Patients with progression of spinal metastases who present to the clinic have better outcomes compared to those who present to the emergency department.

Background: As cancer therapies have improved, spinal metastases are increasingly common. Resulting complications have a significant impact on patient's quality of life. Optimal methods of surveillance and avoidance of neurologic deficits are understudied.

Big data analysis of associations between patient reported outcomes, observer reported toxicities, and overall quality of life in head and neck cancer patients treated with radiation therapy.

Purpose: To assess associations between a variety of patient-reported outcomes (PROs), observer reported toxicities (ORTs), and patient-reported overall quality of life (QOL) for head and neck cancer patients treated with radiotherapy, in order to identify important items for inclusion in prospective patient reporting in the clinic.

Methods: 612 patients completed 27 PRO items from three questionnaires at 1273 follow-up visits, and clinicians provided ORTs according to CTCAE criteria. Using a big data approach, we measured associations among all PROs, between all PROs and ORTs, and between PROs/ORTs and QOL with Pearson (ρ) and Kendall (τ) correlation coefficients, and a novel analysis method based on receiver operating characteristic (ROC) curves used to detect thresholds in response levels demonstrating strong interactions.

The big data effort in radiation oncology: Data mining or data farming?

Although large volumes of information are entered into our electronic health care records, radiation oncology information systems and treatment planning systems on a daily basis, the goal of extracting and using this big data has been slow to emerge. Development of strategies to meet this goal is aided by examining issues with a data farming instead of a data mining conceptualization. Using this model, a vision of key data elements, clinical process changes, technology issues and solutions, and role for professional societies is presented.

SafetyNet: Streamlining and Automating QA in radiotherapy.

Proper quality assurance (QA) of the radiotherapy process can be time-consuming and expensive. Many QA efforts, such as data export and import, are inefficient when done by humans. Additionally, humans can be unreliable, lose attention, and fail to complete critical steps that are required for smooth operations.