Updating Photon-based Normal Tissue Complication Probability Models for Pneumonitis in Lung Cancer Patients Treated with Proton Beam Therapy

Lung Cancer
17/05/2020

Jain V, et al. Pract Radiat Oncol 2020.

ABSTRACT

PURPOSE: No validated models for predicting the risk of radiation pneumonitis (RP) with proton beam therapy (PBT) currently exist. Our goal was to externally validate and recalibrate multiple established photon-based normal tissue complication probability (NTCP) models for RP in a cohort with locally advanced non-small cell lung cancer (LA-NSCLC) treated with contemporary doses of chemoradiation using PBT.

METHODS: The external validation cohort consisted of 99 consecutive patients with LA-NSCLC treated with chemoradiation using PBT. RP was retrospectively scored at 3 and 6 months post-treatment. We evaluated the performance of the photon QUANTEC pneumonitis (QP) model, QUANTEC model adjusted for clinical risk factors (AQP), as well as the newer Netherlands updated QUANTEC model (NQP). A closed testing procedure was performed to test the need for model updating, either by recalibration-in-the-large (re-estimation of intercept), recalibration (re-estimation of intercept/slope) or model revision (re-estimation of all coefficients).

RESULTS: There were 21 events (21%) of ≥ Grade 2 RP. The closed testing procedure on the PBT data set did not detect major deviations between the models and the data and recommended adjustment of the intercept only for the photon-based NQP model (intercept update: -1.2). However, an update of the slope, or revision of the model coefficients, were not recommended by the closed testing procedure, as the deviations were not significant within the power of the data.

CONCLUSIONS: The similarity between dose response relationship for PBT and photons for normal tissue complications has been an assumption until now. We demonstrate that the pre-existing widely used photon based models fit our PBT data well with minor modifications. These now validated and updated NTCP models can aid in individualizing selection of the most optimal treatment technique for a particular patient.