Image-based Data Mining to Probe Dosimetric Correlates of Radiation-induced Trismus

Purpose: To identify imaged regions in which dose is associated with radiation-induced trismus after head and neck cancer radiation therapy (HNRT) using a novel image-based data mining (IBDM) framework. Methods and Materials: A cohort of 86 HNRT patients were analyzed for region identification. Trismus was characterized as a continuous variable by the maximum incisor-to-incisor opening distance (MID) at 6 months after radiation therapy. Patient anatomies and dose distributions were spatially normalized to a common frame of reference using deformable image registration. IBDM was used to identify clusters of voxels associated with MID (P ≤.05 based on permutation testing). The result was externally tested on a cohort of 35 patients with head and neck cancer. Internally, we also performed a dose-volume histogram–based analysis by comparing the magnitude of the correlation between MID and the mean dose for the IBDM-identified cluster in comparison with 5 delineated masticatory structures. Results: A single cluster was identified with the IBDM approach (P s = −0.36, –0.35, −0.32; P =.001,.001,.002, respectively). External validation confirmed an association between mean dose to the IBDM cluster and MID (R s = −0.45; P =.007). Conclusions: IBDM bypasses the common assumption that dose patterns within structures are unimportant. Our novel IBDM approach for continuous outcome variables successfully identified a cluster of voxels that are highly associated with trismus, overlapping partially with the ipsilateral masseter. Tests on an external validation cohort showed an even stronger correlation with trismus. These results support use of the region in HNRT treatment planning to potentially reduce trismus.