Contemporary Tumor Histology Grouping for Survival Prediction of Patients With Metastatic Long-bone Disease Undergoing Surgery

Background: The prevalence of metastatic bone disease continues to rise with advances in cancer diagnostics and systemic therapy. Accurate survival prediction remains essential for surgical decision-making near the end of life. The Katagiri score is a widely used tool incorporating primary tumor histology grouped by expected growth rate. However, it was developed using 2005–2008 data from mainly non-surgical, spinal metastasis patients. As oncologic treatments evolve, tumor histology groupings require reassessment. This study investigated the impact of primary tumor histology on postoperative survival in patients with metastatic long-bone disease. Methods: We retrospectively included 460 patients who underwent surgery for long-bone metastases at two affiliated tertiary institutions between 2016 and 2021. The primary outcome was postoperative survival. Tumor types were classified into three survival groups—better, intermediate, and worse—by setting thresholds at the 33rd and 66th percentiles of coefficients derived from multivariable Cox proportional hazards regression. Results: Median survival was 16.5 months (interquartile ranges IQR, 4.7 to 37.9) for better-survival tumors, 9.2 months (IQR, 2.6 to 20.6) for intermediate, and 3.9 months (IQR, 1.6 to 11.6) for worse. Survival improved significantly in tumors responsive to modern systemic therapies, particularly immunotherapy, including renal cell carcinoma, melanoma, hepatocellular carcinoma, and colorectal carcinoma. Gynecological malignancies demonstrated the poorest outcomes. Discussion: This contemporary, histology-based classification reflects modern oncologic outcomes and provides a practical prognostic tool for clinicians treating patients with extremity metastases. Incorporating this updated grouping into prediction models may enhance accuracy and clinical utility. These findings emphasize the importance of regularly auditing prognostic models to remain aligned with evolving cancer therapies and survival trends.