ArticleAmerican journal of cancer research2026
Time-dependent evolution of osteosarcoma recurrence risk across clinical risk stratification and its prognostic value.
Article in American journal of cancer research, 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
backgroundPatients with osteosarcoma remain at high risk of recurrence after surgery despite multimodal treatment, and previously developed prediction models have rarely considered the time-varying effects of prognostic factors.
methodsWe retrospectively analyzed 498 patients with osteosarcoma who received standard treatment at two centres between 2010 and 2019, including a training cohort (n = 341) and an external validation cohort (n = 157). The proportional hazards assumption was assessed using Schoenfeld residuals. For variables with time-dependent effects, piecewise Cox regression models were used. A dynamic prediction model was developed and evaluated using the concordance index, time-dependent receiver operating characteristic curves, calibration curves, and decision curve analysis.
resultsDuring a median follow-up of 52.0 months, 202 patients (40.6%) developed postoperative recurrence, with most recurrences occurring within 12.0 months after surgery. Multivariable analysis showed that axial location, tumor diameter ≥ 10 cm, Huvos grade I-II, positive surgical margins, and incomplete adjuvant chemotherapy were independent risk factors. Time-dependent analyses showed that the effects of these covariates were strongest during 12.0-24.0 months postoperatively and then declined, becoming non-significant beyond 36.0 months in most cases. The one-, three-, and five-year area under the curve values were 0.805, 0.734, and 0.923 in the training cohort and 0.860, 0.775, and 0.890 in the validation cohort, respectively. Risk scores stratified patients into low-, intermediate-, and high-risk groups, with significantly different recurrence-free survival (P < 0.0001).
conclusionThis dynamic prediction model showed good discriminatory ability and generalizability and may support risk-adapted surveillance, especially within 12-24 months after surgery.
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