Individualizing follow-up strategies in high-grade soft tissue sarcoma with flexible parametric competing risk regression models

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 StögerBernadette Liegl-Atzwanger, Josef Smolle, Dimosthenis Andreou, Andreas Leithner, Alessandro Gronchi, Rick L. Haas, Joanna Szkandera

Onderzoeksoutput: Bijdrage aan tijdschriftArtikelpeer review

12 Citaten (Scopus)


Currently, patients with extremity soft tissue sarcoma (eSTS) who have undergone curative resection are followed up by a heuristic approach, not covering individual patient risks. The aim of this study was to develop two flexible parametric competing risk regression models (FPCRRMs) for local recurrence (LR) and distant metastasis (DM), aiming at providing guidance on how to individually follow-up patients. Three thousand sixteen patients (1931 test, 1085 validation cohort) with high-grade eSTS were included in this retrospective, multicenter study. Histology (9 categories), grading (time-varying covariate), gender, age, tumor size, margins, (neo)adjuvant radiotherapy (RTX), and neoadjuvant chemotherapy (CTX) were used in the FPCRRMs and performance tested with Harrell-C-index. Median follow-up was 50 months (interquartile range: 23.3–95 months). Two hundred forty-two (12.5%) and 603 (31.2%) of test cohort patients developed LR and DM. Factors significantly associated with LR were gender, size, histology, neo-and adjuvant RTX, and margins. Parameters associated with DM were margins, grading, gender, size, histology, and neoadjuvant RTX. C-statistics was computed for internal (C-index for LR: 0.705, for DM: 0.723) and external cohort (C-index for LR: 0.683, for DM: 0.772). Depending on clinical, pathological, and patient-related parameters, LR-and DM-risks vary. With the present model, implemented in the updated Personalised Sarcoma Care (PERSARC)-app, more individualized prediction of LR/DM-risks is made possible.

Originele taal-2Engels
Nummer van het tijdschrift1
StatusGepubliceerd - jan. 2020


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