Background: Most dogs with sinonasal tumors (SNT) treated with radiation therapy (RT) died because of local disease progression.

Hypothesis/objectives: Our hypothesis is that the majority of local failure and residual disease would occur within the radiation field.

Animals: Twenty-two dogs with SNT treated with RT.

Methods: Retrospective cohort study.

Inclusion Criteria: dogs with SNT receiving 10 daily fractions of 4.2 Gy with intensity modulated radiation therapy (IMRT)/image guided radiation therapy (IGRT) and follow-up cone beam computed tomography (CBCT). Each CBCT was registered with the original radiation planning CT and the gross tumor volume (GTV) contoured. The GTV was classified as residual (GTVr) or a failure (GTVf). The dose statistic for each GTV was calculated with the original IMRT plan. For GTVf, failures were classified as "in-field," "marginal," or "out-field" if at least 95, 20-95, or less than 20% of the volume of failure was within 95% (D95) of the total prescription dose, respectively.

Results: There were 52 follow-up CBCT/CTs. Overall there was a GTVr for 20 dogs and GTVf for 16 dogs. The majority of GTVr volume was within the original GTV. GTVf analysis showed that 75% (12/16) were "in-field," 19% (3/16) were "marginal" and 6% (1/16) were "out-field."

Conclusion And Clinical Importance: In-field failures are the main pattern for local recurrence, and there is evidence of radioresistant subvolumes within the GTV.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995431PMC
http://dx.doi.org/10.1111/jvim.16076DOI Listing

Publication Analysis

Top Keywords

radiation therapy
16
residual disease
8
local failure
8
guided radiation
8
sinonasal tumors
8
snt treated
8
dogs snt
8
radiation
6
dogs
6
gtv
5

Similar Publications

Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma.

View Article and Find Full Text PDF

Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs.

View Article and Find Full Text PDF

Background: There is a lack of prognosticators of overall survival (OS) for Oral Squamous Cell Carcinoma (OSCC).

Objectives: We examined collaborative machine learning (cML) in estimating the OS of OSCC patients. The prognostic significance of the clinicopathological parameters was examined.

View Article and Find Full Text PDF

the evolution of axillary management in breast cancer has witnessed significant changes in recent decades, leading to an overall reduction in surgical interventions. There have been notable shifts in practice, aiming to minimize morbidity while maintaining oncologic outcomes and accurate staging for newly diagnosed breast cancer patients. These advancements have been facilitated by the improved efficacy of adjuvant therapies.

View Article and Find Full Text PDF

surgery for rectal cancer often presents multiple tactical and technical challenges due to factors such as the tumor's extent, limited anatomical space, proximity to the anal sphincter complex, and the use of neoadjuvant radiotherapy. These factors can significantly increase the complexity of surgery and the risk of both immediate and delayed complications, which can occur intraoperatively or postoperatively. Objective: the aim of this study was to retrospectively analyze the causes, diagnostic methods, and management of complications in patients who underwent surgery for rectal cancer.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!