Towards automatic scoring of spinal x-ray for ankylosing spondylitis

Manually grading structural changes with the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) on spinal X-ray imaging is costly and timeconsuming due to bone shape complexity and image quality variations. In this study, we address this challenge by prototyping a 2-step auto-grading pipeline, called VertXGradeNet, to automatically predict mSASSS scores for the cervical and lumbar vertebral units (VUs) in X-ray spinal imaging. The VertXGradeNet utilizes VUs generated by our previously developed VU extraction pipeline (VertXNet) as input and predicts mSASSS based on those VUs. VertXGradeNet was evaluated on an in-house dataset of lateral cervical and lumbar X-ray images for axial spondylarthritis patients. Our results show that VertXGradeNet can predict the mSASSS score for each VU when the data is limited in quantity and imbalanced. Overall, it can achieve a balanced accuracy of 0.56 and 0.51 for 4 different mSASSS scores (i.e., a score of 0, 1, 2, 3) on two test datasets. The accuracy of the presented method shows the potential to streamline the spinal radiograph readings and therefore reduce the cost of future clinical trials

VertXNet: Automatic Segmentation and Identification of Lumbar and Cervical Vertebrae from Spinal X-ray Images

Manual annotation of vertebrae on spinal X-ray imaging is costly and time-consuming due to bone shape complexity and image quality variations. In this study, we address this challenge by proposing an ensemble method called VertXNet, to automatically segment and label vertebrae in X-ray spinal images. VertXNet combines two state-of-the-art segmentation models, namely U-Net and Mask R-CNN to improve vertebrae segmentation. A main feature of VertXNet is to also infer vertebrae labels thanks to its Mask R-CNN component (trained to detect 'reference' vertebrae) on a given spinal X-ray image. VertXNet was evaluated on an in-house dataset of lateral cervical and lumbar X-ray imaging for ankylosing spondylitis (AS) patients. Our results show that VertXNet can accurately label spinal X-rays (mean Dice of 0.9). It can be used to circumvent the lack of annotated vertebrae without requiring human expert review. This step is crucial to investigate clinical associations by solving the lack of segmentation, a common bottleneck for most computational imaging projects

VertXNet: an ensemble method for vertebral body segmentation and identification from cervical and lumbar spinal X-rays

Accurate annotation of vertebral bodies is crucial for automating the analysis of spinal X-ray images. However, manual annotation of these structures is a laborious and costly process due to their complex nature, including small sizes and varying shapes. To address this challenge and expedite the annotation process, we propose an ensemble pipeline called VertXNet. This pipeline currently combines two segmentation mechanisms, semantic segmentation using U-Net, and instance segmentation using Mask R-CNN, to automatically segment and label vertebral bodies in lateral cervical and lumbar spinal X-ray images. VertXNet enhances its effectiveness by adopting a rule-based strategy (termed the ensemble rule) for effectively combining segmentation outcomes from U-Net and Mask R-CNN. It determines vertebral body labels by recognizing specific reference vertebral instances, such as cervical vertebra 2 (‘C2’) in cervical spine X-rays and sacral vertebra 1 (‘S1’) in lumbar spine X-rays. Those references are commonly relatively easy to identify at the edge of the spine. To assess the performance of our proposed pipeline, we conducted evaluations on three spinal X-ray datasets, including two in-house datasets and one publicly available dataset. The ground truth annotations were provided by radiologists for comparison. Our experimental results have shown that the proposed pipeline outperformed two state-of-the-art (SOTA) segmentation models on our test dataset with a mean Dice of 0.90, vs. a mean Dice of 0.73 for Mask R-CNN and 0.72 for U-Net. We also demonstrated that VertXNet is a modular pipeline that enables using other SOTA model, like nnU-Net to further improve its performance. Furthermore, to evaluate the generalization ability of VertXNet on spinal X-rays, we directly tested the pre-trained pipeline on two additional datasets. A consistently strong performance was observed, with mean Dice coefficients of 0.89 and 0.88, respectively. In summary, VertXNet demonstrated significantly improved performance in vertebral body segmentation and labeling for spinal X-ray imaging. Its robustness and generalization were presented through the evaluation of both in-house clinical trial data and publicly available datasets

Secukinumab improves active psoriatic arthritis symptoms and inhibits radiographic progression: primary results from the randomised, double-blind, phase III FUTURE 5 study.

OBJECTIVES: To evaluate the effect of subcutaneous (s.c.) secukinumab, an interleukin-17A inhibitor, on clinical signs and symptoms and radiographic progression in patients with psoriatic arthritis (PsA). METHODS: Adults (n=996) with active PsA were randomised 2:2:2:3 to s.c. secukinumab 300 mg or 150 mg with loading dose (LD), 150 mg without LD or placebo. All groups received secukinumab or placebo at baseline, weeks 1, 2 and 3 and then every 4 weeks from week 4. The primary endpoint was the proportion of patients achieving an American College of Rheumatology 20 (ACR20) response at week 16. RESULTS: Significantly more patients achieved an ACR20 response at week 16 with secukinumab 300 mg with LD (62.6%), 150 mg with LD (55.5%) or 150 mg without LD (59.5%) than placebo (27.4%) (p CONCLUSION: S.c. secukinumab 300 mg and 150 mg with and without LD significantly improved clinical signs and symptoms and inhibited radiographic structural progression versus placebo at week 24 in patients with PsA. TRIAL REGISTRATION NUMBER: NCT02404350; Results

Secukinumab provides sustained low rates of radiographic progression in psoriatic arthritis: 52-week results from a phase 3 study, FUTURE 5.

OBJECTIVE: To evaluate the effect of secukinumab on radiographic progression through 52 weeks in patients with PsA from the FUTURE 5 study. METHODS: Patients with active PsA, stratified by prior anti-TNF use (naïve or inadequate response), were randomized to s.c. secukinumab 300 mg load (300 mg), 150 mg load (150 mg), 150 mg no load regimens or placebo at baseline, at weeks 1, 2 and 3 and every 4 weeks starting at week 4. Radiographic progression was assessed by change in van der Heijde-modified total Sharp score (vdH-mTSS; mean of two readers). Statistical analysis used a linear mixed-effects model (random slope) at weeks 24 and 52, and observed data at week 52. Assessments at week 52 included additional efficacy endpoints (non-responders imputation and mixed-effects models for repeated measures) and safety. RESULTS: The majority (86.6%) of patients completed 52 weeks of treatment. The proportion of patients with no radiographic progression (change from baseline in vdH-mTSS ⩽0.5) was 91.8, 85.2 and 87.2% in 300, 150 and 150 mg no load groups, respectively, at week 52. The change in vdH-mTSS from baseline to week 52 using random slope [mean change (s.e.)] was -0.18 (0.17), 0.11 (0.18) and -0.20 (0.18) in 300, 150 and 150 mg no load groups, respectively; the corresponding observed data [mean change (s.d.)] was -0.09 (1.02), 0.13 (1.39) and 0.21 (1.15). Clinical efficacy endpoints were sustained, and no new or unexpected safety signals were reported through 52 weeks. CONCLUSION: Secukinumab 300 and 150 mg with or without s.c. loading regimen provided sustained low rates of radiographic progression through 52 weeks of treatment. TRIAL REGISTRATION: ClinicalTrials.gov, http://clinicaltrials.gov, NCT02404350

Response to anti-IL17 therapy in inflammatory disease is not strongly impacted by genetic background

Response to the anti-IL17 monoclonal antibody secukinumab is heterogeneous, and not all participants respond to treatment. Understanding whether this heterogeneity is driven by genetic variation is a key aim of pharmacogenetics and could influence precision medicine approaches in inflammatory diseases. Using changes in disease activity scores across 5,218 genotyped individuals from 19 clinical trials across four indications (psoriatic arthritis, psoriasis, ankylosing spondylitis, and rheumatoid arthritis), we tested whether genetics predicted response to secukinumab. We did not find any evidence of association between treatment response and common variants, imputed HLA alleles, polygenic risk scores of disease susceptibility, or cross-disease components of shared genetic risk. This suggests that anti-IL17 therapy is equally effective regardless of an individual’s genetic background, a finding that has important implications for future genetic studies of biological therapy response in inflammatory diseases

Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis

Background Novartis and the University of Oxford’s Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression. Method The collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities. For the Multiple Sclerosis (MS) project, the collaboration has anonymised and integrated phase II to phase IV clinical and imaging trial data from ≈35,000 patients across all clinical phenotypes and collected in more than 2200 centres worldwide. For the “IL-17” project, the collaboration has anonymised and integrated clinical and imaging data from over 30 phase II and III Cosentyx clinical trials including more than 15,000 patients, suffering from four autoimmune disorders (Psoriasis, Axial Spondyloarthritis, Psoriatic arthritis (PsA) and Rheumatoid arthritis (RA)). Results A fundamental component of successful data analysis and the collaborative development of novel machine learning methods on these rich data sets has been the construction of a research informatics framework that can capture the data at regular intervals where images could be anonymised and integrated with the de-identified clinical data, quality controlled and compiled into a research-ready relational database which would then be available to multi-disciplinary analysts. The collaborative development from a group of software developers, data wranglers, statisticians, clinicians, and domain scientists across both organisations has been key. This framework is innovative, as it facilitates collaborative data management and makes a complicated clinical trial data set from a pharmaceutical company available to academic researchers who become associated with the project. Conclusions An informatics framework has been developed to capture clinical trial data into a pipeline of anonymisation, quality control, data exploration, and subsequent integration into a database. Establishing this framework has been integral to the development of analytical tools

Modified stoke ankylosing spondylitis spinal score as an outcome measure to assess the impact of treatment on structural progression in ankylosing spondylitis

In ankylosing spondylitis (AS), structural damage that occurs as a result of syndesmophyte formation and ankylosis of the vertebral column is irreversible. Structural damage is currently assessed by conventional radiography and scoring systems that reliably assess radiographic structural damage are needed to capture the differential effects of drugs on structural damage progression. The validity of the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) as a primary outcome measure in evaluating the effect of AS treatments on radiographic progression rates was assessed in this review. The mSASSS has not been used, to date, as a primary outcome measure in a prospective randomized controlled clinical trial of biologic therapy in AS. This review of the medical literature confirmed that the mSASSS is the most validated and widely used method for assessing radiographic progression in AS, correlating with worsening measures of disease signs and symptoms, spinal mobility and physical function, with a 2-year interval being required to ensure sufficient sensitivity to change

Efficacy, safety, and tolerability of secukinumab in patients with active ankylosing spondylitis: a randomized, double-blind phase 3 study, MEASURE 3

Abstract Background Secukinumab, an anti–interleukin-17A monoclonal antibody, improved the signs and symptoms of ankylosing spondylitis (AS) in two phase 3 studies (MEASURE 1 and MEASURE 2). Here, we present 52-week results from the MEASURE 3 study assessing the efficacy and safety of secukinumab 300 and 150 mg subcutaneous maintenance dosing, following an intravenous loading regimen. Methods A total of 226 patients were randomized to intravenous secukinumab 10 mg/kg (baseline, weeks 2 and 4) followed by subcutaneous secukinumab 300 mg (IV-300 mg) or 150 mg (IV-150 mg) every 4 weeks, or matched placebo. Patients in the placebo group were re-randomized to subcutaneous secukinumab at a dose of 300 or 150 mg at week 16. The primary endpoint was the Assessment of SpondyloArthritis international Society criteria for 20% improvement (ASAS20) response rate at week 16 in the IV-300 mg or IV-150 mg versus placebo. Other endpoints assessed through week 52 included improvements in ASAS40, ASAS 5/6, Bath Ankylosing Spondylitis Disease Activity Index, and ASAS partial remission responses, as well as the change from baseline in high-sensitivity C-reactive protein levels. Statistical analyses followed a predefined hierarchical hypothesis testing strategy to adjust for multiplicity of testing, with non-responder imputation used for binary variables and mixed-model repeated measures for continuous variables. Results The primary efficacy endpoint was met; the ASAS20 response rate was significantly greater at week 16 in the IV-300 mg (60.5%; P < 0.01) and IV-150 mg (58.1%; P < 0.05) groups versus placebo (36.8%). All secondary endpoints were met at week 16, except ASAS partial remission in the IV-150 mg group. Improvements achieved with secukinumab in all clinical endpoints at week 16 were also sustained at week 52. Infections, including candidiasis, were more common with secukinumab than with placebo during the placebo-controlled period. During the entire treatment period, pooled incidence rates of Candida infections and grade 3–4 neutropenia were 1.8% for both of these adverse events in secukinumab-treated patients. Conclusions Secukinumab (300 mg and 150 mg dose groups) provided rapid, significant and sustained improvement through 52 weeks in the signs and symptoms of patients with AS. The safety profile was consistent with previous reports, with no new or unexpected findings. Trial registration ClinicalTrials.gov, NCT02008916 . Registered on 8 December 2013. EUDRACT 2013-001090-24. Registered on 24 October 2013). The study was not retrospectively registered