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A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model.

Guillermo LorenzoJon S HeiselmanMichael A LissMichael I MigaHector GomezThomas E YankeelovAlessandro RealiThomas J R Hughes
Published in: Cancer research communications (2024)
Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.
Keyphrases
  • prostate cancer
  • radical prostatectomy
  • decision making
  • big data
  • poor prognosis
  • case report
  • electronic health record
  • machine learning
  • long non coding rna
  • photodynamic therapy
  • fluorescence imaging