JIA C. Mala*a, N. ter Haar, M. Vicecon6 WP5: JIA WP5 and WP10 - PowerPoint PPT Presentation

JIA C. Mala*a, N. ter Haar, M. Vicecon6

WP5: JIA WP5 and WP10

OBJECTIVES To develop a personalized image-based JOINT BIOMECHANICAL MODELING to explore how biomechanical changes may influence the spread of arthritis or structural damage progression. Brussels, 12th May 2016

POTENTIAL PREDICTORS Build a data repository containing (clinical, laboratory, imaging, immunological and microbiota characterization) that will be integrated to obtain a MULTIDIMENSIONAL PATIENT SPECIFIC MODEL of the disease course. DATA HAS GONE THROUGH THE DCV TOOL AND ARE CURRENTLY BEING PROCESSED BY KDD (WP16) Brussels, 12th May 2016

DATA COLLECTION PROGRESS VARIABLE N (%) 2015 N (%) 2016 SELF ASSESMENT PLAN Pa6ents enrolled 123 170 150-185 6 months FU 80 (65) 146 (86%) 12 months FU 33 (27) 101 (60%) 18 months FU 2 (2) 58 (34%) 24 months FU 0 29 (17%) Rou6ne lab 123 (100) 170 (100) Luminex blood 93 (76) 138 (81%) 130-150 Luminex synovial fluid 36 (29) 69 (41%) 35-50 Stools 83 (67) 120 (71%) 100-130 Ultrasound 112 (91) 157 (92%) 140-170 Brussels, 12th May 2016

DATA COLLECTION MRI & GCA MRI CGA Assessment plan Baseline 14 12 26 24 25-40 21 21 6 months 12 12 10 12 months 7 6 10 24 months 3 N/A 3 N/A • AutomaGc segmentaGon and modelling started – see presentaGon WP10 • Ankle MRI scoring system Brussels, 12th May 2016

SYNOVITIS DISTENSION OF THE JOINT RECESS DUE TO THE PRESENCE OF AN ENHANCING THICKENED SYNOVIUM. GRADE 1 GRADE 2 GRADE 3 Score 0 is normal; score 1-3 (mild, moderate, severe) are by thirds of the presumed max volume of the enhancing tissue in the synovial compartment. Brussels, 12th May 2016

RELIABILITY Weighted kappa Tibio.peroneo.talar 0,78 Talo-navicular 0,72 Subtalar-anterior 0,46 Subtalar posterior 0,78 Calcaneo-cuboid 0,88 Cuneo-navicular 0,67 Tarso metatarsal medial 0,71 Tarso metatarsal lateral 0,87 ICC: 0.93 (0.81-0.98) Brussels, 12th May 2016

TENOSYNOVITIS Tendon sheath thickening and enhancement aZer i.v contrast injecGon. K=0.72 K=0.86 K=0.9 Intra Class CorrelaGon Coefficient=0.97 (0.90-0.99) Brussels, 12th May 2016

BONE EROSION A sharply bone lesion, with correct juxta-articular localization which is visible in 2 planes. Bone erosion was scored from 0 to 10 by the volume of the erosion as a proportion of the “assessed bone volume” by 10% increments. Weighted kappa Distal Gbial epiphysis 0,54 Distal fibular epiphysis 0,68 Talus 0,50 Calcaneus 1 Navicular bone 0,61 Cubical bone 1 Medial cuneiform 0,61 ICC=0.77 (0.57-0.94) Brussels, 12th May 2016

BONE MARROW OEDEMA Each bone was scored separately using a 0–3 scale based on the proportion of bone with BMO as follows: 0, no BMO; 1, 1%–33% of bone with BMO; 2, 34%– 66% of bone with BMO; and 3, 67%–100% of bone with BMO. Weighted kappa Distal Gbial epiphysis 1 Distal fibular epiphysis 0,91 talus 0,81 calcaneus 0,80 Navicular bone 0,47 Cubical bone 0,85 medialcuneiform 0,70 Intermediate cuneiform 0,76 Lateral cuneiform 0,4 ICC= 0.83 (0.52-0.96) Brussels, 12th May 2016

CARTILAGE DAMAGE Irregularities and/or interruption of the cartilage surface 0= no cartilage damage; 1= cartilage damage involving 1%–33% of the cartilage surface; 2= damage involving 34%–66% of the cartilage surface; 3= damage involving 67%–100% of the cartilage surface; 4= ankylosis. ICC = 0.64 Brussels, 12th May 2016

BIOMARKERS Pa6ent Biomarkers MRI synoviGs Sex Age MRI tenosynoviGs Height (cm) Weight (kg) score BL BL BL BL M6 M12 M18 M24 BL M6 M12 M18 M24 Flexor and fibular IGG-RF F 13.5 10 163 164 164 163 N/A 53 64 62 63 N/A tendons Flexor and IGG-AP F 9.4 10 138 139 140 145 150 41 42 44 45 50 extensor tendons OPBG- Posterior Gbial M 12.1 3 149 154 152 N/A N/A 47 46 48 N/A N/A MT tendon Brussels, 12th May 2016

MODELLING OUTCOMES Pa6ent Outcomes CarGlage damage MRI damage Treatment response InacGve disease progression progression (ACR70) (Wallace criteria) Last vs. first Last vs. first M6 M12 M18 M24 M6 M12 M18 M24 observaGon observaGon IGG-RF No No Yes Yes Yes N/A Yes No Yes N/A IGG-AP No Yes No Yes Yes Yes No No Yes Yes OPBG-MT No No Yes Yes N/A N/A Yes No N/A N/A Brussels, 12th May 2016

Cytokine analysis • Baseline blood plasma: 138 individual paGents – IGG 62, UMCU 23, OPBG 53 paGents • Goal: find biomarkers/profiles that predict disease outcome – 78 analytes • Progress: – Samples are shipped from all centers – Luminex assay scheduled for 19 th of May – Data analysis done in June-July Brussels, 12th May 2016

Cytokine analysis: selected analytes receptors & MMPs & binding proteins alarmins cytokines chemokines other IL-1 bèta CCL2/MCP-1 IL-1RA MMP-1 OPN IL-6 CCL3/MIP-1 alpha IL-18BPa MMP-3 SclerosGn/SOST IL-10 CCL4/MIP-1 bèta IL-1RI MMP-8 Dkk1 IL-12 p70 CCL8/MCP-2 IL-1RII MMP-9 LepGn IL-13 CCL17/TARC TNF-RI S100A8/MRP8 ResisGn IL-15 CCL18/PARC TNF-RII TIMP-1 GM-CSF IL-17 CCL22/MDC sCD19 HSP70/HSPA1A Amphiregulin IL-17F CCL23/MPIF sIL-2R/sCD25 VimenGn NGF IL-18 CCL25/TECK sCD27 VEGF IL-22 CCL27/C-TACK sIL-6R/sCD126 sICAM IL-23 p19 CXCL5/ENA-78 IL-7R alpha sVCAM IL-25/17E CXCL8/IL-8 sVEGF-R1/Flt-1 IL-27 CXCL9/MIG sCD14 TNF alpha CXCL10/IP-10 CD40L/CD154 IFN alpha CXCL13/BLC IFN gamma LIGHT TWEAK MIF CHI3L1/YKL-40 TSLP LAP/TGF-1 Brussels, 12th May 2016 MIC-1/GFD15

Cytokine analysis: future assays • Cytokine assays for end 2016: – Blood plasma of inacGve paGents (now: 68 pt) • Biomarkers for predicGon which paGents will stay inacGve vs which ones will flare • Progress: sGll sampling inacGve paGents – Synovial fluid plasma (now: 73 pt) • Biomarkers for predicGon which paGents will get inacGve disease, and will respond well to intra-arGcular injecGon • Progress: baseline completed, sGll sampling paGents with persistent acGvity or flare Brussels, 12th May 2016

WP10: JIA modelling WP5 and WP10

Technical goals • Development of arGculated geometric models of JIA affected joints • PaGent-specific biomechanical models and simulaGons • MulG-dimensional modelling of the disease course • Reuse/adapt developed modelling tools (VPHOP & NMS Physiome) • ExtracGon of biomarkers (ideally automated) – Incorporate anatomical and modelling biomarkers into MDP database Brussels, 12th May 2016

OperaGonal goals – YR1 ü Agree and implement imaging and gait analysis data collecGon protocols ² Develop paGent-specific whole body musculoskeletal dynamics model ² Extend model to include detailed foot dynamics model Ø Start data collecGon – Quality assurance Ø Model few cases using manual processing for feasibility and validaGon Ø Automate data processing Ø Develop paGent-specific joint finite element model Ø Apply automated data processing to all paGents Ø Extract anatomical and funcGonal biomarkers Ø Generate full mulGscale paGent-specific models for all paGents Ø Extract biomechanics biomarkers Brussels, 12th May 2016

OperaGonal goals – Y2 ü Agree and implement imaging and gait analysis data collecGon protocols ü Develop paGent-specific whole body musculoskeletal dynamics model ü Extend model to include detailed foot dynamics model ü Start data collecGon – Quality assurance ü Model few cases using manual processing for feasibility and validaGon ² Automate data processing ² Develop paGent-specific joint finite element model Ø Apply automated data processing to all paGents Ø Extract anatomical and funcGonal biomarkers Ø Generate full mulGscale paGent-specific models for all paGents Ø Extract biomechanics biomarkers Brussels, 12th May 2016

Unexpected challenges in using MRI data • Successful automated segmentaGon of 42 bones, except pelvis and foot at month 6. • CarGlage layer at the ankle joint not clearly visible in the images, hence not suitable for FE modelling Brussels, 12th May 2016

From FE to Hertz contact model Brussels, 12th May 2016

Adopted countermeasures • Manual segmentaGon of feet and pelvis bones for month-6 data. • Reduced order model of arGcular contact (Hertzian contact model) to reduce sensiGvity to quality of input data. Brussels, 12th May 2016

OperaGonal goals – YR3 ü Agree and implement imaging and gait analysis data collecGon protocols ü Develop paGent-specific whole body musculoskeletal dynamics model ü Extend model to include detailed foot dynamics model ü Start data collecGon – Quality assurance ü Model few cases using manual processing for feasibility and validaGon ü Automated data processing (specific datasets processed manually) ü Develop paGent-specific joint model using Heartzian contact ² Generate full mulGscale paGent-specific models for all paGents ² Apply automated data processing to all paGents ² Extract anatomical and funcGonal biomarkers ² Extract biomechanics biomarkers Brussels, 12th May 2016

Year 3 main progresses • Improve adaptaGon strategies for automaGc segmentaGon • Increased number of training data sets • Models extended by integraGng other structures • Lower limb modelling procedures established and confirmed • Biomarker extracGon started Brussels, 12th May 2016

Ankle/Lower limbs geometrical models • AutomaGc segmentaGon of anatomical structures in MRI datasets (input for biomechanical modelling) 11 datasets for training 23 datasets for training Brussels, 12th May 2016