Abstract
Purpose: Due to the sharp gradients of intensity-modulated radiotherapy (IMRT) dose distributions, treatment uncertainties may induce substantial deviations from the planned dose during irradiation. Here, we investigate if the planned mean dose to parotid glands in combination with the dose gradient and information about anatomical changes during the treatment improves xerostomia prediction in head and neck cancer patients.Materials and methods: Eighty eight patients were retrospectively analyzed. Three features of the contralateral parotid gland were studied in terms of their association with the outcome, i.e., grade ≥ 2 (G2) xerostomia between 6 months and 2 years after radiotherapy (RT): planned mean dose (MD), average lateral dose gradient (GRADX), and parotid gland migration toward medial (PGM). PGM was estimated using daily megavoltage computed tomography (MVCT) images. Three logistic regression models where analyzed: based on (1) MD only, (2) MD and GRADX, and (3) MD, GRADX, and PGM. Additionally, the cohort was stratified based on the median value of GRADX, and a univariate analysis was performed to study the association of the MD with the outcome for patients in low- and high-GRADX domains.Results: The planned MD failed to recognize G2 xerostomia patients (AUC = 0.57). By adding the information of GRADX (second model), the model performance increased to AUC = 0.72. The addition of PGM (third model) led to further improvement in the recognition of the outcome (AUC = 0.79). Remarkably, xerostomia patients in the low-GRADX domain were successfully identified (AUC = 0.88) by the MD alone.Conclusions: Our results indicate that GRADX and PGM, which together serve as a proxy of dosimetric changes, provide valuable information for xerostomia prediction.
Highlights
The advent of machine learning methods is changing normal tissue complication probability (NCTP) modeling in radiotherapy (RT)
Our results indicate that GRADX and parotid gland migration toward medial (PGM), which together serve as a proxy of dosimetric changes, provide valuable information for xerostomia prediction
We have investigated xerostomia prediction in the context of anatomical changes occurring during the course of radiation treatments
Summary
The advent of machine learning methods is changing normal tissue complication probability (NCTP) modeling in radiotherapy (RT). Image-guided radiotherapy (IGRT) provides more and better daily images of the treated anatomy, increasing the precision and accuracy of radiation delivery [7]. Due to the sharp dose gradients of IMRT dose distributions, even subtle anatomical changes may lead to differences between the planned and the delivered dose distributions—especially within the high gradient regions around the target edges. Current NTCP studies are still largely based on the planned dose [8,9,10,11,12] and not on the delivered dose causing the radiation effect. Patient-specific dose reconstruction involving daily imaging, dose calculation, deformable image registration, and dose accumulation remains very challenging [13, 14]
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