A Scalable Radiomics- and Natural Language Processing-Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study
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Summary
This summary is machine-generated.Artificial intelligence and radiomics can identify painful bone metastases (BMs) from CT scans. This machine learning pipeline offers a scalable method for pain biomarker discovery in cancer patients.
Area Of Science
- Oncology
- Radiology
- Medical Informatics
- Artificial Intelligence
Background
- Objective pain biomarkers are crucial for understanding cancer pain prognosis and management.
- Artificial intelligence (AI) can identify pain biomarkers in patients with bone metastases (BMs).
Purpose Of The Study
- Develop and evaluate a scalable AI pipeline using natural language processing (NLP) and radiomics.
- Differentiate between painful and painless BM lesions in CT images using imaging features.
Main Methods
- Retrospective study of 176 patients with thoracic spine BMs.
- NLP extracted physician-reported pain scores from clinical notes.
- Radiomics features extracted from CT-based regions of interest (ROIs).
- Machine learning models evaluated using standard performance metrics.
Main Results
- A neural network classifier achieved an area under the receiver operating characteristic curve of 0.83 in the test set.
- The best model demonstrated 82% accuracy and 85% specificity.
- The pipeline successfully differentiated painful from painless BM lesions.
Conclusions
- The developed NLP and radiomics pipeline effectively distinguishes painful from painless BM lesions.
- The method is scalable, leveraging NLP for pain scores and CT imaging for BM identification.
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