Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT.
Simon BernatzVitali KochDaniel Pinto Dos SantosJörg AckermannLeon D GrünewaldInga WeitkampIbrahim YelSimon S MartinLukas LengaJan-Erik ScholtzThomas J VoglScherwin MahmoudiPublished in: International journal of computer assisted radiology and surgery (2023)
Radiomics may have the potential to objectively stratify visually unequivocal nodal lymphoma versus benign lymph nodes. Radiomics seems superior to spectral DECT material decomposition in this use case. Therefore, artificial intelligence methodologies may not be restricted to centers with DECT equipment.
Keyphrases
- contrast enhanced
- dual energy
- artificial intelligence
- lymph node
- computed tomography
- diffusion weighted
- magnetic resonance imaging
- magnetic resonance
- image quality
- big data
- machine learning
- diffuse large b cell lymphoma
- diffusion weighted imaging
- deep learning
- physical activity
- positron emission tomography
- neoadjuvant chemotherapy
- lymph node metastasis
- early stage
- sentinel lymph node
- climate change
- risk assessment
- locally advanced