Integrating Data Envelopment Analysis into radiotherapy treatment planning for head and neck cancer patients

Radiotherapy treatment (RT) irradiates a patient's tumour volume while minimising damage to healthy tissue and surrounding critical organs at risk (OAR). In the conventional RT planning process, the RT planner has to iteratively adjust either the planning objectives (tumour or OAR dose levels) or the weights of the planning objectives until an acceptable plan is obtained that satisfies the minimum requirements. At the end of this iterative process, it remains unknown whether this plan is the best that can be obtained for the patient. The oncologist reviews each plan and decides to either treat using this plan or request further plan development, which may or may not lead to an actual improvement of the reviewed plan. We describe how Data Envelopment Analysis (DEA) is used as a real-time decision support tool to assess quality of RT plans for head and neck cancer patients by applying a knowledge-based comparison of each new plan to a library of previous clinically approved plans. This library allows benchmarking, which gives planners and oncologists a better idea of the relative quality of their plan and its improvement potential, resulting in improved use of resources and better quality treatments for patients. Our DEA-based approach provides a novel way of capturing multiple measures of plan quality as well as anatomical differences between patients in the benchmarking process. We present the developed DEA model and results for a set of benchmark instances. Initial results of integrating DEA-based quality feedback into the RT planning process are presented showing that operations research can contribute significantly to planning quality in this setting. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study demonstrates the use of Data Envelopment Analysis as a real-time decision support tool to assess the quality of radiotherapy plans for head and neck cancer patients. By comparing new plans to a library of previous clinically approved plans, the method provides benchmarking to evaluate the relative quality and improvement potential of each plan. The integration of DEA-based quality feedback into the RT planning process shows significant contributions to planning quality.