OPTIModel scoring for metastatic bone tumors: A data-driven tool for surgical planning and prognosis

Objectives: This study aims to evaluate the predictive accuracy and clinical applicability of the OPTIModel Scoring System in Turkish patients with long bone diaphyseal metastases and to compare its survival predictions and surgical recommendations with real-world outcomes. Patients and methods: Between April 2013 and June 2021, a total of 108 patients (52 males, 56 females; mean age: 64.5±10.6 years; range, 45 to 83 years) with histologically confirmed metastatic bone disease affecting the diaphysis of long bones and who underwent surgical treatment were retrospectively analyzed. The OPTIModel Scoring System was applied to estimate survival and guide surgical planning. The prognostic accuracy of the model was statistically analyzed. The patients were stratified into low-, intermediate-, and high-risk groups based on their predicted survival. Results: The most common primary tumors were breast (24.1%), lung (23.1%), and multiple myeloma (13.0%). The femur (46.3%) was the most frequently affected site. The OPTIModel-predicted median survival was 10 months (95% confidence interval [CI]: 7.9-12.1), comparable to the real-world median survival of 13 months (95% CI: 10.8-15.2). Agreement between OPTIModel’s surgical recommendations and real-world procedures was substantial (kappa [κ]=0.74, 95% CI: 0.63-0.85, p<0.001). The Kaplan-Meier analysis revealed no significant difference between predicted and real-world survival distributions (p=0.126). The ROC analysis yielded an area under the curve [AUC] of 0.61 (95% CI: 0.52-0.70), indicating moderate prognostic accuracy. Conclusion: The OPTIModel Scoring System demonstrated substantial agreement with real-world surgical decisions and moderate accuracy in survival prediction for Turkish patients with metastatic long bone diaphyseal tumors. While the model provides valuable insights for clinical decision-making, further refinements incorporating emerging oncological advancements and biomarker-based risk stratification are needed. Future multi-ethnic validation studies are essential to enhance its applicability in diverse populations.