Intensity-modulated radiation therapy for head and neck cancer

Evaluating the impact of patient, tumor, and treatment characteristics on the development of jaw complications in patients treated for oral cancers: a SEER-Medicare analysis

BACKGROUND

Jaw complications, including osteoradionecrosis, are significant sequelae of radiation therapy (RT) for oral cancers. This study identifies the impact of patient, tumor, and treatment characteristics on the development of jaw complications in patients treated with RT.

METHODS

The Surveillance, Epidemiology, and End Results (SEER)-Medicare database was used to identify patients treated with RT for oral cancers from 1999 to 2007. Jaw complications were identified by International Classification of Diseases 9th revision (ICD-9) diagnosis codes and/or related procedures using Current Procedural Terminology (CPT) and ICD-9 codes.

RESULTS

A total of 1848 patients were identified. With a median follow-up of 2.5 years, 297 patients (16.1%) developed jaw complications: 226 patients had a diagnosis, 41 patients had a procedure, and 30 patients had both. On multivariate analysis, female sex, lack of chemotherapy use, and fewer comorbidities were associated with a statistically significant increase in jaw complications.

CONCLUSIONS

Even with modern techniques, jaw complications are a notable and potentially devastating side effect of RT for oral cancers.

A feature-based approach for refinement of model-based segmentation of low contrast structures

Accuracy and robustness are fundamental requirements of any automated method used for segmentation of medical images. Model-based segmentation (MBS) is a well established technique, where uncertainties in image content can be to a certain extent compensated by the use of prior shape information. This approach is, however, often problematic in cases where image information does not allow for generating a strong feature response, one example being soft tissue organs in CT data, which typically appear in low contrast. In this paper, we enhance our recently proposed framework for voxel classification-based refinement of MBS using a level-set segmentation technique with shape priors. We also introduce a novel feature weighting methodology that improves the performance of the classifier, demonstrating results superior to the previous feature selection method. Results of fully automated segmentation of low contrast organs in head and neck CT are presented. Compared to our previous approach, we have achieved an increase of up to 22% in segmentation accuracy.