Login / Signup

Demystifying neuroblastoma malignancy through fractal dimension, entropy, and lacunarity.

Irene DonatoKiran K VelpulaAndrew J TsungJack A TuszynskiConsolato Maria Sergi
Published in: Tumori (2023)
, and λ of imaging recognize groups in neuroblastic tumors. We suggest that future studies including these features may challenge the current Shimada classification of neuroblastoma with categories of favorable and unfavorable histology. It is expected that this methodology could trigger multicenter studies and potentially find practical use in the clinical setting of children's hospitals worldwide.
Keyphrases
  • case control
  • high resolution
  • machine learning
  • healthcare
  • deep learning
  • young adults
  • current status
  • clinical trial
  • fluorescence imaging