Session Item

PlanIQ^TM (Sun Nuclear) software provides a tool that uses target and organ at risk (OAR) geometry to indicate the difficulty of achieving different doses for organ dose-volume histograms. We investigate whether this tool can be used as a priori estimation of the complexity of the plan. Is it possible to fulfill both goals for covering the target while sparing the OAR or is it necessary to compromise? This would be helpful upfront for the planner and oncologist to decide what to prioritize.

108 breast cancer patients treated with postoperative radiotherapy at Odense University Hospital during 2020 were planned in Pinnacle³ (version 16.2.1) and PlanIQ (version 2.2). The cohort of patients included both patients with lumpectomy, mastectomy, and with and without lymph node involvement. A plan was made in Pinnacle where all goals for the target (using DBCG consensus guidelines) were fulfilled while sparing the OAR as much as possible. Plan setup was a tangential field-in-field method. All plans were sent to PlanIQ including the target and the OAR goals and PlanIQ estimated the feasibility of fulfilling the OAR criteria while still fulfilling the target criteria. 54 of the patients were randomly selected and used as training set in order to fit a linear model relating PlanIQ doses (“predicted”) to Pinnacle doses (“actual”). The remaining 54 patients were used as a test set for validation of the fitted models.

The linear model fitted on the training data was:

μ_HeartDose=1.270‧μ_PlanIQ+0.577  and  μ_LungDose=2.764‧μ_PlanIQ+0.166

μ_PlanIQ being the predicted dose from PlanIQ and  μ_HeartDoseand  μ_LungDose the mean heart and lung dose from Pinnacle dose plans for optimal target coverage. The result of applying a linear model to the training set is shown in Figure 1. a) and b). These plots show a scatterplot of the actual dose versus the predicted dose for the mean heart dose and mean ipsilateral lung dose respectively. Plot c) and d) shows Bland-Altman plots for the same OAR. A good correlation was observed with R-squared values of 0.93 and 0.96 for heart and ipsilateral lung respectively. 

Figure 1: a) and b) shows scatterplot of the actual dose versus the predicted dose for the mean heart dose and mean ipsilateral lung dose. c) and d) shows Bland-Altman plots for the same OAR, where the dashed lines indicate the limits of agreements (95% confidence interval).

PlanIQ shows to be useful to use as a quick prediction of doses to heart and ipsilateral lung in breast cancer radiotherapy. This could allow for early clinical decision-making on potential target compromises to keep doses to organs at risk below specific levels. In addition, an early detection of patients that could be candidates to proton therapy due to high dose to heart or lung would be possible with PlanIQ.