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BackgroundSurgery and radiotherapy are current therapeutic options for malignant tumors involving the nasal vestibule. Depending on the location, organ-preserving resection is not always possible, even for small tumors. Definitive radiotherapy is an alternative as an organ-preserving procedure. Carbon ion beam radiotherapy offers highly conformal dose distributions and more complex biological radiation effects eventually resulting in optimized normal tissue sparing and improved outcome. The aim of the current study was to analyze toxicity, local control (LC), and organ preserving survival (OPS) after irradiation of carcinoma of the nasal vestibule with raster-scanned carbon ion radiotherapy boost (CIRT-B) combined with volumetric intensity modulated arc therapy (VMAT) with photons.MethodsBetween 12/2015 and 05/2021, 21 patients with malignant tumors involving the nasal vestibule were irradiated with CIRT-B combined with VMAT and retrospectively analyzed. Diagnosis was based on histologic findings. A total of 17 patients had squamous cell carcinoma (SCC) and 4 had other histologies. In this series, 10%, 67%, and 24% of patients had Wang stages 1, 2, and 3 tumors, respectively. Three patients had pathologic cervical nodes on MRI. The median CIRT-B dose was 24 Gy(RBE), while the median VMAT dose was 50 Gy. All patients with pathologic cervical nodes received simultaneously integrated boost with photons (SIB) up to a median dose of 62.5 Gy to the pathological lymph nodes. Eight patients received cisplatin chemotherapy. All patients received regular follow-up imaging after irradiation. Kaplan–Meier estimation was used for statistical assessment.ResultsThe median follow-up after irradiation was 18.9 months. There were no common toxicity criteria grade 5 or 4 adverse events. A total of 20 patients showed grade 3 adverse events mainly on skin and mucosa. All patients were alive at the end of follow-up. The median OPS after treatment was 56.5 months. The 6- and 24-month OPS were 100% and 83.3%, respectively. All local recurrences occurred within 12 months after radiotherapy. The median progression free survival (PFS) after treatment was 52.4 months. The 6-, 12-, and 24-month PFS rates were 95%, 83.6%, and 74.3%, respectively.ConclusionCIRT-B combined with VMAT in malignant tumors of the nasal vestibule is safe and feasible, results in high local control rates, and thus is a good option as organ-preserving therapy. No radiation-associated grade 4 or 5 acute or late AE was documented.

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.

PurposeThe purpose of this study is to investigate whether machine learning with dosiomic, radiomic, and demographic features allows for xerostomia risk assessment more precise than normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands.Material and methodsA cohort of 153 head-and-neck cancer patients was used to model xerostomia at 0–6 months (early), 6–15 months (late), 15–24 months (long-term), and at any time (a longitudinal model) after radiotherapy. Predictive power of the features was evaluated by the area under the receiver operating characteristic curve (AUC) of univariate logistic regression models. The multivariate NTCP models were tuned and tested with single and nested cross-validation, respectively. We compared predictive performance of seven classification algorithms, six feature selection methods, and ten data cleaning/class balancing techniques using the Friedman test and the Nemenyi post hoc analysis.ResultsNTCP models based on the parotid mean dose failed to predict xerostomia (AUCs 0.85), dose gradients in the right–left (AUCs > 0.78), and the anterior–posterior (AUCs > 0.72) direction. Multivariate models of long-term xerostomia were typically based on the parotid volume, the parotid eccentricity, and the dose–volume histogram (DVH) spread with the generalization AUCs ranging from 0.74 to 0.88. On average, support vector machines and extra-trees were the top performing classifiers, whereas the algorithms based on logistic regression were the best choice for feature selection. We found no advantage in using data cleaning or class balancing methods.ConclusionWe demonstrated that incorporation of organ- and dose-shape descriptors is beneficial for xerostomia prediction in highly conformal radiotherapy treatments. Due to strong reliance on patient-specific, dose-independent factors, our results underscore the need for development of personalized data-driven risk profiles for NTCP models of xerostomia. The facilitated machine learning pipeline is described in detail and can serve as a valuable reference for future work in radiomic and dosiomic NTCP modeling.