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


Abstract and Figures

FREE ACCESS TO THE ARTICLE (LINK IS VALID TILL SEPTEMBER 27, 2017): 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.
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A Prediction Model for Treatment decisions in High-Grade Extremity Soft Tissue sarcomas
Personalized Sarcoma Care (PERSARC)
Veroniek M van Praag, PhD-candidate orthopaedic surgery and Anja J Rueten-Budde, PhD-candidate
mathematics, Lee M Jeys3, Professor orthopaedic oncology, Minna K Laitinen3, Consultant orthopaedic
oncology Rob Pollock4, Consultant Surgical oncology, Will Aston4, Consultant Surgical oncology Jos A van
der Hage5, Consultant orthopaedic oncology PD Sander Dijkstra1, Consultant orthopaedic oncology, Peter C
Ferguson6, Consultant orthopaedic oncology Anthony M Griffin5, research manager, Julie J Willeumier1,
PhD-candidate orthopaedic surgery, Jay S Wunder6, Consultant orthopaedic oncology, Michiel AJ van de
Sande1*, Consultant orthopaedic oncology and Marta Fiocco2,7*, Associate professor in biostatistics
±Shared first authorship, *Shared last authorship
1Department of Orthopaedic Surgery, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA Leiden,
the Netherlands
2Mathematical Institute, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, the Netherlands
3Department of Orthopaedic Surgery, Royal Orthopaedic Hospital, Bristol Road South, Northfield,
Birmingham, B31 2AP, United Kingdom
4Department of Orthopaedic Surgery, Royal National Orthopaedic Hospital, Brockley Hill, Stanmore, HA7
4LP, United Kingdom
5Sarcoma Unit, Netherlands Cancer Institute, Department of Surgery, Postbus 90203 1006
BE Amsterdam, the Netherlands
6University Musculoskeletal Oncology Unit, Mount Sinai Hospital, and Division of Orthopaedics,
Department of Surgery, University of Toronto, 600 University Avenue, Toronto, ON M5G 1X5, Canada
7Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Albinusdreef 2,
2333 ZA, Leiden, the Netherlands
Corresponding author:
Michiel AJ van de Sande, MD PhD
Department of Orthopaedic Surgery, LUMC
Albinusdreef 2, 2300 RC Leiden, The Netherlands; +31715263606
Conflict of interest: nothing to declare.
Level of significance: level III
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 and achieved surgical margins. The PERsonalized SARcoma
Care (PERSARC) model, predicts overall survival and the probability of local recurrence at 3, 5 and 10
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 2000-2014, at five specialised international sarcoma centres. To assess the effect of prognostic
factors on overall survival (OS) and on the cumulative incidence of local recurrence (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 radiotherapy 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 outcomes measures. The developed model is internally validated to
provide a reliable prediction of postoperative OS and CILR for patients with primary high-grade ESTS.
PERSARC model gives reliable patient-specific prediction for different treatments.
Radiotherapy associated with survival and diminished risk of local recurrences.
Higher age and larger tumour size decreased survival.
Wider margins and smaller tumour size decreased risk of developing local recurrences.
The 10-year overall survival rate in grade III patients was 38.5%.
Multimodality treatment of high-grade soft tissue sarcomas of the extremities (ESTS) has improved over
the years; however, local recurrence (LR), distant metastasis (DM) and poor survival remain of great
concern.1 Although the effect of several patient related prognostic factors on overall survival (OS) and LR
is well described, the lack of a validated prediction model that includes treatment modalities, complicates
decision-making aimed at balancing oncologic cure and minimizing the risk of disability after treatment.
Factors, such as vascular invasion2, peripheral tumour growth2, tumour size2-5, infiltrative growth2,
necrosis2, site3, adjuvant chemo- and/or radiotherapy6, histological grade3-5 (for fibro- and
liposarcomas7) and histological subtype3,4 have been shown to have a significant impact on survival.
While some studies indicate that the prognostic value of tumour depth2, state at presentation6, tumour
site8 and age8 remains unclear, others found some of these factors to be good predictors of outcome.3-5
The effect of limb sparing surgery and neo-adjuvant chemo- and/or radiotherapy remains debatable.6
Surgical margins have an impact on LR1,4 but no clear association with OS has been established.1,4
In 2003 a prognostic model based on 175 patients with ESTS became available9 and expanded twice.10,11
The first update included patients who were diagnosed at a time (1967) when MRI was not part of
standard care. Prognostic factors included in those studies were tumour size, vascular invasion, necrosis,
grade, peripheral growth, depth and location. Age, gender, recurrence and metastasis, margins and
histology were not included in the model. Callegaro et al. (2016) developed two nomograms for soft
tissue sarcomas of the extremities and trunk using age, tumour size, histological- grade and subtype,
using exclusively patients with macroscopically complete surgical resections.12 In addition, several
models only provide prognosis for OS and DM, whereas others underline the relevance of LR. Willeumier
et al. (2017) underlined the importance of individual prognostication of LR and OS based on different
combinations of surgical margins and possible (neo-) and/or adjuvant therapy, while also taking different
patient- and tumour characteristics into account.13
In order to support shared decision making between patients and physicians, this study aims to develop a
prognostic personalized sarcoma care model (PERSARC) to predict the cumulative incidence of local
recurrence (CILR) and OS for a patient with high-grade ESTS with specific clinical characteristics and
possible treatment modalities at baseline. The prediction model is internally validated by calibration and
This multicentre study was approved by each of our hospitals’ human subjects review boards.
Study population. The study population included a consecutive series of 838 patients with primary
high-grade ESTS who underwent surgical treatment at one of the five international collaborating
hospitals between January 2001 and December 2014. Due to missing values for 72 patients, 766
individuals were included in development of the PERSARC model. Eligible diagnoses included high-grade
(FNCLCC grade 3) angiosarcoma, malignant peripheral nerve sheath tumour (MPNST), synovial sarcoma,
spindle cell sarcoma, myxofibrosarcoma and (pleomorphic) soft tissue sarcomas not-otherwise-specified
(NOS). Excluded patients include those that were treated without curative intent, had LR or DM within
two months after primary treatment (ruled out by pre-treatment and follow-up staging with lung CT-
scan), had a tumour in their abdomen, thorax, head or neck or received (neo-)adjuvant treatment other
than radio- or chemotherapy.
All collaborating sarcoma centres implemented the ESMO guidelines for soft tissue sarcoma follow-up.14
Patients visited the outpatient clinic for their scheduled clinical and radiographic follow-up: every 3-4
months in the first 2-3 years, then every 6 months and after 5 years yearly. It was common that follow-up
was ended after ten years evidence of no disease.
Study Design. This was a retrospective observational study in which clinical information was gathered
retrospectively (medical records) and by using existing prospective sarcoma databases; (including
documentation of clinic visits, operation reports, histology and radiographic reports). This information
included demographics (centre, patient gender and age at presentation, event and follow-up), tumour
characteristics (presentation, localisation, depth, diameter, histology, grade), treatment characteristics
(goal, time of operation (weeks), resection margin and categorical, type and dose of (neo-)adjuvant
therapy), development of LR and/or DM and last known status. All patients had a minimal follow-up (FU)
of 2 years or experienced an event (LR, DM or death) before. The primary outcome measure was survival,
if the patient was alive at their last documented visit information on the tumour status was gathered.
Secondary outcome measure was local recurrence. Long-term follow-up was obtained through reviewing
documentation of all clinical and radiographic follow-up.
A sarcoma was considered primary if it was previously untreated, a biopsy or whoops excision had been
performed prior to presentation at one of the five contributing specialised sarcoma centres, with no
evidence of metastatic disease. Local recurrence was defined as the presence of viable tumour at the site
of the original tumour bed confirmed by clinical findings, pathological tissue diagnosis or radiological
reports more than two months after primary surgery. Distant recurrence was defined by clinical or
radiological evidence of systemic spread of tumour outside the primary tumour bed.
Tumour size was defined as maximum diameter at pathologic analysis. In patients that received
neoadjuvant radiotherapy and/or chemotherapy, tumour size was defined as maximum diameter
measured by computed tomography or magnetic resonance imaging prior to treatment. Surgical margin
was defined as follows: intraleasional for tumour cells present at the margin of the resection specimen
(<0.1 mm), marginal for tumour cells found within 0.1-2 millimetres of the margin and free for tumour
cells found at least 2 mm away from the margin. 1,13,15 Tumour grade was classified as high-grade based
on established criteria Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC).
Statistical analysis:
Multivariate Cox regression model. To assess the effect of prognostic factors on OS a multivariate Cox
proportional hazards regression model was used. Predictor variables incorporated in the model were
age, tumour size, depth, histology subtype, surgical margin, and radiotherapy. Initially tumour site, and
tumour presentation were considered; however, previous studies 12 and an initial multivariate analysis
(Wald test p value: tumour site p= 0.818, tumour presentation p= 0.696) showed no strong predictive
Fine and Gray model. To estimate the effect of risk factors on the cumulative incidence of LR (CILR), a
competing risks model, which accounts for the competing event death was used (Appendix 1) 16. After
surgery, a patient may be alive with no evidence of disease (ANED). He may then develop LR or die. The
cumulative incidence function is defined as the probability of the event occurring before a certain time
point. Fine and Gray’s method models the effect of covariates on the cumulative incidence in the presence
of competing events. Subdistribution hazard ratios (sHR) estimate the effect of risk factors on the
probability of event occurrence directly. The same covariates used in the Cox model were considered.
Prediction and validation. Predictions for OS and LR at 3, 5 and 10 years after surgery together with
95% confidence intervals (95%CI), which indicate the uncertainty surrounding the estimates are
provided. To justify their use in clinical practice predictive performance of the prediction models was
assessed internally by using leave-one-out cross validation (CV). CV is a technique to simulate model
performance on new data.
Following van Houwelingen (2000) a prognostic model is defined as a rule to compute probabilities given
relevant covariates and its validity can be argued on the basis of model calibration.
Calibration refers to how well predicted probabilities agree with observed probabilities. A common
practice is to group patients from “good” to “bad” prognosis. A model is well calibrated if true and
predicted group probabilities do not differ.
The prediction model can be used to categorize patients based on their prognosis. A patient’s risk factor
information can be summarised into a prognostic index (PI), which is a weighted mean of prognostic
variables, where weights are derived from the prognostic model. Patients with a higher value of PI have a
higher predicted risk. Hence the PI can be used to divide data into 4 equal sized groups: “good prognosis”,
“fairly good prognosis”, “fairly poor prognosis” and “poor prognosis”.
Calibration plots visualize model calibration on a given set of data.17,18 Data are divided into prognostic
groups. At specific time points the groups’ observed outcome (OS or CILR) is plotted against their
predicted outcome. If the points are scattered around the diagonal (x=y) the model is valid without
adjustment. To investigate calibration for data subgroups, one-sampled T-tests are used, where predicted
outcomes were considered the ‘fixed” value and observed outcomes as the evaluated variable.19
Discrimination refers to the ability of the model to assign higher predicted risk to patients who
experience the event earlier compared to those experiencing the event later or not at all. To visualize this
aspect, nonparametric curves are plotted showing the observed outcome (OS or CILR) for different
prognostic groups 20. The spread of the curves indicates how well a model can discriminate. The C-index
quantifies discrimination as the proportion of patient pairs that experience events in the order of risk
predicted.21 It can be adjusted for competing risks18 and can be interpreted as follows: a C-index of 1
means that the model has perfect discrimination and a C-index of 0.5 means that the model predicts just
as well as flipping a coin.22
All statistical analysis was conducted using R software.23 A p-value of 0.05 was defined as statistically
Table 1 summarises patients’ characteristics at baseline for the included 766 patients from the five
international sarcoma centres. The median follow-up was 71.8 months (95%CI: 67.6-75.9), assessed with
the reverse Kaplan-Meier method. In total 369 patients died and 116 developed a LR. The majority of
patients with a LR died (n=83; 72%). OS was estimated to be equal to 63%, 53%, and 39% at 3, 5 and 10
years respectively. Cumulative incidence of LR was estimated to be equal to 13.3% (95%CI: 10.9-15.8),
15.1% (95%CI: 12.4-17.7) and 17.2% (95%CI: 13.9-20.5) at 3, 5 and 10 years respectively. The centre
effect on disease progression was investigated but no significant effect was found.
Table 1. Patient characteristics .
Age (mean (SD))
58.28 (19.39)
Age (%)
30-60 years
281 (36)
<30 years
82 (11)
>60 years
403 (53)
Sex (%)
435 (57)
331 (43)
Depth (%)*
579 (76)
134 (17 )
Deep & superficial
53 (7)
Size in cm (mean (SD))
10.06 (6.21)
Extremity (%)
182 (24)
584 (76)
Presentation (%)
622 (81)
144 (18)
Histology (%)
238 (31 )
91 (12)
Synovial sarcoma
142 (18 )
Spindle cell sarcoma
167 (22)
77 (10 )
51 (7)
Margin (%)
0 mm
140 (18 )
0.1-2 mm
343 (45)
>2 mm
283 (37)
Limb sparing (%)
81 (11)
685 (89 )
RT (%)
184 (24)
400 (52)
182 (24)
MFH/UPS: Malignant Fibrous Histiocytoma/Undifferentiated Pleomorphic Sarcoma; MPNST: malignant peripheral nerve
sheath tumour; RT: radiotherapy.
*Depth: relative to the investing fascia.
Age, tumour size and additional radiotherapy show an independent significant effect on OS (Table 2).
Patients with larger tumours have a significantly increased risk of dying with HR equal to 1.068 (95%CI:
1.052-1.085) for a unit increase of 1 cm. Older age is associated with a higher risk of death with HR equal
to 1.195 (95%CI: 1.116-1.268) for a 10-year increase in age. Note that age and size are included as linear
terms in the model, implying that a ‘k*10’ year change in age and a ‘k’ cm change in size multiply the
hazard by HRk. Surgical margin has a marginally significant effect on OS, with HR equal to 0.786 (95%CI:
0.599-1.033) and 0.711 (95%CI: 0.524-0.964) for margin equal to 0.1-2 mm and >2 mm respectively
(reference category 0 mm). Radiotherapy treatment is associated with a decreased risk of dying
compared to surgery alone with HRs equal to 0.548 (95%CI: 0.399-0.753) and 0.638 (95%CI: 0.486-
0.837) for neoadjuvant and adjuvant radiotherapy respectively.
Table 2. Multivariate Cox model for OS: hazard ratio (HR) along with 95% confidence interval (n = 766).
P value
Deep and superficial
Synovial sarcoma
Spindle cell sarcoma
0 mm
0.1-2 mm
>2 mm
The HR of age corresponds to a unit increase of 10 years and the HR of size corresponds to a unit increase of 1 cm. MFH/UPS:
Malignant Fibrous Histiocytoma/Undifferentiated Pleomorphic Sarcoma; MPNST: malignant peripheral nerve sheath tumour;
RT: radiotherapy. *Depth: relative to the investing fascia.
Figure 1 shows calibration plots for OS at 3, 5 and 10 years. The 3-, 5- and 10-year calibration plots show
points (representing risk groups) scattered close to the diagonal, which is contained in the 95%CIs of the
observed group survival.
Fig. 1: Calibration plots for overall survival
Observed survival obtained using Kaplan-Meier estimator is plotted against predicted survival for patients in 8 equal sized risk
groups identified by their predicted survival at (A) 3 years, (B) 5 years and (C) 10 years, as assessed by cross validation.
A detailed comparison of observed and predicted survival at 3, 5 and 10 years for data subgroups is given
in Table 3. Observed and predicted outcome do not differ significantly; however, for smaller and medium
sized tumours (<5cm, 5cm-10cm) survival is underestimated at 3 and 5, and 10 years respectively.
Table 3. Comparing observed and predicted OS, assessed by cross validation, for subgroups of data at 3,
5 and 10 years.
Figure 2 shows good discrimination of the model visualised by the spread of the Kaplan-Meier estimates
(solid lines). Model-based estimates (dotted lines) show the mean predicted survival per group close to
the observed survival, indicating good calibration.
The C-index for overall survival was estimated to be 0.677 (95%CI 0.643-0.701).
n (%)
3 years
Observed (s.e.)
Difference (95% CI)
5 years
Observed (s.e.)
Difference (95% CI)
10 years
Observed (s.e.)
Difference (95% CI)
281 (36.7)
70.7 (2.8)
-1.8 (-7.3 to 3.7)
60.5 (3.1)
-0.3 (-6.4 to 5.8)
45.4 (4.7)
1.0 ( -8.2 to 10.2)
82 (10.7)
74.6 (4.9)
3.2 (-6.4 to 12.8)
68.9 (5.3)
1.8 (-8.6 to 12.2)
58.3 (6.9)
0.4 (-13.1 to 13.9)
403 (52.6)
54.9 (2.6)
-0.9 (-6.0 to 4.2)
44.4 (2.7)
-0.8 (-6.1 to 4.5)
28.9 (4.1)
0.2 ( -7.8 to 8.2)
123 (16.1)
87.0 (3.1)
-9.8 (-15.9 to -3.7)
78.2 (4.1)
-8.4 (-16.4 to -0.4)
57.8 (8.1)
-0.6 (-16.5 to 15.3)
295 (38.5)
68.7 (2.8)
0.7 ( -4.8 to 6.2)
59.7 (3.0)
0.6 ( -5.3 to 6.5)
53.1 (3.6)
-7.3 (-14.4 to -0.2)
348 (45.4)
49.3 (2.8)
1.1 ( -4.4 to 6.6)
38.6 (2.8)
1.4 ( -4.1 to 6.9)
20.7 (3.8)
5.3 ( -2.1 to 12.7)
579 (75.6)
62.8 (2.1)
-1.9 (-6.0 to 2.2)
52.7 (2.2)
-1.3 (-5.6 to 3.0)
37.5 ( 3.1)
-0.2 ( -6.3 to 5.9)
134 (17.5)
67.6 (4.2)
1.7 (-6.5 to 9.9)
60.9 (4.5)
-0.2 (-9.0 to 8.6)
56.1 ( 4.9)
-9.2 (-18.8 to 0.4)
Deep and superficial
53 (6.9)
51.2 (7.0)
3.8 (-9.9 to 17.5)
37.9 (7.4)
7.8 (-6.7 to 22.3)
19.0 (10.2)
13.7 ( -6.3 to 33.7)
238 (31.1)
62.4 (3.2)
0.3 ( -6.0 to 6.6)
53.4 (3.4)
-0.1 ( -6.8 to 6.6)
36.3 ( 5.0)
2.8 ( -7.0 to 12.6)
91 (11.9)
57.7 (5.2)
2.3 ( -7.9 to 12.5)
50.1 (5.4)
0.1 (-10.5 to 10.7)
33.4 ( 7.7)
2.5 (-12.6 to 17.6)
142 (18.5)
75.1 (3.8)
-1.3 ( -8.7 to 6.1)
66.6 (4.2)
-0.8 ( -9.0 to 7.4)
50.9 ( 5.9)
1.8 ( -9.8 to 13.4)
Spindle cell sarcoma
167 (21.8)
59.9 (3.9)
-4.3 (-11.9 to 3.3)
47.4 (4.3)
-2.0 (-10.4 to 6.4)
41.5 ( 5.0)
-10.6 (-20.4 to -0.8)
77 (10.1)
54.8 (5.9)
-1.1 (-12.7 to 10.5)
44.8 (6.1)
-1.2 (-13.2 to 10.8)
29.2 ( 9.0)
0.4 (-17.2 to 18.0)
0 mm
140 (18.3)
51.5 (4.3)
0.8 (-7.6 to 9.2)
46.8 (4.4)
-4.4 (-13.0 to 4.2)
37.1 (4.8)
-8.3 (-17.7 to 1.1)
0.1-2 mm
343 (44.8)
64.6 (2.6)
-1.5 (-6.6 to 3.6)
52.4 (2.8)
1.1 ( -4.4 to 6.6)
37.9 (3.7)
1.2 ( -6.1 to 8.5)
> 2 mm
283 (36.9)
66.4 (2.9)
-0.9 (-6.6 to 4.8)
57.5 (3.2)
-1.0 ( -7.3 to 5.3)
39.5 (6.3)
3.4 ( -8.9 to 15.7)
182 (23.8)
53.6 (3.8)
-2.7 (-10.1 to 4.7)
44.5 (3.9)
-3.7 (-11.3 to 3.9)
29.5 (6.1)
-2.3 (-14.3 to 9.7)
184 (24)
69.2 (3.5)
0.1 ( -6.8 to 7.0)
60.3 (3.8)
0.4 ( -7.0 to 7.8)
40.3 (6.0)
6.7 ( -5.1 to 18.5)
400 (52.2)
64.1 (2.5)
-0.4 ( -5.3 to 4.5)
53.7 (2.7)
0.6 ( -4.7 to 5.9)
43.3 (3.1)
-3.3 ( -9.4 to 2.8)
Fig. 2. Survival curves for 4 PI groups
Kaplan-Meier survival curves (solid lines) plotted with the model based survival curves (dotted lines) for 4 different prognostic
index groups. Number of patients at risk were 423, 265, and 33 at 3, 5 and 10 years respectively. Black: patients with good; red:
fairly good; green: fairly poor and blue: poor prognosis.
In the Fine and Gray model, tumour size, surgical margin and radiotherapy show a significant effect on
CILR (Table 4). Bigger tumours are associated with a higher probability of LR with sHR equal to 1.031
(95%CI: 1.001-1.063) for a unit increase of 1 cm. Patients with larger margins have a significantly lower
CILR with sHR equal to 0.635 (95%CI: 0.406-0.992) and 0.282 (95%CI: 0.159-0.500) for 0.1-2mm and
>2mm respectively. Radiotherapy treatment is associated with a lower CILR compared to surgery alone
with sHRs equal to 0.312 (95%CI: 0.146-0.668) and 0.700 (95%CI: 0.417-1.175) for neoadjuvant and
adjuvant radiotherapy respectively.
Table 4. Fine and Gray model for LR. Subdistribution hazard ratio (sHR) along with 95% confidence
interval (n = 766).
P value
Deep and superficial
Synovial sarcoma
Spindle cell sarcoma
0 mm
0.1-2 mm
>2 mm
The sHR of age corresponds to a unit increase of 10 years and the sHR of size corresponds to a unit increase of 1 cm.
MFH/UPS: Malignant Fibrous Histiocytoma/Undifferentiated Pleomorphic Sarcoma; MPNST: malignant peripheral nerve
sheath tumour; RT: radiotherapy. *Depth: relative to the investing fascia.
Calibration plots for LR are shown in Figure 3. Points are scattered around the lower diagonal that lies
within the 95%CIs of the observed cumulative incidence, indicating a good calibration. However, the
small distance between lower risk groups and the fact that groups observed outcome not always
monotonically increases indicate the relative difficulty to discriminate among patients with lower risk
Fig. 3: Calibration plots for local recurrence.
Observed LR is plotted against predicted LR for patients in 8 equal sized risk groups identified by their predicted probability for
LR, as assessed by cross validation.
Figure 4 shows crude cumulative incidence curves (solid lines) and model-based estimates (dotted lines)
computed as the mean predicted cumulative incidence for LR. The high-risk groups can clearly be
distinguished from the rest. However, the curves of the lower risk groups are located very close to each
other, which indicates some difficulty to discriminate between patients with low risk resulting from the
small number of LRs observed in those groups.
Fig. 4: Cumulative incidence of local recurrence for 4 PI groups
Crude cumulative incidence curves (solid lines) plotted with the model based Cumulative incidence curves (dotted lines) for 4
different prognostic index groups. Number of patients at risk were 423, 265, and 33 at 3, 5 and 10 years
respectively. Black: patients with good; red: fairly good; green: fairly poor and blue: poor prognosis.
Figure 5 shows the effect of RT on OS and CILR for two patients with the same risk factors (70 years old, 9
cm tumour size, deep depth, MFH/UPS, resection margin of 0.1-2mm) with and without neoadjuvant RT.
The patient without RT (red lines) has worse OS and higher cumulative incidence of LR.
Fig. 5: Survival and CIFLR for patient of 70 years, tumour size 9cm, deep depth, MFH/UPS, resection
margin 0.1-2mm
In red: curves for patient treated with neoadjuvant RT. In black: patient without RT. Solid lines: survival curves. Dotted lines:
cumulative incidence for LR.
Detailed comparisons of observed and predicted probabilities for LR for data subgroups are shown in
Table 5. No significant differences between observed and predicted outcomes were evident. The C-index
for LR was 0.696 (95%CI 0.629-0.743).
Table 5. Comparing observed cumulative incidence and predicted probabilities of LR, assessed by cross
validation, for subgroups of data at 3, 5 and 10 years.
In cancer care, patient characteristics are generally set at presentation, whereas the combination and
timing of treatment(s) is a clinical decision based on each patient’s specific circumstances. Previously we
developed a multistate model to investigate how these variables affect patient outcomes.13 In this study
we developed the PERSARC model which uniquely presents clinicians with the possibility to accurately
predict outcome of OS and CILR, and compare different treatment modalities, for patients with high-
grade ESTS that undergo surgical resection with curative intent. It clearly shows the possible added value
of (neo)adjuvant radiotherapy at an individual patient level (figure 5). Surgical margins, adjuvant
therapies, and individual baseline characteristics are all incorporated in this model. To assess the
predictive value of this model, internal validation was performed.
n (%)
3 years
Observed (s.e.)
Difference (95%
5 years
Observed (s.e.)
Difference (95%
10 years
Observed (s.e.)
Difference (95%
281 (36.7)
10.2 (1.8)
1.2 ( -2.3 to 4.7)
11.5 (2.0)
1.5 ( -2.4 to 5.4)
12.9 (2.1)
1.9 ( -2.2 to 6.0)
82 (10.7)
12.6 (3.7)
-3.0 (-10.3 to 4.3)
15.7 (4.2)
-4.9 (-13.1 to 3.3)
15.7 (4.2)
-3.3 (-11.5 to 4.9)
403 (52.6)
15.6 (1.8)
-0.1 ( -3.6 to 3.4)
17.4 (2.0)
0.1 ( -3.8 to 4.0)
20.8 (2.8)
-0.9 ( -6.4 to 4.6)
123 (16.1)
8.4 (2.6)
1.5 (-3.6 to 6.6)
11.9 (3.2)
-0.6 (-6.9 to 5.7)
18.2 (5.6)
-5.3 (-16.3 to 5.7)
295 (38.5)
10.1 (1.8)
1.4 (-2.1 to 4.9)
11.9 (2.0)
1.2 (-2.7 to 5.1)
14.1 (2.5)
0.9 ( -4.0 to 5.8)
348 (45.4)
17.8 (2.1)
-1.6 (-5.7 to 2.5)
18.8 (2.1)
-0.5 (-4.6 to 3.6)
19.4 (2.2)
1.3 ( -3.0 to 5.6)
579 (75.6)
13.9 (1.5)
0.0 (-2.9 to 2.9)
15.6 (1.6)
0.1 (-3.0 to 3.2)
17.9 (1.9)
-0.1 (-3.8 to 3.6)
134 (17.5)
12.3 (2.9)
1.1 (-4.6 to 6.8)
14.8 (3.3)
0.4 (-6.1 to 6.9)
16.7 (3.7)
0.6 (-6.7 to 7.9)
Deep and
53 (6.9)
9.6 (4.1)
-1.5 (-9.5 to 6.5)
9.6 (4.1)
-0.4 (-8.4 to 7.6)
9.6 (4.1)
0.9 (-7.1 to 8.9)
238 (31.1)
11.9 (2.1)
0.2 ( -3.9 to 4.3)
12.9 (2.2)
0.8 ( -3.5 to 5.1)
15.7 (3.0)
0.0 ( -5.9 to 5.9)
91 (11.9)
17.7 (4.0)
-2.1 ( -9.9 to 5.7)
17.7 (4.0)
-0.1 ( -7.9 to 7.7)
19.6 (4.3)
0.4 ( -8.0 to 8.8)
Synovial sarcoma
142 (18.5)
4.5 (1.8)
2.7 ( -0.8 to 6.2)
9.1 (2.6)
-0.9 ( -6.0 to 4.2)
10.4 (2.9)
-1.0 ( -6.7 to 4.7)
Spindle cell
167 (21.8)
16.7 (2.9)
0.1 ( -5.6 to 5.8)
19.0 (3.3)
0.0 ( -6.5 to 6.5)
26.3 (7.3)
-4.7 (-19.0 to 9.6)
77 (10.1)
19.3 (4.6)
-1.2 (-10.2 to 7.8)
20.9 (4.8)
-0.6 (-10.0 to 8.8)
20.9 (4.8)
2.1 ( -7.3 to 11.5)
0 mm
140 (18.3)
26.2 (3.8)
-2.3 (-9.7 to 5.1)
26.2 (3.8)
0.7 (-6.7 to 8.1)
26.2 (3.8)
4.1 (-3.3 to 11.5)
0.1-2 mm
343 (44.8)
13.4 (1.9)
1.1 (-2.6 to 4.8)
15.9 (2.0)
0.5 (-3.4 to 4.4)
19.3 (2.6)
-0.6 (-5.7 to 4.5)
> 2 mm
283 (36.9)
6.7 (1.5)
0.1 (-2.8 to 3.0)
8.3 (1.8)
-0.5 (-4.0 to 3.0)
9.1 (1.9)
-0.1 (-3.8 to 3.6)
182 (23.8)
15.3 (2.7)
1.1 (-4.2 to 6.4)
18.6 (3.1)
-0.1 (-6.2 to 6.0)
19.9 (3.3)
1.0 (-5.5 to 7.5)
184 (24)
7.3 (2.0)
-1.3 (-5.2 to 2.6)
7.3 (2.0)
-0.4 (-4.3 to 3.5)
7.3 (2.0)
0.6 (-3.3 to 4.5)
400 (52.2)
15.1 (1.8)
0.3 (-3.2 to 3.8)
17.0 (1.9)
0.4 (-3.3 to 4.1)
21.0 (2.8)
-1.1 (-6.6 to 4.4)
This prognostic model illustrates that as the tumour size increases, the prognosis worsens for LR and OS
with sHR equal to 1.031 (95%CI: 1.001-1.063) and HR equal to 1.068 (95%CI: 1.052-1.085) respectively.
These findings are similar to results reported by other groups.12 As expected, age was an adverse
prognostic risk factor for OS,3 which can only be partially explained by comorbidities. Margins are clearly
associated with LR, and seem to have a marginally significant effect on OS (Table 2 and 4). The effect of
recurrence on OS might be attributed to biological aggressiveness of the tumour rather than margins
itself (Table 2 and 4).1,24
Patients who received radiotherapy seem to have better outcomes than those who did not (Table 2 and
4).25 These patients may have been selected out of the total group of ESTS patients based on clinical
characteristics, presenting scenarios, or capability to undergo neoadjuvant radiotherapy.26 All patients
included in this study were treated at one of the five high volume sarcoma centres following discussion of
their case at a multidisciplinary tumour board. Although selection bias may be present, it only reflects
every day care decisions. There are two prospective randomised trials on this topic; in both studies
adjuvant radiotherapy has shown a decrease in LR but had no significant impact on survival. However,
both studies also included patients with low-grade tumours. Furthermore, due to low number of events
(death) per arm they could only detect a minimal benefit of 20% (as mentioned in the trail that had the
most patients per arm).27,28 Previous studies along with the results from this investigation suggest that
neoadjuvant radiotherapy should be considered at multidisciplinary tumour board discussions for all
patients undergoing surgery for primary high-grade ESTS.25,29-32 Patients treated with neoadjuvant
radiation are at significantly increased risk of wound healing complications, whether they receive
conventional treatment or intensity-modulated radiotherapy.25 Therefore certain patients such as the
elderly, those with significant medical comorbidities, or those with prosthetic implants adjacent to the
location of the sarcoma, may be considered inappropriate candidates for neoadjuvant radiation.
The outcomes presented above must be interpreted with caution, since this model is based on clinical
routine data and is therefore susceptible to selection bias. In addition, margin categories are based on
millimetres and histology was not re-evaluated centrally. Therefore, margin assessment and evaluation of
specific ‘close’ margins to anatomic structures; e.g. periosteum, perineurium, or fascia may be subjective
to variability.26 For patients treated in centres where other margin criteria (e.g. Enneking) are in place,
this model may be less applicable. Further research should focus on evaluating the different classification
methods and agreeing on one standardised margin description for patients with ESTS.33-35
While some patients may accept the increased risk of a LR and potential need for subsequent treatment
by opting for less aggressive therapy including minimal margins, others may want to minimize the risk of
another surgery, for example because of age and comorbidities or because of the potential effect on
survival. These trade-offs are delicate and have to be based on clinical experience and substantial
evidence. The prediction model developed in this study provides some indication about the possible
evolution of the disease and helps in shared decision-making. The Personalised Sarcoma Care model is
freely available in the Appstore and Google apps.
Funding: The Dutch Cancer Society (DCS) - KWF Kankerbestrijding
Role of the funding source:
This funding source had no role in the design of this study as well as any role during its execution,
analyses, interpretation of the data, in the writing of the report, or decision to submit the article for
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Appendix I. Competing risk model
A patient enters the state of being alive with no evidence of disease (ANED) after surgery and may move
to the state of local recurrence (LR) or Death.
... (30) Furthermore, a Dutch group as well as a Scandinavian group mentioned that the prognosis of chondrosarcoma is dependent on whether diagnosis and treatment are conducted by an experienced team. (31,32) Bone sarcoma care by an experienced team can only achieved by centralisation given the low incidence rates. We believe that the effect of centralisation could even be greater if we manage to further improve the concentration of both diagnosis and treatment towards a bone tumour centre. ...
... Tumour location and resectability, metastatic disease at diagnosis, response to chemotherapy and local recurrence are known prognostic factors for osteosarcoma and Ewing sarcoma according to published reports. (8,(26)(27)(28)(29)(30)(31)(32)(33) Most of the diagnostic delay occurred in the prehospital setting at the GP's office. We realize that is very difficult for GPs to recognize a bone malignancy because they generally only encounter one or two primary bone sarcomas in their entire careers. ...
... Finally, a prediction model for clinical guidance to facilitate individualized follow-up would be the next step for high-grade bone sarcoma patients similar to the PERSARC model for soft-tissue sarcoma. (32) In conclusion, our study shows a plateau in new local recurrences and distant metastatic events four years after initial treatment for patients with high-grade osteosarcoma and Ewing sarcoma. Even though our study is based on a nationwide population, collaborative research with larger groups is needed in order to do provide a solid scientific basis for future recommendations for follow-up interval and duration in the heterogenous patient population with bone sarcoma. ...
Full-text available
Contents of this thesis Part 1 Bone sarcoma incidence The establishment of the Dutch bone tumour committee archive and the NCR registry has resulted in extensive data storage of bone sarcoma cases for purposes of quality control, research, and improvement of care. These central registries allowed us to perform two nationwide studies on bone sarcoma incidence. In Chapter 2 we conducted a nationwide NCR registry study that produced comprehensive incidence estimates for all main primary bone sarcomas over a 15-year period in the Netherlands. We assessed the effect of centralisation of care on tumour biopsy and treatment. Chapter 3 is another example of how a central tumour registry like the Dutch bone tumour committee archive can facilitate close study of an extremely rare tumour. The presentation, treatment and outcome of periosteal chondrosarcoma in the Netherlands is described over a 59-year period. Part 2 The impact of centralisation In the second part of this thesis we assessed centralisation of care in terms of time to diagnosis, organisation of bone sarcoma care and follow-up in Europe in a nationwide study on bone sarcoma follow-up. Patients with a bone sarcoma may benefit from diagnosis and treatment by an experienced multidisciplinary team. This emphasises the importance of centralisation of care. Still, centralisation of bone sarcoma care could induce time-to-diagnosis and treatment delays. In Chapter 4 we assessed time to diagnosis and its effect on clinical outcome in high-grade sarcoma of bone in a retrospective single bone sarcoma centre study. Patient-related delay as well as the different types of doctor- related delay were singled out and analysed. In Chapter 5, organisation of care was assessed in several bone sarcoma centres in Europe affiliated with a cross-sectional study with the input of an EMSOS study group. Part 3 Follow-up of bone sarcoma The third and last part of the thesis assessed follow-up in terms of its organisation and sequel of oncological events after bone sarcoma treatment in a nationwide study. In Chapter 6, a cross-sectional study was conducted to assess follow-up of bone sarcoma care in Europe. In Chapter 7 a nationwide NCR registry study was conducted, focusing on follow-up. The aim of the study was to assess the oncological events occurring after index treatment with curative intent during follow-up, including time to local recurrence and distant metastasis, in order to obtain additional evidence to assess current follow- up strategies for high-grade bone sarcomas in the Netherlands. Clinical implications, considerations and future perspectives are discussed in Chapter 8. English and Dutch summaries of this thesis are outlined in Chapter 9 and Chapter 10.
... (2) there may be fear and uncertainty from patients' or physicians' perspectives; and (3) the effectiveness of follow-up to influence overall survival is uncertain. As a first explanation, current ESMO and NCCN guidelines do not distinguish between tumor -and treatment-related risk of disease recurrence, even though the risk for local or metastatic disease recurrence is known to differ significantly between STS patients due to tumor and treatment factors [4,11,[15][16][17][18]. Our results also reflect this, showing that among patients experiencing a recurrence, 84% had a high-grade tumor. ...
... Our study gives some suggestions for which patient groups may be targeted to further explore and reduce overuse. Furthermore, the use of prediction tools such as Sarculator and Personalised Sarcoma Care (PERSARC) make more individualized prediction of LR/DM-risk possible [15,17,18,[30][31][32]. This may facilitate clinicians in developing risk-based follow-up schedules, which can result in less-frequent follow-up visits for low-risk patients. ...
Full-text available
Introduction: Follow-up (FU) in soft-tissue sarcoma (STS) patients is designed for early detection of disease recurrence. Current guidelines are not evidenced-based and not tailored to patient or tumor characteristics, so they remain debated, particularly given concerns about cost, radiation frequency, and over-testing. This study assesses the extent to which STS patients received guideline-concordant FU and to characterize which type of patients received more or fewer visits than advised. Methods: All STS patients surgically treated at the Leiden University Medical Center between 2000-2020 were included. For each patient, along with individual characteristics, all radiological examinations from FU start up to 5 years were included and compared to guidelines. Recurrence was defined as local/regional recurrence or metastasis. Results: A total of 394 patients was included, of whom 250 patients had a high-grade tumor (63.5%). Only 24% of patients received the advised three FU visits in the first year. More FU visits were observed in younger patients and those diagnosed with a high-grade tumor. Among patients with a recurrence, 10% received fewer visits than advised, while 28% of patients without a recurrence received more visits than advised. Conclusions: A minority of STS patients received guideline-concordant FU visits, suggesting that clinicians seem to incorporate recurrence risk in decisions on FU frequency.
... Hence, important differences can be observed in the clinical course and prognosis of patients [27]. Over the years, several prognostic prediction models have been developed for overall survival and local recurrence [28][29][30]. ...
... For this project, a retrospectively collected cohort of 3826 patients with eSTS was used [29]. The dataset contained pseudo-anonymised patients from Leiden University Medical Center (Leiden, the Netherlands), Royal Orthopaedic Hospital (Birmingham and Stanmore, UK), Netherlands Cancer Institute (Amsterdam, the Netherlands), Mount Sinai Hospital (Toronto, Canada), the Norwegian Radium Hospital (Oslo, Norway), Aarhus University Hospital (Aarhus, Denmark), Skåne University Hospital (Lund, Sweden), Medical University Graz (Graz, Austria), Royal Marsden Hospital (London, UK), Daniel den Hoed (Rotterdam, the Netherlands), Radboud University Medical Center (Nijmegen, the Netherlands), University Medical Center Groningen (Groningen, the Netherlands), Haukeland University Hospital (Bergen, Norway), Helios Klinikum Berlin-Buch (Berlin, Germany), MedUni Vienna (Vienna, Austria), Vienna General Hospital (Vienna, Austria). ...
Full-text available
Background In health research, several chronic diseases are susceptible to competing risks (CRs). Initially, statistical models (SM) were developed to estimate the cumulative incidence of an event in the presence of CRs. As recently there is a growing interest in applying machine learning (ML) for clinical prediction, these techniques have also been extended to model CRs but literature is limited. Here, our aim is to investigate the potential role of ML versus SM for CRs within non-complex data (small/medium sample size, low dimensional setting). Methods A dataset with 3826 retrospectively collected patients with extremity soft-tissue sarcoma (eSTS) and nine predictors is used to evaluate model-predictive performance in terms of discrimination and calibration. Two SM (cause-specific Cox, Fine-Gray) and three ML techniques are compared for CRs in a simple clinical setting. ML models include an original partial logistic artificial neural network for CRs (PLANNCR original), a PLANNCR with novel specifications in terms of architecture (PLANNCR extended), and a random survival forest for CRs (RSFCR). The clinical endpoint is the time in years between surgery and disease progression (event of interest) or death (competing event). Time points of interest are 2, 5, and 10 years. Results Based on the original eSTS data, 100 bootstrapped training datasets are drawn. Performance of the final models is assessed on validation data (left out samples) by employing as measures the Brier score and the Area Under the Curve (AUC) with CRs. Miscalibration (absolute accuracy error) is also estimated. Results show that the ML models are able to reach a comparable performance versus the SM at 2, 5, and 10 years regarding both Brier score and AUC (95% confidence intervals overlapped). However, the SM are frequently better calibrated. Conclusions Overall, ML techniques are less practical as they require substantial implementation time (data preprocessing, hyperparameter tuning, computational intensity), whereas regression methods can perform well without the additional workload of model training. As such, for non-complex real life survival data, these techniques should only be applied complementary to SM as exploratory tools of model’s performance. More attention to model calibration is urgently needed.
... Despite optimal clinical management, a substantial proportion of NRSTS patients (up to 50%) with localised disease experience distant relapse following surgery 15 . Stratification of these high-risk patients has been limited to the use of nomograms which consider known prognostic factors including tumour grade, size, histological subtype and age amongst other variables [54][55][56] . There are currently very few molecular prognostic signatures for NRSTS and none which are optimised for AYA patients 57 . ...
Full-text available
Adolescents and young adult (AYA) patients with soft tissue tumours (STT) including sarcomas are an underserved group with disparities in treatment outcomes. To define the molecular features between AYA and older adult (OA) patients, we analysed the proteomic profiles of a large cohort of STT across 10 histological subtypes (AYA n=66, OA n=243). AYA tumours are enriched in proteins involved in mitochondrial metabolism while OA patients have elevated inflammatory and cell cycle signalling. By integrating the patient-level proteomic data with functional genomic profiles from sarcoma cell lines, we show that the mRNA splicing pathway is an intrinsic vulnerability in cell lines from OA patients and that components of the spliceosome complex are independent prognostic factors for metastasis free survival in AYA patients. Our study highlights the importance of performing age-specific molecular profiling studies to identify risk stratification tools and targeted agents tailored for the clinical management of AYA patients.
... In 2017, van Praag et al. used an international multicentric cohort to develop a model (not a nomogram) that predicts the cumulative incidence of LR and OS for patients with high-grade eSTS [38]. In contrast to Cahlon et al.'s MSKCC nomogram, the PERSARC model is also applicable to patients who have received medical treatments, making it the only prognostic tool capable of predicting the local outcome after surgical resection in patients with eSTS who received (neo)adjuvant ChT or RT. ...
Full-text available
Reliable tools for prognosis prediction are crucially needed by oncologists so they can tailor individual treatments. However, the wide spectrum of histologies and prognostic behaviors of sarcomas challenges their development. In this field, nomograms could definitely better account for their granularity compared to the more widely used AJCC/UICC TNM staging system. Nomograms are predictive tools that incorporate multiple risk factors and return a numerical probability of a clinical event. Since the development of the first nomogram in 2002, several other nomograms have been built, either general, site-specific, histology-specific, or both. Recently, some new “dynamic” nomograms and prognostic tools have been developed, allowing doctors to “recalculate” a patient’s prognosis by taking into account the time since primary surgery, the event history, and the potential time-dependent effect of covariates. Due to these new tools, prognosis prediction is no longer limited to the time of the first computation but can be adapted and recalculated based on the occurrence (or not) of any event as time passes from the first computation. In this review, we aimed to give an overview of the available nomograms for STS and to help clinicians in the process of selecting the best tool for each patient.
... A recently developed and externally validated ESTS nomogram (SARCULATOR) from 1452 patients treated in four oncology centres across the world aided in the prediction of 5-and 10-year overall survival (OS) and probability of distance recurrences based on patient's age, tumor size, FNCLCC grade, and histological subtype for the macroscopically completely resected ESTS [8]. The PERsonalised SARcoma Care (PERSARC) nomogram is another extremity-specific nomogram that has been internally validated and accounts for treatment modalities such as radiotherapy and achieved surgical margins for predicting OS and local recurrence (LR) at 3, 5, and 10 years among adults [9]. Both nomograms have been incorporated into free applications for clinicians to use. ...
Full-text available
Opinion statement Extremity soft tissue sarcoma (ESTS) constitutes the majority of patients with soft tissue sarcoma (STS). Patients with localized high-grade ESTS > 5 cm in size carry a substantial risk of developing distant metastasis on follow-up. A neoadjuvant chemoradiotherapy approach can enhance local control by facilitating resection of the large and deep locally advanced tumors while trying to address distant spread by treating the micrometastasis for these high-risk ESTS. Preoperative chemoradiotherapy and adjuvant chemotherapy are often used for children with intermediate- or high-risk non-rhabdomyosarcoma soft tissue tumors in North America and Europe. In adults, the cumulative evidence supporting preoperative chemoradiotherapy or adjuvant chemotherapy remains controversial. However, some studies support a possible benefit of 10% in overall survival (OS) for high-risk localized ESTS, especially for those with a probability of 10-year OS < 60% using validated nomograms. Opponents of neoadjuvant chemotherapy argue that it delays curative surgery, compromises local control, and increases the rate of wound complications and treatment-related mortality; however, the published trials do not support these arguments. Most treatment-related side effects can be managed with adequate supportive care. A coordinated multidisciplinary approach involving sarcoma expertise in surgery, radiation, and chemotherapy is required to achieve better outcomes for ESTS. The next generation of clinical trials will shed light on how comprehensive molecular characterization, targeted agents and/or immunotherapy can be integrated into the upfront trimodality treatment to improve outcomes. To that end, every effort should be made to enroll these patients on clinical trials, when available.
... Data are considered the key driver for the evolution from one-size-fits-all to precision medicine [21,31]. Predicting outcomes has been recognized as a major focus in today's health care, and currently, nomograms are still routinely used in daily sarcoma practice [32,33]. However, these are most often based on retrospective data, sometimes even more than 20 years old, without harmonized data acquisition and definitions and with all institutions independent from each other. ...
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Sarcomas represent a large group of rare to very rare diseases, requiring complex management with a transdisciplinary approach. Overall progress has been hampered because of discipline, institution and network fragmentation, and there is no global data harmonization or quality standards. To report on and improve quality, a common definition of quality indicators (QIs) of sarcoma care as well as the capacity to assess longitudinal real-time data is required. An international advisory board of world-renowned sarcoma experts defined six categories of QIs, totaling more than 80 quality indicators. An interoperable (web-based) digital platform was then created combining the management of the weekly sarcoma board meeting with the sarcoma registry and incorporating patient-reported outcome measures (PROMs) into the routine follow-up care to assess the entire care cycle of the patient. The QIs were then programmed into the digital platform for real-time analysis and visualization. The definition of standardized QIs covering all physician- (diagnostics and therapeutics), patient- (PROMS/PREMS), and cost-based aspects in combination with their real-time assessment over the entire sarcoma care cycle can be realized. Standardized QIs as well as their real-time assessment and data visualization are critical to improving the quality of sarcoma care. By enabling predictive modelling and introducing VBHC, precision health care for a complex disease is on the horizon.
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Introduction Current treatment decision-making in high-grade soft-tissue sarcoma (STS) care is not informed by individualised risks for different treatment options and patients’ preferences. Risk prediction tools may provide patients and professionals insight in personalised risks and benefits for different treatment options and thereby potentially increase patients’ knowledge and reduce decisional conflict. The VALUE-PERSARC study aims to assess the (cost-)effectiveness of a personalised risk assessment tool (PERSARC) to increase patients’ knowledge about risks and benefits of treatment options and to reduce decisional conflict in comparison with usual care in high-grade extremity STS patients. Methods The VALUE-PERSARC study is a parallel cluster randomised control trial that aims to include at least 120 primarily diagnosed high-grade extremity STS patients in 6 Dutch hospitals. Eligible patients (≥18 years) are those without a treatment plan and treated with curative intent. Patients with sarcoma subtypes or treatment options not mentioned in PERSARC are unable to participate. Hospitals will be randomised between usual care (control) or care with the use of PERSARC (intervention). In the intervention condition, PERSARC will be used by STS professionals in multidisciplinary tumour boards to guide treatment advice and in patient consultations, where the oncological/orthopaedic surgeon informs the patient about his/her diagnosis and discusses benefits and harms of all relevant treatment options. The primary outcomes are patients’ knowledge about risks and benefits of treatment options and decisional conflict (Decisional Conflict Scale) 1 week after the treatment decision has been made. Secondary outcomes will be evaluated using questionnaires, 1 week and 3, 6 and 12 months after the treatment decision. Data will be analysed following an intention-to-treat approach using a linear mixed model and taking into account clustering of patients within hospitals. Ethics and dissemination The Medical Ethical Committee Leiden-Den Haag-Delft (METC-LDD) approved this protocol (NL76563.058.21). The results of this study will be reported in a peer-review journal. Trial registration number NL9160, NCT05741944 .
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Soft tissue sarcomas (STS) are rare and diverse mesenchymal cancers with limited treatment options. Here we undertake comprehensive proteomic profiling of tumour specimens from 321 STS patients representing 11 histological subtypes. Within leiomyosarcomas, we identify three proteomic sub-types with distinct myogenesis and immune features, anatomical site distribution and survival outcomes. Characterisation of undifferentiated pleomorphic sarcomas and dedifferentiated liposarcomas with low infiltrating CD3 + T-lymphocyte levels nominates the complement cascade as a candidate immunotherapeutic target. Comparative analysis of proteomic and tran-scriptomic profiles highlights the proteomic-specific features for optimal risk stratification in angiosarcomas. Finally, we define functional signatures termed Sarcoma Proteomic Modules which transcend histological subtype classification and show that a vesicle transport protein signature is an independent prognostic factor for distant metastasis. Our study highlights the utility of proteomics for identifying molecular subgroups with implications for risk stratification and therapy selection and provides a rich resource for future sarcoma research.
Aims: The aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients. Methods: Demographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset. Results: A total of 13,646 patients with STS from the SEER database were included, of whom 35.9% experienced five-year cancer-related mortality. The random forest model performed the best overall and identified tumour size as the most important variable when predicting mortality in patients with STS, followed by M stage, histological subtype, age, and surgical excision. Each variable was significant in logistic regression. External validation yielded an AUC of 0.752. Conclusion: This study identified clinically important variables associated with five-year cancer-related mortality in patients with limb and trunk STS, and developed a predictive model that demonstrated good accuracy and predictability. Orthopaedic oncologists may use these findings to further risk-stratify their patients and recommend an optimal course of treatment.
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Objectives This study investigates the effect of surgical margins and radiotherapy, in the presence of individual baseline characteristics, on survival in a large population of high-grade soft tissue sarcoma of the extremities using a multistate model. Design A retrospective multicentre cohort study. Setting 4 tertiary referral centres for orthopaedic oncology. Participants 687 patients with primary, non-disseminated, high-grade sarcoma only, receiving surgical treatment with curative intent between 2000 and 2010 were included. Main outcome measures The risk to progress from ‘alive without disease’ (ANED) after surgery to ‘local recurrence’ (LR) or ‘distant metastasis (DM)/death’. The effect of surgical margins and (neo)adjuvant radiotherapy on LR and overall survival was evaluated taking patients' and tumour characteristics into account. Results The multistate model underlined that wide surgical margins and the use of neoadjuvant radiotherapy decreased the risk of LR but have little effect on survival. The main prognostic risk factors for transition ANED to LR are tumour size (HR 1.06; 95% CI 1.01 to 1.11 (size in cm)) and (neo)adjuvant radiotherapy. The HRs for patients treated with adjuvant or no radiotherapy compared with neoadjuvant radiotherapy are equal to 4.36 (95% CI 1.34 to 14.24) and 14.20 (95% CI 4.14 to 48.75), respectively. Surgical resection margins had a protective effect for the occurrence of LR with HRs equal to 0.61 (95% CI 0.33 to 1.12), and 0.16 (95% CI 0.07 to 0.41) for margins between 0 and 2 mm and wider than 2 mm, respectively. For transition ANED to distant metastases/Death, age (HR 1.64 (95% CI 0.95 to 2.85) and 1.90 (95% CI 1.09 to 3.29) for 25–50 years and >50 years, respectively) and tumour size (1.06 (95% CI 1.04 to 1.08)) were prognostic factors. Conclusions This paper underlined the alternating effect of surgical margins and the use of neoadjuvant radiotherapy on oncological outcomes between patients with different baseline characteristics. The multistate model incorporates this essential information of a specific patient's history, tumour characteristics and adjuvant treatment modalities and allows a more comprehensive prediction of future events.
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Background: Predicting breast cancer outcome in older patients is challenging, as it has been shown that the available tools are not accurate in older patients. The PREDICT tool may serve as an alternative tool, as it was developed in a cohort that included almost 1800 women aged 65 years or over. The aim of this study was to assess the validity of the online PREDICT tool in a population-based cohort of unselected older patients with breast cancer. Methods: Patients were included from the population-based FOCUS-cohort. Observed 5- and 10-year overall survival were estimated using the Kaplan-Meier method, and compared with predicted outcomes. Calibration was tested by composing calibration plots and Poisson Regression. Discriminatory accuracy was assessed by composing receiver-operator-curves and corresponding c-indices. Results: In all 2012 included patients, observed and predicted overall survival differed by 1.7%, 95% confidence interval (CI)=-0.3-3.7, for 5-year overall survival, and 4.5%, 95% CI=2.3-6.6, for 10-year overall survival. Poisson regression showed that 5-year overall survival did not significantly differ from the ideal line (standardised mortality ratio (SMR)=1.07, 95% CI=0.98-1.16, P=0.133), but 10-year overall survival was significantly different from the perfect calibration (SMR=1.12, 95% CI=1.05-1.20, P=0.0004). The c-index for 5-year overall survival was 0.73, 95% CI=0.70-0.75, and 0.74, 95% CI=0.72-0.76, for 10-year overall survival. Conclusions: PREDICT can accurately predict 5-year overall survival in older patients with breast cancer. Ten-year predicted overall survival was, however, slightly overestimated.British Journal of Cancer advance online publication, 19 January 2016; doi:10.1038/bjc.2015.466
The management of recurrent soft tissue sarcoma is a challenging problem for clinicians and has a significant physical, mental, emotional, and oncologic impact for the patient. Despite excellent limb-preservation therapies, approximately one-quarter of patients may eventually develop recurrence of disease. How to most appropriately manage these patients is a matter of debate. Several treatment options exist, including surgical resection, irradiation, systemic chemotherapy, amputation, and regional therapies. This article highlights the management of recurrent extremity soft tissue sarcoma.
Background: Patients with soft tissue sarcoma (STS) being treated following the standardized guidelines can still not be guaranteed to remain free from local recurrence (LR). A complete tumour resection has been accepted as a major prognostic factor for LR. This retrospective study was designed to analyse the influence of two different classifications of resection margins (R-classification and UICC-classification) on LR in STS patients. Materials and methods: Of 411 patients treated at our institution for STS, 265 were eligible for statistical analysis. Kaplan-Meier curves and Cox regression models were used to assess the impact of an R0 resection according to the R-classification (resection margin clear but allowing <1 mm) and according to the UICC-classification (minimal resection margin ≥1 mm) on LR. Results: Survival curves showed a lower LR rate for R0 resections in the UICC-classification, namely 1.3%, 12% and 12% as compared to 2.1%, 9.5% and 16.5% for the R-classification. In multivariate analysis calculated separately for each classification, R1 resection as defined by the R-classification (HR: 11.214; 95%CI: 2.394-52.517; p = 0.002) as well as by UICC-classification (HR: 15.634; 95%CI: 2.493-98.029; p = 0.003) remained significant. Conclusion: In our study, margin status according to both classifications represents an independent prognostic factor for LR in patients with STS following curative surgery. Local control rates were superior after a minimal resection margin of 1 mm (R0 by UICC-classification) compared to R0 resections after the R-classification.
Background: The current American Joint Committee on Cancer/Union for International Cancer Control (AJCC/UICC) staging system does not have sufficient details to encompass the variety of soft-tissue sarcomas, and available prognostic methods need refinement. We aimed to develop and externally validate two prediction nomograms for overall survival and distant metastases in patients with soft-tissue sarcoma in their extremities. Methods: Consecutive patients who had had an operation at the Istituto Nazionale Tumori (Milan, Italy), from Jan 1, 1994, to Dec 31, 2013, formed the development cohort. Three cohorts of patient data from the Institut Gustave Roussy (Villejuif, France; from Jan 1, 1996, to May 15, 2012), Mount Sinai Hospital (Toronto, ON, Canada; from Jan 1, 1994, to Dec 31, 2013), and the Royal Marsden Hospital (London, UK; from Jan 1, 2006, to Dec 31, 2013) formed the external validation cohorts. We developed the nomogram for overall survival using a Cox multivariable model, and a Fine and Gray multivariable model for the distant metastases nomogram. We applied a backward procedure for variables selection for both nomograms. We assessed nomogram model performance by examining overall accuracy (Brier score), calibration (calibration plots and Hosmer-Lemeshow calibration test), and discrimination (Harrell C index). We plotted decision curves to evaluate the clinical usefulness of the two nomograms. Findings: 1452 patients were included in the development cohort, with 420 patients included in the French validation cohort, 1436 patients in the Canadian validation cohort, and 444 patients in the UK validation cohort. In the development cohort, 10-year overall survival was 72·9% (95% CI 70·2-75·7) and 10-year crude cumulative incidence of distant metastases was 25·0% (95% CI 22·7-27·5). For the overall survival nomogram, the variables selected applying a backward procedure in the multivariable Cox model (patient's age, tumour size, Fédération Française des Centres de Lutte Contre le Cancer [FNCLCC] grade, and histological subtype) had a significant effect on overall survival. The same variables, except for patient age, were selected for the distant metastases nomogram. In the development cohort, the Harrell C index for overall survival was 0·767 (95% CI 0·743-0·789) and for distant metastases was 0·759 (0·736-0·781). In the validation cohorts, the Harrell C index for overall survival and distant metastases were 0·698 (0·638-0·754) and 0·652 (0·605-0·699; French), 0·775 (0·754-0·796) and 0·744 (0·720-0·768; Canadian), and 0·762 (0·720-0·806) and 0·749 (0·707-0·791; UK). The two nomograms both performed well in terms of discrimination (ability to distinguish between patients who have had an event from those who have not) and calibration (accuracy of nomogram prediction) when applied to the validation cohorts. Interpretation: Our nomograms are reliable prognostic methods that can be used to predict overall survival and distant metastases in patients after surgical resection of soft-tissue sarcoma of the extremities. These nomograms can be offered to clinicians to improve their abilities to assess patient prognosis, strengthen the prognosis-based decision making, enhance patient stratification, and inform patients in the clinic. Funding: None.
Background: Limb-sparing surgery in combination with radiation therapy is a well-established treatment for high-grade soft tissue sarcomas of the extremities. But selection of cases and optimal sequence of irradiation and surgery still remain controversial. Methods: 769 patients with a high-grade soft tissue sarcoma of the extremities, who underwent a limb-sparing surgery, were retrospectively reviewed. Group 1 (N = 89) was treated with neo-adjuvant radiation therapy, group 2 (N = 315) with adjuvant irradiation and group 3 (N = 365) with surgery alone. Results: After a mean follow up of 45 months 95 local recurrences occurred resulting in a local recurrence-free survival of 83.2% after 5 years and 75.9% after 10 years. Contaminated surgical margins (Odds ratio: 2.42) and previous inadequate surgeries (Odds ratio: 1.89) were identified as risk factors for failed local control. Neo-adjuvant radiation therapy provides the best local recurrence-free rate for 5 years (90.0%), whereas after 10 years (78.3%) adjuvant irradiation showed better local control. The metastatic-free rate was independent from achieved surgical margins (p = 0.179). Group 1 showed the highest rate of revision surgery (9.0%), followed by group 3 (5.5%) and group 2 (4.4%) (p = 0.085). However, the rate of irradiation-correlated side effects was higher in group 2 (15.2%) than in group 1 (11.2%) (p = 0.221). Conclusion: Surgery has to be effective for successful local control and remains the mainstay of the treatment in combination with neo-adjuvant as well as adjuvant irradiation. In really wide or even radical resections the benefit of radiation therapy can be discussed as the irradiation induced side effects are not negligible.