Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations

Abstract

Radiotherapy treatment planning of complex radiotherapy techniques, such as intensity modulated radiotherapy and volumetric modulated arc therapy, is a resource-intensive process requiring a high level of treatment planner intervention to ensure high plan quality. This can lead to variability in the quality of treatment plans and the efficiency in which plans are produced, depending on the skills and experience of the operator and available planning time. Within the last few years, there has been significant progress in the research and development of intensity modulated radiotherapy treatment planning approaches with automation support, with most commercial manufacturers now offering some form of solution. There is a rapidly growing number of research articles published in the scientific literature on the topic. This paper critically reviews the body of publications up to April 2018. The review describes the different types of automation algorithms, including the advantages and current limitations. Also included is a discussion on the potential issues with routine clinical implementation of such software, and highlights areas for future research.

Figure 1.

A typical manual IMRT treatment planning pathway. The example shown is for a prostate + seminal vesicle case. The steps are as follows: (1) CT scan with PTVs and OARs delineated; here the colours of ROIs are red: Prostate PTV, dark blue: SV PTV, yellow: bladder OAR, brown: rectum OAR. (2) create a range of “helper” (ROI) to aid the optimiser; e.g. the part of an OAR not overlapping with the PTV, PTVs overlapping with each other, ring structures to control dose spillage. In the example in Step 2, the ROIs shown are yellow: bladder cropped from prostate PTV, green: rectum cropped from seminal vesicle PTV, magenta: SV PTV cropped from prostate PTV, blue: prostate PTV unedited as it is the higher dose prescription than SV PTV. Step (3) set-up beam geometry. (4) Define the initial optimisation objectives either from scratch or from a class solution. (5) Run the inverse optimiser until it converges to a solution, calculate dose distribution. (6) Evaluate the resulting plan, if it is clinically acceptable proceed to Step 8, otherwise go to Step 7 to adjust the optimisation objectives. The part shaded in green (steps 5, 6, 7) is the iterative process of optimisation required by the planner to arrive at a clinically acceptable treatment plan to be approved by the clinician in Step 8. After this step, the plan will go through the quality control process and preparation for treatment, not shown on the flow chart. IMRT, intensity modulated radiotherapy; OAR, organ at risk; ROIs, regions of interest; PTV, planning target volume; SV, seminal vesicle.

Figure 2.

Trend showing the number of peer-reviewed publications on innovations in automated planning software per year, and the cumulative number of publications. The graph shows a significant increase from 2011.

Figure 3.

An example of DVH prediction KBP in a 3-dose level localised prostate cancer case. The shaded lines are the predicted range of achievable DVHs for the different OARs. The solid lines are the actual achieved DVH in the plan. This example is from Varian RapidPlan and the dashed lines and arrows are the optimisation objectives that have been generated by RapidPlan. Courtesy: Royal Surrey County Hospital NHS Foundation Trust, Guildford, UK. DVH, dose-volume histogram; KBP, knowledge-based planning; OAR, organ at risk.

Figure 4.

Schematic diagram of two competing criteria. The graph shows a large number of different feasible planning solutions, representing a variety of different permutations for criterion 1 and 2. The solid line represents the pareto front where improving one criterion inevitably leads to the worsening of the other and vice versa. Plans that lie on this front are the “pareto optimal solutions”, shown as blue circles in the schematic. The plans shown as diamonds are referred to as “dominated” because there is always a solution on the pareto front where at least one criterion can be improved.