ArticleScientific reports2021
A predictive model for pain response following radiotherapy for treatment of spinal metastases.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- Radiomics and Back Pain.Global spine journal · 2026Review
- A CT-based radiomics model for predicting pain relief after radiotherapy in patients with bone metastases: a dual-center study.Frontiers in oncology · 2026Article
- Complication avoidance, rehabilitation, pain therapy and palliative care for patients with metastatic spine tumors: WFNS spine committee recommendations.Neurosurgical review · 2024Review
- Review
- Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review.JMIR cancer · 2024Review
- Computational Modeling, Augmented Reality, and Artificial Intelligence in Spine Surgery.Advances in experimental medicine and biology · 2024Review
- The importance of planning CT-based imaging features for machine learning-based prediction of pain response.Scientific reports · 2023Article
- MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases.Insights into imaging · 2023Article
- Predictive model based on DCE-MRI and clinical features for the evaluation of pain response after stereotactic body radiotherapy in patients with spinal metastases.European radiology · 2023Article
- Article
- Differentiation of predominantly osteolytic from osteoblastic spinal metastases based on standard magnetic resonance imaging sequences: a comparison of radiomics model versus semantic features logistic regression model findings.Quantitative imaging in medicine and surgery · 2022Article
- Review
- Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest.Scientific reports · 2022Article
- Differentiation between spinal multiple myeloma and metastases originated from lung using multi-view attention-guided network.Frontiers in oncology · 2022Article
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Authors and funding
9 authors.
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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.
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