Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features.

Francesco PrataUmberto AnceschiErmanno CordelliEliodoro FaiellaAngelo CivitellaPiergiorgio TuzzoloAndrea IannuzziAlberto RagusaFrancesco EspertoSalvatore Mario PrataRosa SiciliaGiovanni MutoRosario Francesco GrassoRoberto Mario ScarpaPaolo SodaGiuseppe SimoneRocco Papalia

Published in: Current oncology (Toronto, Ont.) (2023)

Radiomic analysis allowed us to develop a tool that was able to predict a Gleason score of ≥7. This new tool may improve the detection rate of clinically significant prostate cancer and overcome the limitations of the subjective interpretation of magnetic resonance imaging, reducing the number of useless biopsies.

Keyphrases

prostate cancer
radical prostatectomy
magnetic resonance imaging
machine learning
computed tomography
artificial intelligence
loop mediated isothermal amplification
magnetic resonance
deep learning
physical activity