A J Rueten-Budde1, V M van Praag2, M A J van de Sande2, M Fiocco3. 1. Mathematical Institute, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, the Netherlands. Electronic address: a.j.ruten-budde@math.leidenuniv.nl. 2. Department of Orthopaedic Surgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands. 3. Mathematical Institute, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, the Netherlands; Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, the Netherlands.
Abstract
PURPOSE: There is increasing interest in personalized prediction of disease progression for soft tissue sarcoma patients. Currently, available prediction models are limited to predictions from time of surgery or diagnosis. This study updates predictions of overall survival at different times during follow-up by using the concept of dynamic prediction. PATIENTS AND METHODS: Information from 2232 patients with high-grade extremity soft tissue sarcoma, who underwent surgery at 14 specialized sarcoma centers, was used to develop a dynamic prediction model. The model provides updated 5-year survival probabilities from different prediction time points during follow-up. Baseline covariates as well as time-dependent covariates, such as status of local recurrence and distant metastases, were included in the model. In addition, the effect of covariates over time was investigated and modelled accordingly in the prediction model. RESULTS: Surgical margin and tumor histology show a significant time-varying effect on overall survival. The effect of margin is strongest shortly after surgery and diminishes slightly over time. Development of local recurrence and distant metastases during follow-up have a strong effect on overall survival and updated predictions must account for their occurrence. CONCLUSION: The presence of time-varying effects, as well as the effect of local recurrence and distant metastases on survival, suggest the importance of updating predictions during follow-up. This newly developed dynamic prediction model which updates survival probabilities over time can be used to make better individualized treatment decisions based on a dynamic assessment of a patient's prognosis.
PURPOSE: There is increasing interest in personalized prediction of disease progression for soft tissue sarcomapatients. Currently, available prediction models are limited to predictions from time of surgery or diagnosis. This study updates predictions of overall survival at different times during follow-up by using the concept of dynamic prediction. PATIENTS AND METHODS: Information from 2232 patients with high-grade extremity soft tissue sarcoma, who underwent surgery at 14 specialized sarcoma centers, was used to develop a dynamic prediction model. The model provides updated 5-year survival probabilities from different prediction time points during follow-up. Baseline covariates as well as time-dependent covariates, such as status of local recurrence and distant metastases, were included in the model. In addition, the effect of covariates over time was investigated and modelled accordingly in the prediction model. RESULTS: Surgical margin and tumor histology show a significant time-varying effect on overall survival. The effect of margin is strongest shortly after surgery and diminishes slightly over time. Development of local recurrence and distant metastases during follow-up have a strong effect on overall survival and updated predictions must account for their occurrence. CONCLUSION: The presence of time-varying effects, as well as the effect of local recurrence and distant metastases on survival, suggest the importance of updating predictions during follow-up. This newly developed dynamic prediction model which updates survival probabilities over time can be used to make better individualized treatment decisions based on a dynamic assessment of a patient's prognosis.
Authors: Maria Anna Smolle; Michiel van de Sande; Dario Callegaro; Jay Wunder; Andrew Hayes; Lukas Leitner; Marko Bergovec; Per-Ulf Tunn; Veroniek van Praag; Marta Fiocco; Joannis Panotopoulos; Madeleine Willegger; Reinhard Windhager; Sander P D Dijkstra; Winan J van Houdt; Jakob M Riedl; Michael Stotz; Armin Gerger; Martin Pichler; Herbert Stöger; Bernadette Liegl-Atzwanger; Josef Smolle; Dimosthenis Andreou; Andreas Leithner; Alessandro Gronchi; Rick L Haas; Joanna Szkandera Journal: Cancers (Basel) Date: 2019-12-21 Impact factor: 6.639
Authors: Maria A Smolle; Laurin Herbsthofer; Mark Goda; Barbara Granegger; Iva Brcic; Marko Bergovec; Susanne Scheipl; Barbara Prietl; Amin El-Heliebi; Martin Pichler; Armin Gerger; Florian Posch; Martina Tomberger; Pablo López-García; Julia Feichtinger; Claudia Baumgartner; Andreas Leithner; Bernadette Liegl-Atzwanger; Joanna Szkandera Journal: Oncoimmunology Date: 2021-03-11 Impact factor: 8.110
Authors: Dario Callegaro; Rosalba Miceli; Sylvie Bonvalot; Peter C Ferguson; Dirk C Strauss; Veroniek V M van Praag; Antonin Levy; Anthony M Griffin; Andrew J Hayes; Silvia Stacchiotti; Cecile Le Pèchoux; Myles J Smith; Marco Fiore; Angelo Paolo Dei Tos; Henry G Smith; Charles Catton; Joanna Szkandera; Andreas Leithner; Michiel A J van de Sande; Paolo G Casali; Jay S Wunder; Alessandro Gronchi Journal: EClinicalMedicine Date: 2019-11-22
Authors: Gijsbert M Kalisvaart; Willem Grootjans; Judith V M G Bovée; Hans Gelderblom; Jos A van der Hage; Michiel A J van de Sande; Floris H P van Velden; Johan L Bloem; Lioe-Fee de Geus-Oei Journal: Diagnostics (Basel) Date: 2021-12-04