative imaging analysis. Further work is needed to see if these findings are replicated in a younger PFP population

Marked and Rapid Change of Bone Shape In Acutely Acl Injured Knees – An Exploratory Analysis Of The Kanon Trial

Background To investigate changes in knee 3D bone shape over the first 5 years after acute anterior cruciate ligament (ACL) injury in participants of the randomized controlled KANON-trial. Methods Serial MR images over 5 years from 121 young (32 women, mean age 26.1 years) adults with an acute ACL tear in a previously un-injured knee were analyzed using statistical shape models for bone. A matched reference cohort of 176 individuals was selected from the Osteoarthritis Initiative (OAI). Primary endpoint was change in bone area of the medial femoral condyle; exploratory analyses compared results by treatment and examined other knee regions. Comparisons were made using repeated measures mixed model ANOVA with adjustment for age, sex and BMI. Results Mean medial femur bone area increased 3.2% (78.0 [95% CI 70.2 to 86.4] mm2) over 5 years after ACL injury and most prominently in knees treated with ACL reconstruction. A higher rate of increase occurred over the first two years compared to the latter three-years (66.2 [59.3 to 73.2] vs. 17.6 [12.2 to 23.0] mm2) and was 6.7 times faster than in the reference cohort. The pattern and location of shape change in the extrapolated KANON data was very similar to that observed in another knee-osteoarthritis cohort. Conclusion 3D shape modelling after acute ACL injury revealed rapid bone shape changes, already evident at 3 months. The bone-change pattern after ACL injury demonstrated flattening and bone growth on the outer margins of the condyles similar to that reported in established knee osteoarthritis

Precision, Reliability, and Responsiveness of a Novel Automated Quantification Tool for Cartilage Thickness: Data from the Osteoarthritis Initiative

Objective; Accurate automated segmentation of cartilage should provide rapid reliable outcomes for both epidemiological studies and clinical trials. We aimed to assess the precision and responsiveness of cartilage thickness measured with careful manual segmentation or a novel automated technique. Methods; Agreement of automated segmentation was assessed against two manual segmentation datasets: 379 MR images manually segmented in-house (Training set), and 582 from the OAI with data available at 0, 1, and 2 years (Biomarkers set). Agreement of mean thickness was assessed using Bland-Altman plots, change with pairwise Students t-test, in the central medial femur and tibia regions (cMF, cMT). Repeatability was assessed on a set of 19 knees imaged twice on the same day. Responsiveness was assessed using standardised response means (SRMs). Results; Agreement of manual vs automated methods was excellent with no meaningful systematic bias (Training set cMF bias 0.1mm 95%CI ±0.35, Biomarkers set bias 0.1mm ±0.4). The smallest detectable difference (SDD) for cMF was 0.13mm, coefficient of variation (CoV) 3.1%; cMT 0.16 mm, 2.65%. Reported change using manual segmentations in the cMF region at 1 year was -0.031mm, confidence limit (-0.022, -0.039), p<10-4, SRM -0.31 (-0.23,-0.38); at 2 years was -0.071 (-0.058,-0.085), p<10-4, SRM -0.43(-0.36,-0.49). Reported change using automated segmentations in the cMF at 1 year was - 0.059 (-0.047, -0.071), p<10-4, SRM -0.41(-0.34,-0.48) ; 2 years: -0.14 (-0.123,-0.157), p<10-4, SRM -0.67 (-0.6,-0.72). Conclusion; A novel cartilage segmentation method provides highly accurate and repeatable measures with comparable cartilage thickness measurements to careful manual segmentation, but with improved responsiveness

Sterile Neutrinos in E_6 and a Natural Understanding of Vacuum Oscillation Solution to the Solar Neutrino Puzzle

If Nature has chosen the vacuum oscillation solution to the Solar neutrino puzzle, a key theoretical challenge is to understand the extreme smallness of the $\Delta m^2_{\nu_e-\nu_X}$ ($\sim 10^{-10} eV^2$) required for the purpose. We find that in a class of models such as [SU(3)]^3 or its parent group E_6, which contain one sterile neutrino, $\nu_{is}$ for each family, the $\Delta m^2_{\nu_i-\nu_{is}}$ is proportional to the cube of the lepton Yukawa coupling. Therefore fitting the atmospheric neutrino data then predicts the $\nu_e-\nu_{es}$ mass difference square to be $\sim (\frac{m_e}{m_{\mu}})^3 \Delta m^2_{atmos}$, where the atmospheric neutrino data is assumed to be solved via the $\nu_{\mu}-\nu_{\mu s}$ oscillation. This provides a natural explanation of the vacuum oscillation solution to the solar neutrino problem.Comment: 7 pages, UMD-PP-99-109; new references added; no other chang

Where does meniscal damage progress most rapidly? An analysis using three-dimensional shape models on data from the Osteoarthritis Initiative

Objectives: Meniscal pathology is integral to knee osteoarthritis (OA) and its progression; it provides a progression biomarker and a potential treatment target. MRI demonstrates large heterogeneity in meniscal damage; this structural complexity means measurement is difficult. The aim of this study was to apply novel 3D image analysis to determine which meniscal pathologies demonstrated most change during OA progression. Methods: Knee images were selected from the progression cohort of the Osteoarthritis Initiative choosing participants with risk factors for medial OA progression. Medial and lateral menisci were manually segmented then analysed using a statistical shape model of the tibia as a reference surface. Responsiveness was assessed at 1 year using standardised response means (SRMs) for 4 constructs: meniscal volume, extrusion volume, thickness and tibial coverage; anatomical sub-regions of these constructs were also explored. Results: Paired images from 86 participants (median age 61.5, 49% female, 56% obese) were included. Reliability of the novel meniscal measurements was very good (ICCs all &gt; 0.98). Meniscal volume and extrusion demonstrated no significant change. Moderate responsiveness was observed for medial meniscus thickness (SRM -0.35) and medial tibial coverage (SRM - 0.36). No substantial change was seen for the lateral meniscus measures. Sub-region analysis did not improve responsiveness; while greater change was seen in the posterior medial compartment, it was associated with increased variance of the change. Conclusions: The location of meniscal damage was consistently in the posterior medial region, and two measurements (thickness and tibial coverage) were most responsive. Meniscal measures should add to discriminatory power in OA progression assessment

Baryogenesis with Scalar Bilinears

We show that if a baryon asymmetry of the universe is generated through the out-of-equilibrium decays of heavy scalar bilinears coupling to two fermions of the minimal standard model, it is necessarily an asymmetry conserving $(B-L)$ which cannot survive past the electroweak phase transition because of sphalerons. We then show that a surviving $(B-L)$ asymmetry may be generated if the heavy scalars decay into two fermions, \underline {and into two light scalars} (which may be detectable at hadron colliders). We list all possible such trilinear scalar interactions, and discuss how our new baryogenesis scenario may occur naturally in supersymmetric grand unified theories.Comment: LATEX, 14 pages, one figure include

Osteoarthritic bone marrow lesions almost exclusively colocate with denuded cartilage: a 3D study using data from the Osteoarthritis Initiative

Objectives: The aetiology of bone marrow lesions (BMLs) in knee osteoarthritis (OA) is poorly understood. We employed three-dimensional (3D) active appearance modelling (AAM) to study the spatial distribution of BMLs in an OA cohort and compare this with the distribution of denuded cartilage. Methods: Participants were selected from the Osteoarthritis Initiative progressor cohort with Kellgren–Lawrence scores ≥2, medial joint space narrowing and osteophytes. OA and ligamentous BMLs and articular cartilage were manually segmented. Bone surfaces were automatically segmented by AAM. Cartilage thickness of <0.5 mm was defined as denuded and ≥0.5–1.5 mm as severely damaged. Non-quantitative assessment and 3D population maps were used for analysing the comparative position of BMLs and damaged cartilage. Results: 88 participants were included, 45 men, mean age (SD) was 61.3 (9.9) years and mean body mass index was 31.1 (4.6) kg/m2. 227 OA and 107 ligamentous BMLs were identified in 86.4% and 73.8% of participants; OA BMLs were larger. Denuded cartilage was predominantly confined to a central region on the medial femur and tibia, and the lateral facet of the trochlear femur. 67% of BMLs were colocated with denuded cartilage and a further 21% with severe cartilage damage. In the remaining 12%, 25/28 were associated with cartilage defects. 74% of all BMLs were directly opposing (kissing) another BML across the joint. Conclusions: There was an almost exclusive relationship between the location of OA BML and cartilage denudation, which itself had a clear spatial pattern. We propose that OA, ligamentous and traumatic BMLs represent a bone response to abnormal loading

Machine-learning, MRI bone shape and important clinical outcomes in osteoarthritis: data from the Osteoarthritis Initiative

Objectives: Osteoarthritis (OA) structural status is imperfectly classified using radiographic assessment. Statistical shape modelling (SSM), a form of machine-learning, provides precise quantification of a characteristic 3D OA bone shape. We aimed to determine the benefits of this novel measure of OA status for assessing risks of clinically important outcomes. Methods: The study used 4796 individuals from the Osteoarthritis Initiative cohort. SSM-derived femur bone shape (B-score) was measured from all 9433 baseline knee MRIs. We examined the relationship between B-score, radiographic Kellgren-Lawrence grade (KLG) and current and future pain and function as well as total knee replacement (TKR) up to 8 years. Results: B-score repeatability supported 40 discrete grades. KLG and B-score were both associated with risk of current and future pain, functional limitation and TKR; logistic regression curves were similar. However, each KLG included a wide range of B-scores. For example, for KLG3, risk of pain was 34.4 (95% CI 31.7 to 37.0)%, but B-scores within KLG3 knees ranged from 0 to 6; for B-score 0, risk was 17.0 (16.1 to 17.9)% while for B-score 6, it was 52.1 (48.8 to 55.4)%. For TKR, KLG3 risk was 15.3 (13.3 to 17.3)%; while B-score 0 had negligible risk, B-score 6 risk was 35.6 (31.8 to 39.6)%. Age, sex and body mass index had negligible effects on association between B-score and symptoms. Conclusions: B-score provides reader-independent quantification using a single time-point, providing unambiguous OA status with defined clinical risks across the whole range of disease including pre-radiographic OA. B-score heralds a step-change in OA stratification for interventions and improved personalised assessment, analogous to the T-score in osteoporosis

The relationship between clinical characteristics, radiographic osteoarthritis and 3D bone area: data from the Osteoarthritis Initiative

Background Radiographic measures of osteoarthritis (OA) are based upon two dimensional projection images. Active appearance modelling (AAM) of knee magnetic resonance imaging (MRI) enables accurate, 3D quantification of joint structures in large cohorts. This cross-sectional study explored the relationship between clinical characteristics, radiographic measures of OA and 3D bone area (tAB). Methods Clinical data and baseline paired radiographic and MRI data, from the medial compartment of one knee of 2588 participants were obtained from the NIH Osteoarthritis Initiative (OAI). The medial femur (MF) and tibia (MT) tAB were calculated using AAM. ‘OA-attributable’ tAB (OA-tAB) was calculated using data from regression models of tAB of knees without OA. Associations between OA-tAB and radiographic measures of OA were investigated using linear regression. Results In univariable analyses, height, weight, and age in female knees without OA explained 43.1%, 32.1% and 0.1% of the MF tAB variance individually and 54.4% when included simultaneously in a multivariable model. Joint space width (JSW), osteophytes and sclerosis explained just 5.3%, 14.9% and 10.1% of the variance of MF OA-tAB individually and 17.4% when combined. Kellgren Lawrence (KL) grade explained approximately 20% of MF OA-tAB individually. Similar results were seen for MT OA-tAB. Conclusion Height explained the majority of variance in tAB, confirming an allometric relationship between body and joint size. Radiographic measures of OA, derived from a single radiographic projection, accounted for only a small amount of variation in 3D knee OA-tAB. The additional structural information provided by 3D bone area may explain the lack of a substantive relationship with these radiographic OA measures

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio