Automated Generation of Radiotherapy Treatment Plans Using Machine Learning Methods
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With the development of medical linear accelerator technologies, the precision and complexity of external beam radiation therapy have increased tremendously over the years. The goal of radiation therapy has always been to push the limit to irradiate the target volume while preserving normal tissues. To achieve this goal, treatment planning for radiation therapy has become a labor-intensive and time-consuming task, which requires a high level of experience and knowledge from the planner. Therefore, automated treatment planning, or auto-planning, is of particular interest in radiation therapy research. The advantages of auto-planning are reduced planning time and increased plan quality consistency.Since the treatment planning workflow has multiple steps, auto-planning includes the automation of different planning procedures, such as contouring, beam placement, and inverse optimization, which can be achieved in different approaches. The main approaches are knowledge-based planning, automated rule implementation and reasoning, and multicriteria optimization. We can generally consider such novel auto-planning applications as artificial intelligence (AI). This study primarily focuses on treatment plan generation using knowledge-based planning and machine learning techniques. The study includes two main projects: automated beam setting for whole breast radiation therapy (WBRT) and fluence map prediction for intensity modulated radiation therapy (IMRT). In WBRT planning, tangential beams are used to irradiate the entire breast volume and avoid the organs-at-risk (OARs) (i.e., the lungs and the heart) as much as possible. The placement of the beams is vital in determining the planning target volume (PTV) coverage and normal tissue sparing. Furthermore, planners need to take multiple clinical considerations into account, e.g., avoiding the contralateral breast and the heart, and use a variety of techniques to meet the demands. Therefore, we developed an automated beam setting program which takes simple user settings and optimizes target coverage and OAR sparing. The program can be launched from the Eclipse Treatment Planning System (TPS) as a binary plug-in script, which generates a graphical user interface to accept user inputs. Several beam geometries are supported: tangential beams only (supine), tangential plus supraclavicular (SCV) beams (supine), and prone beams. For all geometries, the program calculates the optimal gantry angles, collimator angles, isocenter location, jaw sizes, and MLC shapes. The borders of the SCV beams are also matched to the tangential beams by using couch kicks on the main tangential fields. For the supine geometries, a coefficient was learned from existing clinical plans to balance between the PTV and lung coverages. The program searches from an initial setting based on breast wires and finds the optimal setting. For the prone geometry, the planner can set a margin to customize the coverage near the PTV-lung interface. The program has been implemented together with a WBRT fluence prediction program, which creates electronic compensation (ECOMP) plans from the given beam settings. This automated workflow can significantly reduce the workload of the forward planned ECOMP plans. The results showed that the AI plans achieved similar or better plan quality compared to the manual plans. In IMRT planning, inverse optimization is the standard practice to create treatment plans. Dose-volume histogram (DVH) constraints and priorities are set by the planner to start the optimization and often continuously tuned throughout the planning process until the optimal dose distribution is achieved. The actual parameters to be optimized are fluence map intensities of the IMRT beams. Numerous efforts have been devoted in KBP to predict either the DVH or the dose of the optimal plan. The rationale is that, given the patient anatomy and the physician’s prescription, the DVH or dose in the final plan can be predicted based on similar previous plans. The predicted DVH or dose can then be used as a reference to either evaluate the plan quality or generate new plans by converting them into inverse optimization objectives, which is a process also known as dose mimicking. However, most dose mimicking techniques are still in the development stage and not yet commercially available. We explored the feasibility to directly predict optimal fluence maps and generate IMRT plans without inverse optimization. In order to achieve fluence map prediction, we first investigated the correlation between patient anatomy and fluence maps. A database of patient anatomy and fluence maps was built with pancreas SBRT cases. Treatment planning was done on 2D axial slices with in-house dose calculation and fluence optimization algorithms. For a new slice, an atlas matching method was developed to search for the most anatomically similar slice in the database and initialize the optimization with the existing fluence. The atlas-guided fluence optimization reduced the optimization cost and offered a small dosimetric improvement compared to uniform initialization. With more training data, deep learning methods were experimented to predict fluence maps from patient anatomy. A deep learning framework consisting of two convolutional neural networks (CNN) was developed. As each plan has several beams, all beam doses must add up to the optimal plan’s total dose, while each beam dose is deposited only by said beam’s fluence map. Therefore, the BD-CNN predicts the individual beam doses (BD) for an IMRT plan, which tries to minimize the prediction error for both the beam doses and the total dose. Once the beam doses are available, each fluence map (FM) is generated separately by the FM-CNN. As the fluence maps exist in the beam’s eye view (BEV), a projection of the 3D beam dose onto the 2D BEV is necessary. The resulting dose map is used as the input to the FM-CNN, which predicts the fluence map as the output. The predicted fluence maps are imported into the TPS for leaf sequencing and dose calculation, generating a deliverable plan. These projects are retrospective studies using anonymized patient data for training and testing. The development of the deep learning framework was split into several stages: the initial test of the feasibility was conducted for pancreas stereotactic body radiation therapy (SBRT) with a single PTV, unified dose constraints, and a fixed 9-beam geometry; the networks were then modified to allow variable dose inputs and multiple PTVs for pancreas SBRT with simultaneous integrated boost (SIB); a transfer learning technique was applied to the training of the framework for adrenal SBRT plans with different beam settings and dose constraints, using the pancreas model as the base model. The framework has evolved to be more robust and support different sites and planning styles over time. The AI plans with predicted fluence maps achieved similar plan quality as manual plans for most cases. For some cases with particularly challenging patient anatomies, the AI plans can struggle to reach the high standard of the expert plans. Fluence map prediction is a viable way to directly generate IMRT plans without inverse optimization. This application may be especially useful for adaptive treatment planning.
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