Radiotherapy treatment planning

Elekta’s Leksell Gamma Knife® (stereotactic radiosurgery)

The introduction of Elekta’s Leksell Gamma Knife® PERFEXION™ has taken the field of stereotactic radiosurgery to a new level. Unlimited access to cranial volume and full automation approach open a new research area for the researchers who have been already working on the previous Lekshell Gamma Knife unit for years. Although this method is primarily designed to be performed on brain tumors, today, it is considered an effective treatment for several other conditions, including arteriovenous malformations and pituitary tumors.

The new capabilities of the PERFEXION™ unit require new methods of treatment plan design. In previous Gamma Knife® designs, the collimator (the component that adjusts the shape/location of the radiation delivery) had to be manually adjusted, rendering complex treatments too labor intensive for clinical viability. Because the movement of the collimator in the new PERFEXION™ unit is now automated, complex treatment plans that can very tightly conform to the targeted treatment area can be delivered in clinical settings.

In order to deliver a high quality treatment, we select collimator positions based on optimization methods. The optimization algorithms are designed to deliver an appropriate amount of dose to the target area while simultaneously avoiding sensitive healthy tissues, thereby leaving the patient with a high quality of life and fewer side effects after treatment.

Total body irradiation (TBI)

Diseases requiring bone marrow transplants, also called stem cell transplants, include leukemia, lymphoma, sickle cell disease and aplastic anemia, as well as some immunodeficiencies. As part of the conditioning process to prepare the patient for the bone marrow transplant, the patient may be treated with total body
irradiation (TBI). The purpose of TBI is to eliminate the underlying disease and to suppress the recipient’s immune systems, thus preventing rejection of new donor stem cells. Once the conditioning treatment is complete, the patient receives the bone marrow transplant to restore healthy bone marrow function.

The goal for this research program is to design treatments that will not irradiate the whole body, but instead will only focus on the bone marrow. Such a treatments are no longer total body irradiation, but total marrow irradiation (TMI).

TMI treatment plans will be developed using a mathematical model that will provide for treatments resulting in the desired eradication of existing bone marrow cells while simultaneously avoiding organs and healthy tissues that do not require irradiation. This will be achieved by using intensity modulated radiation therapy (IMRT), a type of radiation therapy capable of delivering any distribution of radiation intensity from each beam. The resulting treatments will be better able to eradicate the patient’s existing bone marrow with potentially fewer side effects from radiation overdose, thereby improving the patient’s quality of life and preparation for a bone marrow transplant.

Automated quality assurance

The process of validating a radiotherapy treatment plan requires a quality assurance review by an expert. If the plan is deemed acceptable, it proceeds to treatment. Otherwise, it is returned to a dosimetrist to revise the treatment to meet standards for the particular clinic. This review is time-consuming, taking as much as tens of thousands of man-hours annually at large treatment centers, and may be subject to human errors.

The goal of this research is to automate the QA process using machine learning to learn clinic treatment standards and flag treatments that do not meet those standards. There can be little tolerance for incorrectly identifying an erroneous plan, as there may significant health implications for the patient. Notably, a plan that does not conform to standards may not be erroneous; it may be part of trial of new treatments or the patient may require unusual treatment. Thus, any flagged plan must be manually reviewed.

The challenge in correctly classifying plans as acceptable or not is twofold. First, radiotherapy QA datasets are extremely high-dimensional due to the number of features required to represent dose distributions. Second, the datasets are highly imbalanced in that most stored plans are acceptable, as unacceptable plans are generally overwritten with improved plans. We develop classification and anomaly detection tools to identify unacceptable plans with a low false negative rate, and interpretation methods to communicate to dosimetrists how to improve unacceptable plans.