Background: Traditionally, radiation therapy plans are optimized without consideration of chemotherapy. Here, we model the risk of radiation pneumonitis (RP) in the presence of a possible interaction between chemotherapy and radiation dose distribution.
Material and methods: Three alternative treatment plans are compared in 18 non-small cell lung cancer patients previously treated with helical tomotherapy; the tomotherapy plan, an intensity modulated proton therapy plan (IMPT) and a three dimensional conformal radiotherapy (3D-CRT) plan. All plans are optimized without consideration of the chemotherapy effect. The effect of chemotherapy is modeled as an independent cell killing process using a uniform chemotherapy equivalent radiation dose (CERD) added to the entire organ at risk. We estimate the risk of grade 3 or higher RP (G3RP) using the critical volume model.
Results: The mean risk of clinical G3RP at zero CERD is 5% for tomotherapy (range: 1-18 %) and 14% for 3D-CRT (range 2-49%). When the CERD exceeds 9 Gy, however, the risk of RP with the tomotherapy plans become higher than the 3D-CRT plans. The IMPT plans are less toxic both at zero CERD (mean 2%, range 1-5%) and at CERD = 10 Gy (mean 7%, range 1-28%). Tomotherapy yields a lower risk of RP than 3D-CRT for 17/18 patients at zero CERD, but only for 7/18 patients at CERD = 10 Gy. IMPT gives the lowest risk of all plans for 17/18 patients at zero CERD and for all patients with CERD = 10 Gy.
Conclusions: The low dose bath from highly conformal photon techniques may become relevant for lung toxicity when radiation is combined with cytotoxic chemotherapy as shown here. Proton therapy allows highly conformal delivery while minimizing the low dose bath potentially interacting with chemotherapy. Thus, intensive drug-radiation combinations could be an interesting indication for selecting patients for proton therapy. It is likely that the IMRT plans would perform better if the CERD was accounted for during optimization, but more clinical data is required to facilitate evidence-based plan optimization in the multi-modality setting.