RADIANS: A Multidisciplinary Central Nervous System Clinic Model for Radiation Oncology and Neurosurgery Practice

Shearwood McClelland, Timur Mitin, Jerry J. Jaboin, Jeremy N. Ciporen

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Background: Radiation therapy for central nervous system disease commonly involves collaboration between Radiation Oncology and Neurosurgery. We describe our early experience with a multidisciplinary clinic model. Methods: In 2016, the novel RADIANS (RADIation oncology And NeuroSurgery) clinic model was initiated at a community hospital. Disease and treatment demographics were collected and analyzed. Patient satisfaction was assessed via a blinded survey questionnaire. Results: Forty-two patients have been seen since the inception of RADIANS. The median age was 65; and the median patient distance from RADIANS was 42.7 miles (mean = 62.6; range = 0.7–285). Half of the patients traveled >50 miles to receive care, and >80% were seen for central nervous system metastases. Of the patients receiving radiation, 75% received stereotactic radiosurgery/stereotactic body radiation therapy. The mean overall satisfaction from 0 (not satisfied) to 5 (very satisfied) was 4.8. Conclusions: The RADIANS clinic model has proved viable and well-liked by patients in a community setting, with the majority of radiation therapy administered being stereotactic radiosurgery/stereotactic body radiation therapy rather than conventional fractionation.

Original languageEnglish (US)
Pages (from-to)8-10
Number of pages3
JournalWorld Neurosurgery
Volume122
DOIs
StatePublished - Feb 2019

Keywords

  • Central nervous system metastases
  • Community hospital
  • Multidisciplinary clinic
  • Neurosurgery
  • RADIANS
  • Radiation oncology

ASJC Scopus subject areas

  • Surgery
  • Clinical Neurology

Fingerprint

Dive into the research topics of 'RADIANS: A Multidisciplinary Central Nervous System Clinic Model for Radiation Oncology and Neurosurgery Practice'. Together they form a unique fingerprint.

Cite this