Abstract
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.
Highlights
To establish a predictive model for pain response following radiotherapy using a combination of radiomic and clinical features of spinal metastasis
To better understand pain response following radiotherapy for spinal metastasis, we performed a retrospective analysis using patient clinical features and radiology-based images. 69 patients were enrolled, and their clinical features are listed in Table 1. 48 patients were classified as responders and 21 as non-responders (Fig. 3)
Using a combination of clinical features and radiomics, we sought to establish a predictive model for patient pain response to radiotherapy treatment
Summary
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. We built a pain response model in patients with spinal metastases using a combination of clinical and radiomic features. In a report on predictive clinical models, the World Health Organization performance status (PS), numerical rating scale (NRS), and primary tumor site were important factors for predicting pain r elief[3]. We built a highly accurate model using radiomic and clinical features for predicting pain relief after radiotherapy for painful spinal metastases. Our models provide proof-of-concept for predictive tools for pain response and, with further refinement, will become an essential clinical tool for patients with bone metastases
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
