Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning.
Lorenzo UggaRenato CuocoloDomenico SolariElia GuadagnoAlessandra D'AmicoTeresa SommaPaolo CappabiancaMaria Laura Del Basso de CaroLuigi Maria CavalloArturo BrunettiPublished in: Neuroradiology (2019)
Machine learning analysis of texture-derived parameters from preoperative T2 MRI has proven to be effective for the prediction of pituitary macroadenomas ki-67 proliferation index class. This might aid the surgical strategy making a more accurate preoperative lesion classification and allow for a more focused and cost-effective follow-up and long-term management.
Keyphrases
- machine learning
- contrast enhanced
- magnetic resonance imaging
- artificial intelligence
- patients undergoing
- magnetic resonance
- diffusion weighted imaging
- computed tomography
- deep learning
- big data
- growth hormone
- signaling pathway
- high resolution
- neoadjuvant chemotherapy
- squamous cell carcinoma
- lymph node metastasis
- radiation therapy