AAPM MPLA case: Betrayal versus disillusionment.

This work of fiction depicts a scenario in which a faculty member felt they were criticized unfairly and inappropriately for honesty on a faculty survey, which reflected poorly on administration. The faculty member was left struggling with how to respond to conflicting feelings and perception of misaligned goals and mission. Simultaneously, the department chair felt they were blindsided by issues that could have been addressed without the embarrassment of a poor survey.

A brain MRI dataset and baseline evaluations for tumor recurrence prediction after Gamma Knife radiotherapy.

Prediction and identification of tumor recurrence are critical for brain cancer treatment design and planning. Stereotactic radiation therapy delivered with Gamma Knife has been developed as one of the common treatment approaches combined with others by delivering radiation that targets accurately on the tumor while not affecting nearby healthy tissues. In this paper, we release a fully publicly available brain cancer MRI dataset and the companion Gamma Knife treatment planning and follow-up data for the purpose of tumor recurrence prediction.

Model development of dose and volume predictors for esophagitis induced during chemoradiotherapy for lung cancer as a step towards radiobiological treatment planning.

Background: Currently, radiation therapy treatment planning system intends biological optimization that relies heavily upon plan metrics from tumor control probability (TCP) and normal tissue complication probability (NTCP) modeling. Implementation and expansion of TCP and NTCP models with alternative data is an important step towards reliable radiobiological treatment planning. In this retrospective single institution study, the treatment charts of 139 lung cancer patients treated with chemo-radiotherapy were reviewed and correlated dosimetric predictors with the incidence of esophagitis and established NTCP model of esophagitis grade 1 and 2 for lung cancer patients.

A gradient mapping guided explainable deep neural network for extracapsular extension identification in 3D head and neck cancer computed tomography images.

Background: Diagnosis and treatment management for head and neck squamous cell carcinoma (HNSCC) is guided by routine diagnostic head and neck computed tomography (CT) scans to identify tumor and lymph node features. The extracapsular extension (ECE) is a strong predictor of patients' survival outcomes with HNSCC. It is essential to detect the occurrence of ECE as it changes staging and treatment planning for patients.

Building Better Patient Care in Mississippi Radiation Oncology: Why Mississippi Needs a Collaborative Quality Initiative.

Objectives: Cancer is an insidious and devastating disease that affects many people. Progress in mortality rate has not been realized universally across the United States, and challenges remain in how to best make up the ground that has been lost in these areas, one of which is Mississippi. Radiation therapy is a significant contributor to cancer control rates and certain challenges exist specifically regarding this treatment modality.