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errors below 2.5Gy. For proton H&N plans, a dataset size
of at least 173 plans resulted in all mean errors below
2.5Gy. Dataset sizes are shown in Table 1. Shown visually
in Figure 1, using predictive modeling of the plan
outcome, re-planning a lung SBRT case resulted in
improved dose to critical structures while maintaining
coverage to the PTV, compared to the clinically-developed
and treated plans.
Error Target (Gy) 5 4 3 2.5 2
Lung dataset size 16 46 61 69 74
HN Proton size 169 130 153 173 192
HN Photon size 68 136 145 121 136
Table 1:
Plan datasets required for desired dose accuracy.
Figure 1 Comparison of clinical plan developed without
(A) and with (B) predictive modeling.
Conclusion
We have demonstrated the ability to predict dosimetric
indices. These results have clinical implications that
extend from decision making to planning workflow
improvement to quality improvement.
OC-0254 Prospective validation of independent DVH
prediction for QA of automatic treatment planning
Y. Wang
1
, B.J.M. Heijmen
1
, S.F. Petit
1
1
Erasmus MC - Cancer Institute, Radiation Oncology,
Rotterdam, The Netherlands
Purpose or Objective
In our institute, fully automated, knowledge-based
treatment planning is used in routine clinical practice. For
the majority of patients, this is expected to result in high
quality treatment plans. However, technical and
procedural issues might result in suboptimal plans for
some patients that might go undetected. In this study, we
prospectively investigated the clinical usefulness of an
independent DVH prediction tool to detect outliers in
treatment plan quality for prostate cancer patients.
Material and Methods
All prostate cancer patients treated from January 2015 till
half September 2016 with the full prescribe ed dose
delivered to the prostate only or to the prostate+seminal
vesicles were included in the study. They were treated
with an automatically generated VMAT or dMLC plan. The
QA method was based on overlap volume histogram and
principal component analysis and is fully independent of
the planning method. The model was trained with 50% of
the patients treated in 2015 (N=22) and validated on the
other 50% (N=21). We focused on 5 different dose metrics:
rectum D
mean
, V
65
, V
75
; anus D
mean
and bladder D
mean
.
Next, to study the clinical usefulness of treatment
planning QA, the QA model was applied prospectively for
the patients treated in 2016 (N=50). Patients for which at
least one of the five dose metrics fell outside the 90%
prediction confidence interval (CI) were further improved
by manual plan adjustments (‘re-planning’). The re-
planning goals were to keep or improve
all
dose metrics of
interest within or lower than the 90% CI, and anyway not
deteriorate them by more than 1Gy/1% compared to the
original plan. Given the 5 parameters of interest and the
90% criteria, (1- (0.9)
5
)≈40% patients were expected to
fall outside the prediction range.
Results
Figure 1 shows the results of the model validation.
17 Patients from the prospective cohort were classified as
outliers, including all four patients with metal hips, which
were excluded from further analysis. The remaining
outliers 13/46 (28.3%) were re-planned and for all the re-
planning requirements (above) were met. As shown in
Figure 2, the new plans were moderately superior to the
clinical plans for rectum D
mean
(average improvement
0.9Gy, max. improvement 3.0Gy,
p
=0.009), V
65
(2.4%, max
4.2%,
p
=0.001), anus D
mean
(1.5Gy, max 6.8Gy,
p=
0.004),
and bladder D
mean
(1.7Gy, max 5.1Gy,
p
=0.001). The
rectum V
75
of the new plans was slightly higher than with
the original plan (0.2 %,
p
=0.028). No significant
differences were found in PTV conformity or the femoral
head D
max
.