ArticleSpine deformity2026
Tell me your triradiate is closed without telling me it's closed: assessment of surrogate radiographic markers for prediction of triradiate closure.
Article in Spine deformity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionClosure of the triradiate cartilage (TRC) is an important skeletal maturity marker indicating the decline of peak growth in patients with adolescent idiopathic scoliosis (AIS). Our goal was to use surrogate maturity markers such as Risser and Sanders bone age to predict TRC closure.
methodsWe analyzed a prospectively collected multicenter cohort of patients with nonoperatively treated AIS from 5 centers. The cohort included 541 patients (393 [72.6%] female) with 1240 radiographic assessments. TRC status was categorized as open, closing, or closed. Risser stage, Sanders stage, and menarchal status were assessed at each encounter.
resultsSpearman rank correlation showed correlation between TRC status and other maturity metrics such as Risser (0.522; p<0.001), Sanders (0.582; p<0.001), and menarche (0.428; p<0.001). Only 1.6% of patients with open TRC were >Sanders 3. 76.6% of patients had completely closed TRC at ≥Sanders 4. Using this cutoff, the sensitivity of Sanders was 0.764 and the specificity was 0.774 (PPV=0.873; NPV=0.617.) Multivariable prediction demonstrated increasing probabilities of TRC closure with advancing Risser and Sanders stages, exceeding 90% for most observed combinations at Sanders stages 6 and 7. In females, a composite score of 2 or more characteristics (postmenarchal, Risser >0, Sanders >3) optimized both specificity (0.832) and sensitivity (0.716) for correctly predicting a closed TRC (PPV=0.864; NPV=0.663).
conclusionTRC closure can be predicted using routinely assessed skeletal maturity markers. Combining Risser and Sanders stages provides clinically interpretable probabilities of TRC closure, while the presence of ≥2 maturity characteristics in females optimizes predictive accuracy.
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