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algorithm using a 1 mm dose calculation grid size. X-ray energy used was 6 flattening filter free (FFF) and
dose rate for planning was 1400 MU/min. Contouring of the gross tumor volume (GTV) and the critical
structures such as the brain, brainstem, chiasm, optic nerves and tracts, eyes and lenses closely followed
previously published guidelines . At our institution, no margin is used to convert GTV to planning target
[7,8]
volume (PTV). The range of the PTV was from 0.1-7 cm3. The gradient index (GI) was defined as the ratio
of volume covered by the 50% isodose line to that covered by the 100% isodose . The conformity index
[16]
(CI) and the homogeneity index (HI) were chosen for plan evaluation. The CI was defined as the ratio of the
volume covered by the 100% isodose to the volume covered by the PTV. The HI was taken as the maximum
PTV dose to the prescription dose. Dose prescribed in a single fraction was 20 Gy, 18 Gy or 16 Gy and was
decided based on the size and volume of the lesion and its proximity to critical organs. The constraints
and strategy for optimization was similar to that previously published [7,8,18,19] . The dose constraints used for
planning are shown in Table 1.

KBP with RapidPlan
RapidPlan is a treatment planning application developed by Varian medicalsystems that utilizes a knowledge-based
approach. Previously accepted clinical plans are taken from which data are extracted which include the
volumes of the OARs and PTVs, percentage of the overlap volume for each OAR with the target, percentage
of the OAR volume that is out of the field for each OAR, prescription dose, structure dose-volume histogram
(DVH) and geometry based expected DVH for each OAR. The geometry based expected dose is a measure
of dose received by a portion of an OAR if only the patient anatomy and desired target dose are to be
considered. Principle component analysisis conducted on this extracted data and the principle components
are used to build a DVH estimation model . When a treatment plan is to be generated for a new patient,
[17]
the RapidPlan model will create DVH estimates for the OARs based on the anatomy for that particular case.
These DVH estimates will then be used as part of the objectives for optimization to achieve the dosimetric
goals for that patient.

Model training
To create the RapidPlan DVH estimation model, we selected 66 patients with 125 lesions (range 1-4, median 1).
The model was trained for multiple target dose levels, namely high risk, intermediate risk and a low risk PTV.
These levels of risk for the PTVs were matched accordingly in both the training and validation. For all single
lesion cases, the plan had only 1 target, which was matched to high dose level. If the plan had 2 targets that
were prescribed to the same dose level, then they both were matched to the highest dose level. However, if
they went to different dose levels, the target receiving higher prescription was matched to the higher dose
level and the one receiving the next dose level was matched to the intermediate dose. Similarly if a plan
had 3 targets, each prescribed to different dose levels, the targets were matched correspondingly, i.e., high
to high, intermediate to intermediate and low to low. However if they went to 2 different dose levels, the
target(s) with the highest prescription dose would be matched to high and the target(s) with the next dose
level would be matched to intermediate dose level. Similarly if all the 3 went to the same dose level, all
of them would be matched to high dose. This methodology of matching is recommended by the training
software. The OARs included for training were brain, brainstem, chiasm, optic nerves, eyes and lenses. The
training process consisted of identifying the geometric outliers and the dosimetric outliers. The geometric
outliers are typically cases were the PTV and/or OAR volumes, shapes and overlaps differ substantially from
the majority of the training set. Dosimetric outliers are cases where the clinical DVH differs substantially
from the estimated DVH. Geometric outliers can be identified using regression plots, which illustrate the
correlation between the best prediction of the DVH and the most likely geometric parameter that would be
responsible for that DVH such as the volume of the structure or the overlap with the target or a combination
of both. Geometric outliers are points that are typically identified as points that fall far away from the
regression line or that are substantially isolated from it. Similarly, dosimetric outliers can be identified using
something called residual plots that correlate the best estimation of the DVH to the actual clinical DVH for