1. An optimization approach to automate radiotherapy treatment planning
Presenter: Qian Li
Today, I’m going to talk about the my internship at UCSF the past few months. I will talk about the problem we have and an overview how have I solved.

2. What’s Radiotherapy? It delivers targeted beams of radiation to kill cancer cells
At the radiation Oncology department where I’m interning at, we deliver radiotherapy treatment to patients with cancer. There are two common types of treatments for cancer. One is chemo, which delivers drug to all body and the other is radiotherapy which delivers radiation targeting on an area of body. As you can see from this machine, after it received a detailed dose distribution data, it will rotate around the human body and deliver specific dose of radiation. There are shields in the machine blocking some part of radiation so it will only go to specific places.

3. How to make a plan?
Then the planning process is very important. This is the software to optimize rotating angles and the shielding and dose of distribution. I will do most work for you. Planners whose job on a daily basis… The planning could take an hour or one day. Why it is time consuming it the software… the data, optimizer is a black box. We don’t know what kind of data we put there, it will generate a plan

4. An example of input and output on the planning software
Target
Constraints (Gy)
Output (Gy)
Good?
min
79 (at 95%) 78 No
max
84 (at 0.01%) 81
Yes
Can change dose and weight. explain

5. Dose limits for the organs
Except for target, we have a list of requirements on the organs. What a good combination of doses and weights? Need a strategic approach to this problem. So I’m writing an numerical optimization in Python on top of the medical software so it will automate the planning process.

6. Define loss function for the optimization
Target
Constraints (Gy)
Output (Gy)
Cost
min
79 at 95% 78 1
max
84 at 0.01% 80
Overall loss = weighted sum of individual costs

7. Overall Strategy while loop: a. keep the organ constraints constant
b. changing target constraints until costs of target are all zero
c. calculate overall loss
if overall loss is not lowered:
revert all changes to previous cycle and stop the while loop else:
increase weight of unmet organ constraints

8. One example of optimization strategy: Use finite difference to evaluate change
Values
Output
New values
New output
Ratio of Change 79 75 80 78
3/1 = 3
Then, use the ratio of change in previous step to adjust values in next step.

9. Next Step: Particle swarm optimization
Sample more points on constraints that have the problem