External validation and adaptation of a dynamic prediction model for patients with high‐grade extremity soft tissue sarcoma

• Published in: Journal of surgical oncology Vol. 123; no. 4; pp. 1050 - 1056
• Main Authors: Rueten‐Budde, Anja J., Praag, Veroniek M., Sande, Michiel A. J., Fiocco, Marta
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.03.2021; John Wiley and Sons Inc
• Subjects: dynamic prediction; external validation; Extremities - pathology; Extremities - surgery; Female; Follow-Up Studies; Humans; landmark analysis; Male; Melanoma/Sarcoma; Middle Aged; Models, Statistical; Neoplasm Grading; Neoplasm Recurrence, Local - mortality; Neoplasm Recurrence, Local - pathology; Neoplasm Recurrence, Local - surgery; Nomograms; Patients; Retrospective Studies; Sarcoma; Sarcoma - mortality; Sarcoma - pathology; Sarcoma - surgery; soft tissue sarcoma; survival; Survival Rate; external validation; dynamic prediction; soft tissue sarcoma; landmark analysis; survival
• ISSN: 0022-4790; 1096-9098
• DOI: 10.1002/jso.26337

Background and Objectives A dynamic prediction model for patients with soft tissue sarcoma of the extremities was previously developed to predict updated overall survival probabilities throughout patient follow‐up. This study updates and externally validates the dynamic model. Methods Data from 3826 patients with high‐grade extremity soft tissue sarcoma, treated surgically with curative intent were used to update the dynamic PERsonalised SARcoma Care (PERSARC) model. Patients were added to the model development cohort and grade was included in the model. External validation was performed with data from 1111 patients treated at a single tertiary center. Results Calibration plots show good model calibration. Dynamic C‐indices suggest that the model can discriminate between high‐ and low‐risk patients. The dynamic C‐indices at 0, 1, 2, 3, 4, and 5 years after surgery were equal to 0.697, 0.790, 0.822, 0.818, 0.812, and 0.827, respectively. Conclusion Results from the external validation show that the dynamic PERSARC model is reliable in predicting the probability of surviving an additional 5 years from a specific prediction time point during follow‐up. The model combines patient‐, treatment‐specific and time‐dependent variables such as local recurrence and distant metastasis to provide accurate survival predictions throughout follow‐up and is available through the PERSARC app.