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ESTRO 36 2017
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OC-0140 Updating QUANTEC and clinically adjusted
QUANTEC models for pneumonitis at external
validation
A. Van Der Schaaf
1
, J. Lodeweges
1
, A. Niezink
1
, J.
Langendijk
1
, J. Widder
1
1
UMCG University Medical Center Groningen, Radiation
Oncology, Groningen, The Netherlands
Purpose or Objective
To externally validate and eventually recalibrate and
update the original QUANTEC pneumonitis (QP) model
(Marks et al, IJROBP 2010) and the QUANTEC model
adjusted for clinical risk factors (AQP; Appelt et al, Acta
Oncol 2014) in a cohort treated with 3D-CRT, IMRT, or
VMAT, combined in 90% with chemotherapy.
Material and Methods
The external validation cohort was composed of n=220
patients with lung cancer (NSCLC, SCLC) stages (II-)III with
complete dosimetric and prospectively scored
pneumonitis data (G2 or higher), treated from 2013 to
2016 within the framework of a prospective data
registration program (clinicaltrials.gov NCT02421718).
Model performance was tested for discrimination (area
under the curve, AUC), (pseudo-)explained variance
(Nagelkerke’s R
2
), and calibration (Hosmer-Lemeshow
test, HL-test), before and after intercept and slope
recalibration. Then, updating was performed by first
refitting the coefficients from the AQP-model to our data,
then stepwise manually removing unnecessary variables,
followed by adding new potential variables. The
procedure was then repeated automatically using Akaike
and Bayes Information Criteria (AIC, BIC), respectively.
Resulting models were in turn internally validated to
correct AUC and R
2
for optimism using bootstrapping with
backward elimination based on AIC.
Results
After recalibration of intercept and slope, the QP-model
predicting pneumonitis based exclusively on mean lung
dose (MLD) performed well (AUC=0.77; R
2
=0.21; HL-test:
p=0.38), while without recalibration the model would not
fit our data (HL-test: p<0.001). The AQP-model needed
recalibration of the intercept only, but discriminated
worse and explained less variance (AUC=0.72; R
2
=0.16)
compared with the recalibrated QP-model. This suggested
the need to add different factors to improve
discrimination. Using restrictive (BIC) analysis, the final
model contained smoking status (current vs
former&never) and MLD (AUC=0.78; R
2
=0.23). At less
restrictive analysis (AIC), age, total-lung-volume, V5 and
V30 of the heart, sequential chemotherapy, and MLD
might be useful; in addition, MLD may be replaced by
ipsilateral-lung V20 and total-lung V5. At internal
validation, this latter model rendered AUC=0.80 and
R
2
=0.28, however with much higher correction for
optimism, implying potentially decreased generalizability
to other cohorts.
Conclusion
Intending external validation, both the QP and the AQP-
models needed recalibration (of slope and intercept, and
of intercept only, respectively), which might be explained
by employment of modern RT techniques and 90%
administration of chemoradiotherapy in our cohort. A
conservatively improved pneumonitis model employing
modern chemoradiotherapy-techniques includes MLD and
current-smoking status (Figure).
OC-0141 Validation of dose-sensitive heart regions
affecting survival in SABR lung cancer patients
A. McWilliam
1
, J. Kennedy
2
, C. Faivre-Finn
1
, M. Van Herk
1
1
The University of Manchester, Division of Molecular and
Clinical Cancer Science- Faculty of Biology- Medicine and
Health, Manchester, United Kingdom
2
The Christie NHS Foundation Trust, Department of
Informatics, Manchester, United Kingdom
Purpose or Objective
Recent advances in radiotherapy allow an increasing
proportion of lung cancer patients to be treated with
curative intent. However, evidence is emerging that dose
to critical organs may be influencing patient survival. The
authors recently presented their work identify a dose
sensitive sub-region located in the base of the heart where
excess dose resulted in worse patient survival (McWilliam
IJROBP 96(2S):S48-S49). This work aims to determine
whether the same effect was observed in patients treated
with Stereotactic Ablative Radiotherapy (SABR), thereby
validating our previous results.
Material and Methods
The previous work used 1101 non-small cell lung cancer
patients treated with 55Gy in 20 fractions. Validation was
performed in 89 SABR patients treated with 60Gy in 5
fractions. For both groups, CT scans and dose distributions