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Eur J Cancer. 2017 Sep;83:313-323. doi: 10.1016/j.ejca.2017.06.032. Epub 2017 Aug 8.

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

Author information

1
Department of Orthopaedic Surgery, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA Leiden, The Netherlands.
2
Mathematical Institute, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands.
3
Department of Orthopaedic Surgery, Royal Orthopaedic Hospital, Bristol Road South, Northfield, Birmingham B31 2AP, United Kingdom.
4
Department of Orthopaedic Surgery, Royal National Orthopaedic Hospital, Brockley Hill, Stanmore HA7 4LP, United Kingdom.
5
Sarcoma Unit, Netherlands Cancer Institute, Department of Surgery, Postbus 90203 1006 BE Amsterdam, The Netherlands.
6
University Musculoskeletal Oncology Unit, Mount Sinai Hospital, Division of Orthopaedics, Department of Surgery, University of Toronto, 600 University Avenue, Toronto, ON M5G 1X5, Canada.
7
Department of Orthopaedic Surgery, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA Leiden, The Netherlands. Electronic address: majvandesande@lumc.nl.
8
Mathematical Institute, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands; Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA Leiden, The Netherlands.

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.

KEYWORDS:

Local recurrence; Margins; Prediction; Prognosis; Prognostic factors; Radiotherapy; Sarcoma; Soft-tissue sarcoma; Statistics & research methods; Survival

PMID:
28797949
DOI:
10.1016/j.ejca.2017.06.032
[Indexed for MEDLINE]

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