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.
Hossein NaseriSonia SkameneMarwan TolbaMame Daro FayePaul RamiaJulia KhriguianMarc DavidJohn KildeaPublished in: JMIR AI (2023)
Our NLP- and radiomics-based machine learning pipeline was successful in differentiating between painful and painless BM lesions. It is intrinsically scalable by using NLP to extract pain scores from clinical notes and by requiring only center points to identify BM lesions in CT images.
Keyphrases
- machine learning
- contrast enhanced
- deep learning
- spinal cord
- artificial intelligence
- magnetic resonance imaging
- lymph node metastasis
- computed tomography
- big data
- chronic pain
- magnetic resonance
- neuropathic pain
- convolutional neural network
- pain management
- autism spectrum disorder
- cross sectional
- oxidative stress
- image quality
- squamous cell carcinoma
- anti inflammatory
- positron emission tomography