Automated treatment planning of postmastectomy radiotherapy

Abstract

Purpose Breast cancer is the most common cancer in women globally and radiation therapy is a cornerstone of its treatment. However, there is an enormous shortage of radiotherapy staff, especially in low‐ and middle‐income countries. This shortage could be ameliorated through increased automation in the radiation treatment planning process, which may reduce the workload on radiotherapy staff and improve efficiency in preparing radiotherapy treatments for patients. To this end, we sought to create an automated treatment planning tool for postmastectomy radiotherapy (PMRT). Methods Algorithms to automate every step of PMRT planning were developed and integrated into a commercial treatment planning system. The only required inputs for automated PMRT planning are a planning computed tomography scan, a plan directive, and selection of the inferior border of the tangential fields. With no other human input, the planning tool automatically creates a treatment plan and presents it for review. The major automated steps are (a) segmentation of relevant structures (targets, normal tissues, and other planning structures), (b) setup of the beams (tangential fields matched with a supraclavicular field), and (c) optimization of the dose distribution by using a mix of high‐ and low‐energy photon beams and field‐in‐field modulation for the tangential fields. This automated PMRT planning tool was tested with ten computed tomography scans of patients with breast cancer who had received irradiation of the left chest wall. These plans were assessed quantitatively using their dose distributions and were reviewed by two physicians who rated them on a three‐tiered scale: use as is, minor changes, or major changes. The accuracy of the automated segmentation of the heart and ipsilateral lung was also assessed. Finally, a plan quality verification tool was tested to alert the user to any possible deviations in the quality of the automatically created treatment plans. Results The automatically created PMRT plans met the acceptable dose objectives, including target coverage, maximum plan dose, and dose to organs at risk, for all but one patient for whom the heart objectives were exceeded. Physicians accepted 50% of the treatment plans as is and required only minor changes for the remaining 50%, which included the one patient whose plan had a high heart dose. Furthermore, the automatically segmented contours of the heart and ipsilateral lung agreed well with manually edited contours. Finally, the automated plan quality verification tool detected 92% of the changes requested by physicians in this review. Conclusions We developed a new tool for automatically planning PMRT for breast cancer, including irradiation of the chest wall and ipsilateral lymph nodes (supraclavicular and level III axillary). In this initial testing, we found that the plans created by this tool are clinically viable, and the tool can alert the user to possible deviations in plan quality. The next step is to subject this tool to prospective testing, in which automatically p...

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“…For example, the Radiation Planning Assistant (RPA, https:// rpa.mdanderson.org) is being developed at The University of Texas MD Anderson Cancer Center to fully automate the radiation treatment planning process with no or minimal user interventions. [45][46][47][48] For such a system to be effective in reducing the workload at busy clinics with limited resources, including radiation oncologists, high-quality automatic contouring is essential. 23 The present study showed that deep learning can achieve this, and we are integrating these tools to autosegment normal tissue and target volumes into the RAP system.…”

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“…Furthermore, in patients who have undergone breast-conserving surgery, the surgery is usually followed by radiotherapy to eradicate local subclinical residual disease. Patients who have had a total mastectomy may also be offered radiotherapy for reasons such as positive postmastectomy margins, primary tumor greater than five centimeters, the involvement of four or more lymph nodes [17].…”

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“…Each iteration proceeds in a trial‐and‐error fashion, with the planner editing the fluence, calculating 3D dose, and evaluating the dose until “optimal” dosimetry is achieved. There are several published studies on automated treatment planning algorithms being implemented for various anatomical sites, including breast 8–16 . Many institutions use in‐house developed software for plan automation, and while that is a feasible solution for those that already have it or have the resources to develop, maintain,and validate in‐house solutions, such an approach may not be accessible for many nonacademic clinics.…”