Predicting xerostomia in head and neck cancer using imaging biomarkers from daily tomotherapy MVCTs
Berger, T. Noble, D. J. Shelley, L. E. McMullan, T. Romanchikova, M. Carruthers, L. J. Cebamanos, L. Beckett, G. Duffton, A. Paterson, C.
Berger, T.
Noble, D. J.
Shelley, L. E.
McMullan, T.
Romanchikova, M.
Carruthers, L. J.
Cebamanos, L.
Beckett, G.
Duffton, A.
Paterson, C.
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Abstract
Purpose or Objective
There is a growing interest in predicting late xerostomia in
head and neck (H&N) cancer patients using image
biomarkers (IMBs) calculated on CT images acquired at
sparse intervals over the course of radiotherapy. In
delivering radiotherapy with image guidance (IG) the
TomoTherapy (Accuray, Sunnyvale, CA, USA) system allows
daily IG mega-voltage CT (MVCT) scans to be acquired with
ease. The aim of this study was to 1) investigate whether changes in daily MVCT IMBs calculated on the parotid
glands could predict late xerostomia (grade>1) and 2)
establish the predictive power of the IMBs at different
intervals of the radiotherapy course.
Material and Methods
As illustrated in Figure 1, IMBs (N=73) consisting of first
and higher order features were calculated by textural
analysis on the parotid glands of daily MVCT
images (0.76x0.76x6mm) of 60 H&N cancer patients. All
patients were treated in 30 fractions (fx) with CTCAE
toxicity recorded at 12 months. Linear regression was
calculated on the IMBs over 6 different time intervals of
treatment (first 5, 10, 15, 20, 25 and 30 fx) and the slope
of the regression extracted. To select the best (<9) IMBs
for prediction on each interval, LASSO analysis was used
with 4-fold cross validation (100 times). To evaluate the
robustness of each of the 100 combinations, the patients
were split into four folds. The training set made up of 3
folds and the testing set, the remaining fold, were used to
test the predictive power of each of the LASSO
combinations using a logistic regression model. This was
evaluated by the Area Under the Curve (AUC), resulting in
4 AUCtest and 4 AUCtrain values which were averaged:
mAUCtest and mAUCtrain. For robustness, this process was
repeated 100 times for each combination of predictors and
the median mAUCtest and mAUCtrain, calculated. The 30
combinations with the highest median mAUC were
selected for each interval and their median mAUC
distributions compared using a t-test.
Results Of the 60 patients, 27% reported xerostomia (grade>1) at
12 months. The highest median mAUCtrain / mAUCtest was
0.93/0.80, 0.90/0.75, 0.89/0.73, 0.93/0.82, 0.90/0.75
and 0.94/0.84 for the slopes of the first 5, 10, 15, 20, 25,
30 fractions, respectively. As shown in Figure 2, the best
performing 30 combinations of predictors for the first 5,
20 and 30 fractions performed better in median
mAUCtrain and mAUCtest compared to the others. However,
compared to the first 5 fractions the AUC distributions for
30 and 20 fx were not significantly higher (p>0.05).
Conclusion Conclusion
These preliminary results indicate that it may be possible
to predict late xerostomia in H&N cancer patients using
IMBs calculated on daily MVCT images. It is interesting to
note that the highest AUCs, which may indicate the
strongest predictors of late xerostomia, were present after
the first 5 fractions. These findings should be confirmed on
a larger cohort but open up the prospect of setting
adaptation protocols in H&N cancer after just one week.
Description
Date
2020
Publisher
Collections
Keywords
Type
Meetings and Proceedings
Citation
Berger T, Noble DJ, Shelley LE, McMullan T, Romanchikova M, Carruthers LJ, et al. PO-1584: Predicting xerostomia in head and neck cancer using imaging biomarkers from daily tomotherapy MVCTs. Radiotherapy and Oncology . 2020 Nov;152:S859–60.