Postoperative Nomogram for 12-Year Sarcoma-Specific Death

• Main Authors: Kattan, M., Leung, Denis H. Y., Brennan, M. F.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2002
• Subjects: Econometrics; Medicine and Health Sciences
• Online Access: https://ink.library.smu.edu.sg/soe_research/338
• Institution: Singapore Management University

Purpose: Few published studies have analyzed risk factors for sarcoma-specific death. We developed and internally validated a nomogram that combines the factors to predict the probability of 12-year sarcoma-specific death using a database of 2,136 prospectively followed adult patients treated at a single institution. Patients and Methods: Nomogram predictor variables included age at diagnosis, tumor size (? 5, 5 to 10, or > 10 cm), histologic grade (high or low), histologic subtype (fibrosarcoma, leiomyosarcoma, liposarcoma, malignant fibrous histiocytoma, malignant peripheral nerve tumor, synovial, or other), depth (superficial or deep), and site (upper extremity, lower extremity, visceral, thoracic or trunk, retrointraabdominal, or head or neck). Death from sarcoma or treatment complication was the predicted end point. Three prediction methods were compared, Kaplan-Meier analysis of all possible subsets, recursive partitioning, and Cox proportional hazards regression analysis. The concordance index was used as an accuracy measure with bootstrapping to correct for optimistic bias. Results: Sarcoma-specific death at 12 years was 36% (95% confidence interval, 33% to 39%). The bootstrap-corrected concordance indices were as follows: Kaplan-Meier, 0.69; recursive partitioning, 0.74; and Cox regression, 0.77. A nomogram was drawn on the basis of the Cox regression model. This nomogram was internally validated using bootstrapping and shown to have excellent calibration. Conclusion: A nomogram has been developed to predict 12-year sarcoma-specific death. This tool may be useful for patient counseling, follow-up scheduling, and clinical trial eligibility determination.