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Radiation therapy is one of
the most well used applications

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of optimization in health care.

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In practice,
radiation machines are

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connected to treatment
planning software that

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implements and solves
optimization models.

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Both linear optimization
models, like we

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saw here, and other types
of optimization models.

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Some examples of
radiation machines

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that use optimization
software are

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Pinnacle by Philips, RayStation
by RaySearch Labs and Eclipse

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by Varian.

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There are many extensions
to the optimization model

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we described here.

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One is the selection
of beam angles.

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The beam angles can be selected
jointly with the intensity

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profiles by using an
integer optimization.

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This allows the treatment
to be further personalized

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to the specific tumor and
person receiving the radiation.

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Integer optimization is another
type of optimization model

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and will be the
topic of next week.

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Another extension is
accounting for uncertainty.

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Often the quality
of IMRT treatments

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is degraded due to
uncertain organ motion.

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For example, in lung
cancer, the target area

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is around the lungs.

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But since the
patient is breathing

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throughout the treatment,
the target area is moving

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and the location is
slightly uncertain.

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We can manage this
uncertainty using

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a method known as
robust optimization.

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There is a significant amount
of literature on this method

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and its application
to radiation therapy.

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Linear optimization
significantly

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improves the efficiency
of radiation therapy.

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Manually designing
an IMRT treatment

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would be inefficient
and impractical.

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By using linear
optimization, IMRT

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can be designed in an
efficient and systematic way.

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As we saw in this
lecture, clinical criteria

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can be modeled using constraints
and the treatment planner

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can explore trade-offs
by changing the model.

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But ultimately, IMRT and
the use of optimization

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benefits the patient.

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In head and neck
cancers saliva glands

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were rarely spared
prior to IMRT,

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but by using optimized
IMRT treatments,

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saliva glands can now be spared.

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In prostate cancer,
IMRT treatments

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reduced toxicities and
allow for higher tumor doses

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to be delivered safely.

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And in lung cancer
optimized IMRT

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reduced the risk of radiation
induced pneumonitis.

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These are just three examples
of the benefits of IMRT.

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Overall, optimized
IMRT allows clinicians

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to deliver treatment that
was previously impossible

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and to improve the quality
of life for patients.