A novel assessment of whole-mount Gleason grading in prostate cancer to identify candidates for radical prostatectomy: a machine learning-based multiomics study
Jing Ning, Clemens P. Spielvogel, David Haberl, Karolína Trachtová, Stefan Stoiber, Sazan Rasul, Vojtěch Bystrý, Gabriel Wasinger, Pascal Baltzer, Elisabeth Gurnhofer, Gerald Timelthaler, Michaela Schlederer, Laszlo A. Papp, Helga Schachner, Thomas H. Helbich, Markus Hartenbach, Bernhard Grubmüller, Shahrokh F. Shariat, Marcus Hacker, Alexander Haug, Lukas Kenner
Abstract
: The findings demonstrate a superior assessment of the developed multiomics-based ML model in whole-mount GG compared to the current clinical baseline of bxGG. This enables personalized patient management by identifying high-risk PCa patients for RP.
Topics & Concepts
ProstatectomyMedicineProstate cancerRadiomicsMagnetic resonance imagingProstateBiopsyGrading (engineering)OncologyNuclear medicineInternal medicineRadiologyCancerBiologyEcologyProstate Cancer Treatment and ResearchRadiomics and Machine Learning in Medical ImagingProstate Cancer Diagnosis and Treatment