Quality assurance incorporating artificial intelligence-generated reference contours in a phase II radiotherapy trial

Abstract

Background and purposeA dummy run quality assurance was conducted for a prospective phase II trial of tailored radiotherapy (RT) according to the response after neoadjuvant chemotherapy (NAC) followed by surgery in locally advanced breast cancer (RTaNAC). Since inter-institutional variations in dose distributions can impact clinical outcomes, this dummy run aimed to develop an RT plan protocol to minimize these variations.Material and methodsThis study involved computed tomography images from three clinical scenarios: RT with no lymph node (LN) boost (Scenario 1), LN boost up to 60 Gy3.5, equivalent dose in 2 Gy fractions (EQD2) with α/β = 3.5 Gy (Scenario 2), and LN boost up to 66 Gy3.5 (Scenario 3). Seven institutions developed RT plans under a two-step process: first according to each institution’s policies (Step 1) and then using additional reference information, including artificial intelligence (AI) auto-contoured structures and LN boost target volume information (Step 2). Dose-volume histograms for breast and regional nodal areas were analyzed between the two steps.ResultsInter-institutional variation observed in Step 1 improved in Step 2 for breast and regional nodal areas. Specifically for Scenario 1, dose coverage for the nodal clinical target volumes of the axillary level I LN improved from 57.5% to 97.5% (p-value = 0.075), and that of the supraclavicular LN improved from 75.6% to 88.8% (p-value = 0.046).ConclusionVariations in dose/volume metrics among institutions were mitigated by AI auto-contoured structures and LN boost target volume information. Through this dummy run, the participating institutions reached a consensus on an RT plan protocol to support multi-institutional expansion of RTaNAC.