  We determine how prediction methods combine with optimization methods in
two-stage knowledge-based planning (KBP) pipelines to produce radiation therapy
treatment plans. We trained two dose prediction methods, a generative
adversarial network (GAN) and a random forest (RF) with the same 130 treatment
plans. The models were applied to 87 out-of-sample patients to create two sets
of predicted dose distributions that were used as input to two optimization
models. The first optimization model, inverse planning (IP), estimates weights
for dose-objectives from a predicted dose distribution and generates new plans
using conventional inverse planning. The second optimization model, dose
mimicking (DM), minimizes the sum of one-sided quadratic penalties between the
predictions and the generated plans using several dose-objectives. Altogether,
four KBP pipelines (GAN-IP, GAN-DM, RF-IP, and RF-DM) were constructed and
benchmarked against the corresponding clinical plans using clinical criteria;
the error of both prediction methods was also evaluated. The best performing
plans were GAN-IP plans, which satisfied the same criteria as their
corresponding clinical plans (78%) more often than any other KBP pipeline.
However, GAN did not necessarily provide the best prediction for the
second-stage optimization models. Specifically, both the RF-IP and RF-DM plans
satisfied all clinical criteria 25% and 15% more often than GAN-DM plans (the
worst performing planning), respectively. GAN predictions also had a higher
mean absolute error (3.9 Gy) than those from RF (3.6 Gy). We find that
state-of-the-art prediction methods when paired with different optimization
algorithms, produce treatment plans with considerable variation in quality.
