ESTRO 36 Abstract Book

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S119 ESTRO 36 2017
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We retrospectively analyzed outcomes in patients with a
primary or recurrent non-small cell lung cancer measuring
≥5 cm, who were treated with 5 or 8 fractions of SABR at
a single center, between 2003-2014. Patients who had
prior thoracic radiotherapy were excluded. The maximum
tumor diameter in the axial, transversal, or sagittal planes
was measured on lung window-level settings in the end-
inspiratory phase of the 10-phase free-breathing four-
dimensional planning CT scan. Between 2003-2008, SABR
was delivered using 8-12 non-coplanar static conformal
beams and stereoscopic X-ray image-guidance, and after
2008, Volumetric Modulated Arc Therapy was used with
online cone-beam CT based positioning on the tumor. All
cases with potential severe toxicity (i.e. grade 3 or higher,
≥G3) were evaluated by a clinical panel consisting of three
clinicians using the Common Terminology Criteria for
Adverse Events version 4.03, and was consensus based.
Results
63 consecutive patients with a median tumor diameter of
5.8 cm (range 5.1-10.4) were identified; 81% had T2N0
disease, and 18% T3N0. Median Charlson Comorbidity
Index was 2 (range 0-6). After a median follow-up of 54.7
months, median survival was 28.3 months (95% CI 18.3-
38.2). For T2b tumors, median OS was 28.7 months (95%
CI 12.2-45.3), and for T3 tumors this was 21.5 months (95%
CI 16.4-26.6). Disease free survival at 2 years was 82.1%,
and local, regional, and distant control rates at 2 years
were 95.8%, 93.7%, and 83.6%, respectively. Distant
metastases only were the commonest pattern of failure
(10%). ≥G3 toxicity was recorded in 30% of patients, with
radiation pneumonitis the most common toxicity (19%). A
likely (n = 4) or possible (n = 8) treatment-related death
was scored in 19% of patients. Retrospective review of CT
scans revealed pre-existing interstitial lung disease in 8
patients (13%), with fatal toxicity developing in 5 of them
(63%).
Conclusion
Lung SABR in tumors ≥5 cm resulted in high local control
rates and acceptable survival outcomes, except in
patients with co-existing interstitial lung disease. Our
findings indicate that a more systematic screening for
interstitial lung disease should take place prior to referral
for SABR.
PV-0239 Validation of lung cancer survival models in a
clinical routine SBRT population
J. Van Soest1, T. Purdie2, M. Giuliani2, P. Lindsay2, A.
Hope2, D. Jaffray2, A. Dekker1
1 Maastricht University Medical Centre+, Department of
Radiotherapy MAASTRO - GROW School for Oncology &
Developmental Biology, Maastricht, The Netherlands
2 University Health Network, Radiation Medicine
Program, Toronto, Canada
Purpose or Objective
In recent years, many different prediction models have
been developed based on clinical trial data. Although
some of these models have been validated in external
datasets, most of these validations did not report whether
this validation tested reproducibility (same cohort
characteristics for training and validation set), or
transferability (different cohort characteristics). In this
work, we performed an external validation of a survival
prediction model learned on Stage III NSCLC patients in a
routine clinical dataset of lung SBRT patients.
Material and Methods
Inclusion criteria were all patients with clinical T1-2N0M0
treated with SBRT from January 2005 to March 2014. The
survival prediction model under validation was published
before (PMID 25936599), including its dataset. Cohorts
(original training and current validation) were compared
using a KM curve and by calculating the cohort differences
AUC (PMID 25179855). This cohort differences model
predicts whether patients belong to the training or
validation dataset. If this model has a high AUC, it means
that the model is able to predict whether a patient
belongs to the training/validation cohort; indicating large
cohort differences and testing for transferability of the
model. If the AUC is low (close to 0.5), the cohort
differences are small, and therefore validation tests
reproducibility in a similar patient cohort.
For validation, we applied the model to predict survival at
several endpoints (6 months, 1, 2 and 3 years), and put
the results into context with the cohort differences AUC.
Finally, we learned a new logistic regression model for 2-
year survival, using stepwise AIC as variable selection
method.
Results
When investigating cohort differences, the KM curve in
figure 1 already shows a difference between the Stage III
training cohort, and the current clinical routine validation
cohort of SBRT patients. Both in terms of survival and
follow-up. The cohort differences AUC was 0.97;
indicating an almost perfect prediction whether a patient
belonged to the training or validation cohort. This
indicates a large difference between patients in the
training and validation cohort.
Table 1 shows the model performance on the validation
dataset, indicating a decline in the current validation
dataset (original model training & validation AUC: 0.64
and 0.58-0.60, respectively). Learning a model for 2-year
survival on this SBRT cohort increased the AUC (0.73,
n=267, events=97).
Conclusion
Our current validation shows that the prediction model
under investigation, learned using Stage III NSCLC
patients, shows an equal performance in a routine clinical
cohort of SBRT patients. Based on the cohort differences
AUC, we conclude that the previous model is transferable
to another patient population. Preliminary investigations
show that a specific model for SBRT patients could
increase model performance. Therefore future work is to
further refine training, and externally validate a new
model for NSCLC patients treated with SBRT.
PV-0240 A logistic regression model to predict 30-day
mortality: difference between routine and trial data
A. Jochems1, I. El-Naqa 2, M. Kessler2, C. Mayo2, J.
Reeves2, J. Shruti2, M. Matuszak 2, R. Ten Haken2, C.
Faive-Fin3, G. Price3, L. Holloway4, S. Vinod5, M. Field4,
M. Samir Barakat 4, D. Thwaites6, A. Dekker1, P. Lambin1
1MAASTRO Clinic, Radiotherapy, Maastricht, The
Netherlands
2University of Michigan, Radiation oncology, Ann-Arbor,