Purpose: To explore the parotid normal tissue complication probability (NTCP) modeling with percolation-based dose clusters for head and neck (HN) cancer patients receiving concomitant chemotherapy and radiation therapy.
Methods: Seventy-five HN patients treated with intensity modulated radiation therapy (IMRT) and chemotherapy were retrospectively included in the study. Cluster models incorporating the spatial dose distribution in the parotid gland were developed to evaluate the radiation induced complication. Cluster metrics including the mean cluster size (NMCS) and the largest cluster size both normalized by the gland volume (NSLC) were evaluated and scrutinized against the benchmark NTCP. Two fitting strategies to the Lyman-Kutcher-Burman (LKB) model using the maximum likelihood method were devised: the volume parameter n fixed at 1.0 (mean dose model) and unrestricted (full LKB model). The fitted parameters TD50 (uniform dose leading to 50% complication probability) and m (steepness of the curve) were assessed with the LKB NTCP models with the available xerostomia data. Statistical analyses including confidence intervals and bootstrapping were performed to evaluate the statistical significance of the results.
Results: NSLC was found to be a better metric with reference to the LKB model and strong correlation (r~0.95) was observed between NTCP and NSLC. The mean dose model returned the parameter TD50 (39.9Gy) and m (0.4) from the NSLC distribution. The threshold dose determining the clusters was around 40Gy. Drastically different TD50 and m values were obtained from the fittings via the full LKB model, where the threshold dose would be near 27Gy if parotid was deemed as a parallel organ. Bootstrapping analyses further confirmed the fitting outcomes.
Conclusion: Cluster models could serve as useful predictors for parotid radiotherapy induced complication. Parameterization of the clustering patterns showed different predictions from current clinical recommendations. Further investigation is needed to validate the cluster models as useful clinical decision support tools.