A prediction model for treatment decisions in high-grade extremity soft-tissue sarcomas: Personalised sarcoma care (PERSARC). (September 2017)

• Record Type: Journal Article
• Authors: van Praag, Veroniek M.; Rueten-Budde, Anja J.; Jeys, Lee M.; Laitinen, Minna K.; Pollock, Rob; Aston, Will; van der Hage, Jos A.; Dijkstra, P.D. Sander; Ferguson, Peter C.; Griffin, Anthony M.; Willeumier, Julie J.; Wunder, Jay S.; van de Sande, Michiel A.J.; Fiocco, Marta
• Is Part Of: European journal of cancer. Volume 83(2017)
• Page Start: 313
• Page End: 323
• Publication Date: 2017-09
• Subjects: Prognosis -- Prognostic factors -- Prediction -- Sarcoma -- Soft-tissue sarcoma -- Survival -- Margins -- Radiotherapy -- Local recurrence -- Statistics & research methods; Cancer -- Periodicals; Neoplasms -- Periodicals; Cancer -- Périodiques; Cancer; Tumors; Electronic journals; Periodicals; Electronic journals; 616.994
• DOI: 10.1016/j.ejca.2017.06.032
• Languages: English
• ISSNs: 0959-8049

Abstract

Abstract: Background: To support shared decision-making, we developed the first prediction model for patients with primary soft-tissue sarcomas of the extremities (ESTS) which takes into account treatment modalities, including applied radiotherapy (RT) and achieved surgical margins. The PERsonalised SARcoma Care (PERSARC) model, predicts overall survival (OS) and the probability of local recurrence (LR) at 3, 5 and 10 years. Aim: Development and validation, by internal validation, of the PERSARC prediction model. Methods: The cohort used to develop the model consists of 766 ESTS patients who underwent surgery, between 2000 and 2014, at five specialised international sarcoma centres. To assess the effect of prognostic factors on OS and on the cumulative incidence of LR (CILR), a multivariate Cox proportional hazard regression and the Fine and Gray model were estimated. Predictive performance was investigated by using internal cross validation (CV) and calibration. The discriminative ability of the model was determined with the C-index. Results: Multivariate Cox regression revealed that age and tumour size had a significant effect on OS. More importantly, patients who received RT showed better outcomes, in terms of OS and CILR, than those treated with surgery alone. Internal validation of the model showed good calibration and discrimination, with a C-index of 0.677 and 0.696 for OS and CILR, respectively. Conclusions: The PERSARC model is the first to incorporate known clinical risk factors with the use of different treatments and surgical outcome measures. The developed model is internally validated to provide a reliable prediction of post-operative OS and CILR for patients with primary high-grade ESTS. Level of significance: level III. Highlights: The PERsonalised SARcoma Care model gives reliable patient-specific prediction for different treatments. Radiotherapy associated with survival and diminished risk of local recurrences (LRs). Higher age and larger tumour size decreased survival. Wider margins and smaller tumour size decreased the risk of developing LRs. The 10-year overall survival rate in grade III patients was 38.5%.