{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T05:03:53Z","timestamp":1789967033043,"version":"4.0.1"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:p>\n                    Although prediction of radiographic knee osteoarthritis (rKOA) using baseline clinical and radiological data has been widely studied, the impact of temporal changes in imaging and clinical biomarkers remains less investigated. This study examined whether longitudinal changes in spatially resolved trabecular bone texture (TBT) improve prediction of incident rKOA. Participants with longitudinal data were selected from the Osteoarthritis Initiative. Incident rKOA was defined as Kellgren\u2013Lawrence grade (KL) 0 at baseline and KL \u2265 2 at 48 months. One knee per participant was included (n=481). Fractal-based TBT descriptors were extracted from 16 automatically defined regions of interest across the tibial subchondral bone. Five logistic-regression models combined baseline clinical and radiological covariates, baseline TBT, and 24-month longitudinal changes (\u0394TBT). Bidirectional stepwise selection based on the Akaike Information Criterion was applied once to TBT descriptors, whereas clinical and radiological covariates were incorporated\n                    <jats:italic>a priori<\/jats:italic>\n                    . The resulting fixed model specifications were evaluated using stratified 10-fold cross-validation repeated 300 times. Performance was assessed using area under the ROC curve (AUC), F1 score, balanced accuracy, positive and negative predictive values, and DeLong comparisons with the reference model. Adding baseline TBT to the clinical and radiographic reference model increased AUC, although not significantly (0.64 versus 0.53; p=0.055). Models incorporating \u0394TBT yielded the highest apparent discrimination (AUC 0.76, 95% CI 0.70\u20130.82; p&amp;lt;0.001 versus the reference model), but threshold-dependent performance remained modest, with maximum balanced accuracy of 0.56, PPV of 0.22, and F1 score of 0.21. Selected baseline and longitudinal TBT descriptors were distributed across medial and lateral compartments, including regions near the central axis and subchondral cartilage. In this exploratory analysis, models incorporating spatially resolved 24-month TBT changes showed higher apparent ranking discrimination than the clinical and radiographic reference model. However, classification at the 0.5 threshold remained limited, and AUC improvement may be optimistic because feature selection preceded cross-validation. These findings support further investigation of \u0394TBT as a candidate imaging marker but do not establish clinical utility. Leakage-free internal and external validation across independent cohorts and imaging systems are required.\n                  <\/jats:p>","DOI":"10.3389\/fphys.2026.1904598","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T04:29:17Z","timestamp":1789964957000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatially resolved longitudinal changes in tibial subchondral trabecular bone texture for exploratory prediction of incident knee osteoarthritis"],"prefix":"10.3389","volume":"17","author":[{"given":"Ahmad","family":"Almhdie-Imjabbar","sequence":"first","affiliation":[{"name":"Plateforme Recherche Innovation M\u00e9dicale Mutualis\u00e9e d'Orl\u00e9ans (PRIMMO), University Hospital Center of Orleans","place":["Orleans, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hechmi","family":"Toumi","sequence":"additional","affiliation":[{"name":"Plateforme Recherche Innovation M\u00e9dicale Mutualis\u00e9e d'Orl\u00e9ans (PRIMMO), University Hospital Center of Orleans","place":["Orleans, France"]},{"name":"Department of Rheumatology, University Hospital Center","place":["Orleans, France"]},{"name":"Faculty of Sciences, Technology, University of Orleans","place":["Orleans, France"]},{"name":"Laboratoire des Adaptations M\u00e9taboliques \u00e0 l\u2019Exercice en Conditions Physiologiques et Pathologiques (AME2P), Universit\u00e9 Clermont Auvergne (UCA)","place":["Aubi\u00e8re, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Lespessailles","sequence":"additional","affiliation":[{"name":"Plateforme Recherche Innovation M\u00e9dicale Mutualis\u00e9e d'Orl\u00e9ans (PRIMMO), University Hospital Center of Orleans","place":["Orleans, France"]},{"name":"Department of Rheumatology, University Hospital Center","place":["Orleans, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Trans. 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