A predictive model for pain response following radiotherapy for treatment of spinal metastases

Sci Rep. 2021 Jun 18;11(1):12908. doi: 10.1038/s41598-021-92363-0.


To establish a predictive model for pain response following radiotherapy using a combination of radiomic and clinical features of spinal metastasis. This retrospective study enrolled patients with painful spine metastases who received palliative radiation therapy from 2018 to 2019. Pain response was defined using the International Consensus Criteria. The clinical and radiomic features were extracted from medical records and pre-treatment CT images. Feature selection was performed and a random forests ensemble learning method was used to build a predictive model. Area under the curve (AUC) was used as a predictive performance metric. 69 patients were enrolled with 48 patients showing a response. Random forest models built on the radiomic, clinical, and 'combined' features achieved an AUC of 0.824, 0.702, 0.848, respectively. The sensitivity and specificity of the combined features model were 85.4% and 76.2%, at the best diagnostic decision point. We built a pain response model in patients with spinal metastases using a combination of clinical and radiomic features. To the best of our knowledge, we are the first to examine pain response using pre-treatment CT radiomic features. Our model showed the potential to predict patients who respond to radiation therapy.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Disease Management
  • Disease Susceptibility
  • Female
  • Humans
  • Image Processing, Computer-Assisted
  • Male
  • Middle Aged
  • Pain / diagnosis*
  • Pain / etiology*
  • Prognosis
  • ROC Curve
  • Radiotherapy / methods
  • Research Design
  • Retrospective Studies
  • Spinal Neoplasms / complications*
  • Spinal Neoplasms / diagnosis
  • Spinal Neoplasms / radiotherapy
  • Tomography, X-Ray Computed
  • Treatment Outcome