These predicted high-risk CTVs provided close agreement to the ground-truth compared with current interobserver variability. The predicted contours could be implemented clinically, with only minor or no changes.

Radiomics is one such “big data” approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between “favorable” and “unfavorable” clusters were noted.

The findings from this prospective longitudinal registry validate prior observations that dose to submental musculature predicts for increased burden of dysphagia after oropharyngeal IMRT. Findings also support dichotomization of DIGEST grade ≥2 as a dose-dependent split for use as an endpoint in trials or predictive dose-response analysis of videofluoroscopy results.

Highlights MRI-CT deformable image registration was not superior to rigid registration. Dice similarity coefficients were 0.65, 0.62, and 0.63 for deformable registrations. Dice similarity coefficient was 0.63 for rigid registration. Registration quality was superior in muscle and gland compared to bone and vessel.

Signal intensity kinetics of radiation injury can be broadly correlated with the functional muscular defect. Serial MRI during the course of RT may provide an opportunity to quantitatively track muscular pathology for subclinical detection of patients at high risk to develop dysphagia.

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The single item MDASI-HN-DM correlated with the multi-item XQ and performed favorably in the prediction of QOL. A MDASI-HN-DM cut point of ≥6 correlated with decline in QOL.

Background: Central neurocytoma (CN) is a rare tumor accounting for <0.5% of all intracranial tumors. Surgery ± radiotherapy is the mainstay treatment. This international multicentric study aims to evaluate the outcomes of CNs patients after multimodal therapies and identify predictive factors. Patients and methods: We retrospectively identified 33 patients with CN treated between 2005 and 2019. Treatment characteristics and outcomes were assessed. Results: All patients with CN underwent surgical resection. Radiotherapy was delivered in 19 patients. The median radiation dose was 54 Gy (range, 50–60 Gy). The median follow-up time was 56 months. The 5-year OS and 5-year PFS were 90% and 76%, respectively. Patients who received radiotherapy had a significantly longer PFS than patients without RT (p = 0.004) and a trend towards longer OS. In addition, complete response after treatments was associated with longer PFS (p = 0.07). Conclusions: Using RT seems to be associated with longer survival rates with an acceptable toxicity profile.