Author Correction: Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy.

In the original version of the Data Descriptor the surname of author Hesham Elhalawani was misspelled. This has now been corrected in the HTML and PDF versions.

Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy.

Cross sectional imaging is essential for the patient-specific planning and delivery of radiotherapy, a primary determinant of head and neck cancer outcomes. Due to challenges ensuring data quality and patient de-identification, publicly available datasets including diagnostic and radiation treatment planning imaging are scarce. In this data descriptor, we detail the collection and processing of computed tomography based imaging in 215 patients with head and neck squamous cell carcinoma that were treated with radiotherapy.

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges.

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated.

Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function.

Purpose: Automating and standardizing the contouring of clinical target volumes (CTVs) can reduce interphysician variability, which is one of the largest sources of uncertainty in head and neck radiation therapy. In addition to using uniform margin expansions to auto-delineate high-risk CTVs, very little work has been performed to provide patient- and disease-specific high-risk CTVs. The aim of the present study was to develop a deep neural network for the auto-delineation of high-risk CTVs.

Patient reported dry mouth: Instrument comparison and model performance for correlation with quality of life in head and neck cancer survivors.

Purpose: To identify a clinically meaningful cut-point for the single item dry mouth question of the MD Anderson Symptom Inventory-Head and Neck module (MDASI-HN).

Methods: Head and neck cancer survivors who had received radiation therapy (RT) completed the MDASI-HN, the University of Michigan Hospital Xerostomia Questionnaire (XQ), and the health visual analog scale (VAS) of the EuroQol Five Dimension Questionnaire (EQ-5D). The Bayesian information criteria (BIC) were used to test the prediction power of each tool for EQ-5D VAS.

Correction to: Long-term patient reported outcomes following radiation therapy for oropharyngeal cancer: cross-sectional assessment of a prospective symptom survey in patients ≥65 years old.

In the original publication [1] the name of author Jeremy M. Aymard was spelled wrong. The original article was updated to rectify this error.

Long-term patient reported outcomes following radiation therapy for oropharyngeal cancer: cross-sectional assessment of a prospective symptom survey in patients ≥65 years old.

Background: Given the potential for older patients to experience exaggerated toxicity and symptoms, this study was performed to characterize patient reported outcomes in older patients following definitive radiation therapy (RT) for oropharyngeal cancer (OPC).

Methods: Cancer-free head and neck cancer survivors (>6 months since treatment completion) were eligible for participation in a questionnaire-based study. Participants completed the MD Anderson Symptom Inventory-Head and Neck module (MDASI-HN).