Dynamic prediction of overall survival for patients with high-grade extremity soft tissue sarcoma

Rueten‐Budde, Anja J.; Praag, Veroniek M. van; studygroup, Persarc; Sande, Michiel A. J. van de; Fiocco, Marta

doi:10.1016/j.suronc.2018.09.003

Cited by 37 publications

(34 citation statements)

References 17 publications

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“…Previously published studies have well-investigated risks of LR, DM, and overall survival (OS) in large, retrospective cohorts of patients with eSTS [14,16,20,21]. The nomogram for OS by Kattan et al [21] in 2002 and the two more recent nomograms for DM and OS by Callegaro et al [16] published in 2016 added significant value to predict individual patient risks.…”

Section: Discussionmentioning

confidence: 99%

Individualizing Follow-Up Strategies in High-Grade Soft Tissue Sarcoma with Flexible Parametric Competing Risk Regression Models

Smolle

Sande

Callegaro

et al. 2019

Cancers

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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.

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Section: Discussionmentioning

confidence: 99%

Individualizing Follow-Up Strategies in High-Grade Soft Tissue Sarcoma with Flexible Parametric Competing Risk Regression Models

Smolle

Sande

Callegaro

et al. 2019

Cancers

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“…Compared to previous studies [13], our nomogram adds significant insights since it is built on a larger cohort, is valid for patients with grade I eSTS as well as higher grade tumors, adopts a more updated and granular histological classification and included an external validation. Moreover, we were able to select easily definable covariates which were all strongly associated with survival.…”

Section: Discussionmentioning

confidence: 99%

“…In the past few years there have been studies exploring survival-specific prognostic information in patients with soft tissue and bone sarcoma showing that the effect of prognostic variables changes as patient's survival time increases and that, without events, the likelihood of surviving improves as time goes by after treatment [9], [10], [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Development and external validation of a dynamic prognostic nomogram for primary extremity soft tissue sarcoma survivors

et al. 2019

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Background: Prognostic nomograms for patients with extremity soft tissue sarcoma (eSTS) typically predict survival or the occurrence of local recurrence or distant metastasis at time of surgery. Our aim was to develop and externally validate a dynamic prognostic nomogram for overall survival in eSTS survivors for use during follow-up. Methods: All primary eSTS patients operated with curative intent between 1994 and 2013 at three European and one Canadian sarcoma centers formed the development cohort. Patients with F ed eration Française des Centres de Lutte Contre le Cancer (FNCLCC) grade II and grade III eSTS operated between 2000 and 2016 at seven other European reference centers formed the external validation cohort. We used a landmark analysis approach and a multivariable Cox model to create a dynamic nomogram; the prediction window was fixed at five years. A backward procedure based on the Akaike Information Criterion was adopted for variable selection. We tested the nomogram performance in terms of calibration and discrimination. Findings: The development and validation cohorts included 3740 and 893 patients, respectively. The variables selected applying the backward procedure were patient's age, tumor size and its interaction with landmark time, tumor FNCLCC grade and its interaction with landmark time, histology, and both local recurrence and distant metastasis (as first event) indicator variables. The nomogram showed good calibration and discrimination. Harrell C indexes at different landmark times were between 0.776 (0.761À0.790) and 0.845 (0.823À0.862) in the development series and between 0.675 (0.643À0.704) and 0.810 (0.775À0.844) in the validation series. Interpretation: A new dynamic nomogram is available to predict 5-year overall survival at different times during the first three years of follow-up in patients operated for primary eSTS. This nomogram allows physicians Research in contextEvidence before this studyWe searched PubMed for studies published before Nov 1, 2018, that investigated the use of nomograms for predicting prognosis of patients with extremity soft tissue sarcoma. We used the search terms 'nomogram', 'prediction', 'sarcoma', and 'extremity'. Available prognostic nomograms for patients with extremity soft tissue sarcoma predicted survival or the occurrence of local recurrence or distant metastasis at time of surgery. After surgery, the individual risk of dying shows a non-linear evolution which is determined by the time elapsed from surgery, the event history (occurrence of local recurrence or distant metastasis vs. no events), and by the time-dependent effect of baseline covariates (tumor and patient characteristics). At the time of our search, there were no nomograms available for use during follow-up. The only available dynamic prognostic model was limited to patients with high-grade tumors, adopted a suboptimal histological classification and was not externally validated.Added value of this study.With this study, we created and externally validated a dynamic prognostic nomogra...

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“…As most statistical methods are based on complete data sets, multiple imputation was applied by using the R-package Amelia II. 11 14 Ten complete data sets were imputed and used for all statistical analyses. All 10 data sets shared the same observed values with the original data, while missing values were imputed using estimated sufficient statistics.…”

Section: Methodsmentioning

confidence: 99%

Dynamic prediction of overall survival: a retrospective analysis on 979 patients with Ewing sarcoma from the German registry

et al. 2020

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ObjectivesThis study aimed at developing a dynamic prediction model for patients with Ewing sarcoma (ES) to provide predictions at different follow-up times. During follow-up, disease-related information becomes available, which has an impact on a patient’s prognosis. Many prediction models include predictors available at baseline and do not consider the evolution of disease over time.SettingIn the analysis, 979 patients with ES from the Gesellschaft für Pädiatrische Onkologie und Hämatologie registry, who underwent surgery and treatment between 1999 and 2009, were included.DesignA dynamic prediction model was developed to predict updated 5-year survival probabilities from different prediction time points during follow-up. Time-dependent variables, such as local recurrence (LR) and distant metastasis (DM), as well as covariates measured at baseline, were included in the model. The time effects of covariates were investigated by using interaction terms between each variable and time.ResultsDeveloping LR, DM in the lungs (DMp) or extrapulmonary DM (DMo) has a strong effect on the probability of surviving an additional 5 years with HRs and 95% CIs equal to 20.881 (14.365 to 30.353), 6.759 (4.465 to 10.230) and 17.532 (13.210 to 23.268), respectively. The effects of primary tumour location, postoperative radiotherapy (PORT), histological response and disease extent at diagnosis on survival were found to change over time. The HR of PORT versus no PORT at the time of surgery is equal to 0.774 (0.594 to 1.008). One year after surgery, the HR is equal to 1.091 (0.851 to 1.397).ConclusionsThe time-varying effects of several baseline variables, as well as the strong impact of time-dependent variables, show the importance of including updated information collected during follow-up in the prediction model to provide accurate predictions of survival.

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Dynamic prediction of overall survival for patients with high-grade extremity soft tissue sarcoma

Cited by 37 publications

References 17 publications

Individualizing Follow-Up Strategies in High-Grade Soft Tissue Sarcoma with Flexible Parametric Competing Risk Regression Models

Individualizing Follow-Up Strategies in High-Grade Soft Tissue Sarcoma with Flexible Parametric Competing Risk Regression Models

Development and external validation of a dynamic prognostic nomogram for primary extremity soft tissue sarcoma survivors

Dynamic prediction of overall survival: a retrospective analysis on 979 patients with Ewing sarcoma from the German registry

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