Cancer Radiomic and Perfusion Imaging Automated Framework: Validation on Musculoskeletal Tumors.
Elvis Duran SierraRaul F ValenzuelaMathew A CanjirathinkalColleen M CostelloeHeerod MoradiJohn E MadewellWilliam A MurphyBehrang AminiPublished in: JCO clinical cancer informatics (2024)
The CARPI processing of two clinical validation data sets confirmed the software application's ability to differentiate between different types of tumors and help predict patient response to treatment on the basis of radiomic features. Benchmark comparison with five similar open-source solutions demonstrated the advantages of CARPI in the automated perfusion feature extraction, relational database generation, and graphic report export features, although lacking a user-friendly graphical user interface and predictive model building.
Keyphrases
- machine learning
- deep learning
- high throughput
- contrast enhanced
- papillary thyroid
- high resolution
- big data
- case report
- electronic health record
- squamous cell
- artificial intelligence
- data analysis
- emergency department
- magnetic resonance imaging
- squamous cell carcinoma
- computed tomography
- adverse drug
- childhood cancer
- single cell
- young adults
- fluorescence imaging
- neural network
- smoking cessation
- drug induced