Wear patterns in knee OA correlate with native limb geometry
Background: To date, the amount of cartilage loss is graded by means of discrete scoring systems on artificially divided regions of interest (ROI). However, optimal statistical comparison between and within populations requires anatomically standardized cartilage thickness assessment. Providing anat...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2022.1042441/full |
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| author | A. Van Oevelen A. Van Oevelen A. Van Oevelen I. Van den Borre K. Duquesne K. Duquesne A. Pizurica J. Victor J. Victor N. Nauwelaers N. Nauwelaers P. Claes P. Claes P. Claes P. Claes E. Audenaert E. Audenaert E. Audenaert E. Audenaert |
| author_facet | A. Van Oevelen A. Van Oevelen A. Van Oevelen I. Van den Borre K. Duquesne K. Duquesne A. Pizurica J. Victor J. Victor N. Nauwelaers N. Nauwelaers P. Claes P. Claes P. Claes P. Claes E. Audenaert E. Audenaert E. Audenaert E. Audenaert |
| author_sort | A. Van Oevelen |
| collection | DOAJ |
| description | Background: To date, the amount of cartilage loss is graded by means of discrete scoring systems on artificially divided regions of interest (ROI). However, optimal statistical comparison between and within populations requires anatomically standardized cartilage thickness assessment. Providing anatomical standardization relying on non-rigid registration, we aim to compare morphotypes of a healthy control cohort and virtual reconstructed twins of end-stage knee OA subjects to assess the shape-related knee OA risk and to evaluate possible correlations between phenotype and location of cartilage loss.Methods: Out of an anonymized dataset provided by the Medacta company (Medacta International SA, Castel S. Pietro, CH), 798 end-stage knee OA cases were extracted. Cartilage wear patterns were observed by computing joint space width. The three-dimensional joint space width data was translated into a two-dimensional pixel image, which served as the input for a principal polynomial autoencoder developed for non-linear encoding of wear patterns. Virtual healthy twin reconstruction enabled the investigation of the morphology-related risk for OA requiring joint arthroplasty.Results: The polynomial autoencoder revealed 4 dominant, orthogonal components, accounting for 94% of variance in the latent feature space. This could be interpreted as medial (54.8%), bicompartmental (25.2%) and lateral (9.1%) wear. Medial wear was subdivided into anteromedial (11.3%) and posteromedial (10.4%) wear. Pre-diseased limb geometry had a positive predictive value of 0.80 in the prediction of OA incidence (r 0.58, p < 0.001).Conclusion: An innovative methodological workflow is presented to correlate cartilage wear patterns with knee joint phenotype and to assess the distinct knee OA risk based on pre-diseased lower limb morphology. Confirming previous research, both alignment and joint geometry are of importance in knee OA disease onset and progression. |
| first_indexed | 2024-04-12T07:41:05Z |
| format | Article |
| id | doaj.art-9170c98c7e1d4a51ae4b69110d12d17b |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-04-12T07:41:05Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-9170c98c7e1d4a51ae4b69110d12d17b2022-12-22T03:41:49ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852022-11-011010.3389/fbioe.2022.10424411042441Wear patterns in knee OA correlate with native limb geometryA. Van Oevelen0A. Van Oevelen1A. Van Oevelen2I. Van den Borre3K. Duquesne4K. Duquesne5A. Pizurica6J. Victor7J. Victor8N. Nauwelaers9N. Nauwelaers10P. Claes11P. Claes12P. Claes13P. Claes14E. Audenaert15E. Audenaert16E. Audenaert17E. Audenaert18Department of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, BelgiumDepartment of Human Structure and Repair, Ghent University, Ghent, BelgiumDepartment of Electromechanics, InViLab Research Group, University of Antwerp, Antwerp, BelgiumTELIN-GAIM, Faculty of Engineering and Architecture, Ghent University, Ghent, BelgiumDepartment of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, BelgiumDepartment of Human Structure and Repair, Ghent University, Ghent, BelgiumTELIN-GAIM, Faculty of Engineering and Architecture, Ghent University, Ghent, BelgiumDepartment of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, BelgiumDepartment of Human Structure and Repair, Ghent University, Ghent, BelgiumMedical Imaging Research Center, MIRC, University Hospitals Leuven, Leuven, BelgiumDepartment of Electrical Engineering, ESAT/PSI KU Leuven, Leuven, BelgiumMedical Imaging Research Center, MIRC, University Hospitals Leuven, Leuven, BelgiumDepartment of Electrical Engineering, ESAT/PSI KU Leuven, Leuven, BelgiumDepartment of Human Genetics, KU Leuven, Leuven, BelgiumMurdoch Childrens Research Institute, Royal Children’s Hospital, Melbourne, VIC, AustraliaDepartment of Orthopedic Surgery and Traumatology, Ghent University Hospital, Ghent, BelgiumDepartment of Human Structure and Repair, Ghent University, Ghent, BelgiumDepartment of Electromechanics, InViLab Research Group, University of Antwerp, Antwerp, BelgiumDepartment of Trauma and Orthopedics, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United KingdomBackground: To date, the amount of cartilage loss is graded by means of discrete scoring systems on artificially divided regions of interest (ROI). However, optimal statistical comparison between and within populations requires anatomically standardized cartilage thickness assessment. Providing anatomical standardization relying on non-rigid registration, we aim to compare morphotypes of a healthy control cohort and virtual reconstructed twins of end-stage knee OA subjects to assess the shape-related knee OA risk and to evaluate possible correlations between phenotype and location of cartilage loss.Methods: Out of an anonymized dataset provided by the Medacta company (Medacta International SA, Castel S. Pietro, CH), 798 end-stage knee OA cases were extracted. Cartilage wear patterns were observed by computing joint space width. The three-dimensional joint space width data was translated into a two-dimensional pixel image, which served as the input for a principal polynomial autoencoder developed for non-linear encoding of wear patterns. Virtual healthy twin reconstruction enabled the investigation of the morphology-related risk for OA requiring joint arthroplasty.Results: The polynomial autoencoder revealed 4 dominant, orthogonal components, accounting for 94% of variance in the latent feature space. This could be interpreted as medial (54.8%), bicompartmental (25.2%) and lateral (9.1%) wear. Medial wear was subdivided into anteromedial (11.3%) and posteromedial (10.4%) wear. Pre-diseased limb geometry had a positive predictive value of 0.80 in the prediction of OA incidence (r 0.58, p < 0.001).Conclusion: An innovative methodological workflow is presented to correlate cartilage wear patterns with knee joint phenotype and to assess the distinct knee OA risk based on pre-diseased lower limb morphology. Confirming previous research, both alignment and joint geometry are of importance in knee OA disease onset and progression.https://www.frontiersin.org/articles/10.3389/fbioe.2022.1042441/fullstatistical shape analysisknee diagnostic imagingosteoarthritisalignmentknee wear |
| spellingShingle | A. Van Oevelen A. Van Oevelen A. Van Oevelen I. Van den Borre K. Duquesne K. Duquesne A. Pizurica J. Victor J. Victor N. Nauwelaers N. Nauwelaers P. Claes P. Claes P. Claes P. Claes E. Audenaert E. Audenaert E. Audenaert E. Audenaert Wear patterns in knee OA correlate with native limb geometry Frontiers in Bioengineering and Biotechnology statistical shape analysis knee diagnostic imaging osteoarthritis alignment knee wear |
| title | Wear patterns in knee OA correlate with native limb geometry |
| title_full | Wear patterns in knee OA correlate with native limb geometry |
| title_fullStr | Wear patterns in knee OA correlate with native limb geometry |
| title_full_unstemmed | Wear patterns in knee OA correlate with native limb geometry |
| title_short | Wear patterns in knee OA correlate with native limb geometry |
| title_sort | wear patterns in knee oa correlate with native limb geometry |
| topic | statistical shape analysis knee diagnostic imaging osteoarthritis alignment knee wear |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2022.1042441/full |
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