Using PERSARC
Prediction and validation using PERsonalized SARcoma Care (PERSARC)
The PERSARC app
The mobile PERSARC (PERsonalised SARComa Care) app is a prognostic tool specifically designed to support shared decision making for patients with a primary high-grade Soft-Tissue Sarcoma of the extremity (eSTS), (to be) treated with surgical resection (and radiotherapy). The information in this app is relevant for patients with grade II or III sarcoma, and not for sarcoma subtypes other than those mentioned in the app or patients that receive any form of chemotherapy before or shortly after surgery. Using patient- and tumor-related characteristics, the app provides an estimate of the oncological outcome in terms of Overall Survival (OS) or incidence of Local Recurrence (LR).
The PERSARC model was developed by DASPO in an earlier project on STS with prof.dr. M. Fiocco (Mathematical Institute Leiden University) and prof.dr. M.A.J. van de Sande (Leiden University Medical Centre & Princess Máxima Center for Pediatric Oncology) as principal investigators. The implementation of the app was funded by the Dutch Cancer Society (KWF).
Background
In light of the heterogeneity of eSTS tumours, treatment recommendations depend on several factors and management decisions are often made by a multidisciplinary team. In this scenario, the PERSARC app represents the first informative support tool for the dynamic prediction of OS and LR in high-grade eSTS, using patient-, tumour- and treatment-related characteristics. It is currently used by many physicians in their daily practice to assess a more realistic prognosis of expected recurrence rates and life expectancy, improving their clinical-based predictive capability.
Relevance for cancer research
PERSARC predictions provide physicians and caregivers with valuable information that can be shared with the patient to strike a balance between prognosis and quality of life. It assists clinicians in better estimating realistic expectations for recurrence rates and life expectancy. This, in turn, may promote the identification of high-risk patients, who may benefit from perioperative chemotherapy, or the more frequent selection of limb salvage treatments, all the while ensuring comparable survival rates for these patients. Furthermore, given its established validity, PERSARC can serve as a tool for the validation and comparison of new models for OS and/or LR in eSTS.
Aims
Aims
This project about prediction and validation using PERSARC involves several studies.
A first study aimed to perform a risk stratified analyses based on the PERSARC prediction tool to assess which patients with eSTS might benefit from perioperative chemotherapy (CTX). This study demonstrated a beneficial effect of AI-based CTX on OS in a selected group of high-risk patients with an absolute survival benefit of 11% as stratified by the PERSARC prediction tool.
A second study in collaboration with the Erasmus MC Institute (Rotterdam) aimed to assess the performance of currently available risk calculators, including PERSARC, in a cohort of patients with malignant peripheral nerve sheath tumours (MPNSTs) and to create an MPNST-specific prediction model including type-specific predictors for overall survival.
A third study in collaboration with the Oslo University Hospital (Norway), the Foundation IRCCS National Cancer Institute of Milan (Italy), and the MD Anderson Cancer Center (University of Texas, USA) aims to compare different clinical risk stratification criteria to predict patient outcome in localized STS: (i) the deep-seated tumor location criterium, (ii) the Scandinavian Sarcoma Group (SSG) XX criteria, (iii) the PERSARC-risk stratification, and (iv) the Sarculator-risk statification.
A fourth study in collaboration with the Skåne University Hospital (Sweden) aims to validate a support model currently use in clinical practice to identify high-risk patients.
A fifth study in collaboration with the Department of Orthopedic Surgery at UCLA (USA) aims to externally validate different prediction models in soft-tissue sarcomas.
Project Outcomes
Project Outcomes
Acem I, Steyerberg EW, Spreafico M, Grünhagen DJ, Callegaro D, Spinner RJ, Pendleton CA, Coert JH, Miceli R, Abruzzese G, Flucke UE, Slooff WBM, van Dalen T, Been LB, Bonenkamp HJ, Anten MHME, Broen MPG, Bemelmans MHA, Bramer JAM, Schaap GR, Kievit AJ, van der Hage J, van Houdt WJ, van de Sande MAJ, Gronchi A, Verhoef C & Martin E (2023). Survival after Resection of Malignant Peripheral Nerve Sheath Tumours: introducing and validating a novel type-specific prognostic model. [Submitted]
Acem I, van Houdt WJ, Grünhagen DJ, van der Graaf WTA, Rueten-Budde AJ, Gelderblom H, Verhoef C, PERSARC research group & van de Sande MAJ (2022). The role of perioperative chemotherapy in primary high-grade extremity soft tissue sarcoma: a risk-stratified analysis using PERSARC. European Journal of Cancer, 165:71–80. doi: 10.1016/j.ejca.2022.01.013
PERSARC References
Rueten-Budde AJ, van de Sande M, van Praag VM & Fiocco M; PERSARC Study Group (2021). External validation and adaptation of a dynamic prediction model for patients with high-grade extremity soft tissue sarcoma. J Surg Oncol, 123(4):1050-1056. doi: 10.1002/jso.26337
Rueten-Budde AJ, van Praag VM, PERSARC studygroup, van de Sande MAJ & Fiocco M (2018). Dynamic prediction of overall survival for patients with high-grade extremity soft tissue sarcoma. Surgical Oncology, 27(4):695-701. doi: 10.1016/j.suronc.2018.09.003
van Praag VM, Rueten-Budde AJ, Jeys LM, Laitinen MK, Pollock R, Aston W, van der Hage JA, Dijkstra PS, Ferguson PC, Griffin AM, Willeumier JJ, Wunder JS, van de Sande MAJ & Fiocco M (2017). A prediction model for treatment decisions in high-grade extremity soft-tissue sarcomas: Personalised sarcoma care (PERSARC). European Journal of Cancer, 83:313-323. doi: 10.1016/j.ejca.2017.06.032
Team
Team
Prof.dr. M. Fiocco, Mathematical Institute Leiden University, Leiden University Medical Center (Biomedical Data Sciences) & Princess Máxima Center for Pediatric Oncology
Prof.dr. M.A.J. van de Sande, Leiden University Medical Centre (Orthopaedic Oncology and Surgical Oncology) & Princess Máxima Center for Pediatric Oncology
Dr. M. Spreafico, Mathematical Institute Leiden University & Leiden University Medical Center (Biomedical Data Sciences)
