Reply to Jue et al. Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on "Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model. Diagnostics 2021, 11, 335".

Francesco Gentile,Daniela Terracciano,Antonietta Liotti,Michele Cennamo,Matteo Ferro,Evelina La Civita,Bartolomeo Della Ventura,Dario Bruzzese,Raffaele Velotta

doi:10.3390/diagnostics11071214

Abstract

In their comment "Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer [...].

Highlights

Francesco Gentile 1,*, Matteo Ferro 2, Bartolomeo Della Ventura 3, Evelina La Civita 4, Antonietta Liotti 4, Michele Cennamo 4, Dario Bruzzese 5, Raffaele Velotta 3 and Daniela Terracciano 4,*
Diagnostics 2021, 11, 335”, Jue and colleagues argued that, while artificial intelligence has the potential to revolutionize the detection of cancers and other pathologies in medicine, the use of PSA density in a deep learning model may be even more effective if, before the analysis, samples are stratified based on PSA values
Our group struggled with the use of additional biomarkers for identification of clinically significant prostate cancer (PCa) and recognised a potential of PSA molecular forms that should not be dismissed [1]

Summary

Introduction

Francesco Gentile 1,* , Matteo Ferro 2 , Bartolomeo Della Ventura 3 , Evelina La Civita 4, Antonietta Liotti 4, Michele Cennamo 4, Dario Bruzzese 5 , Raffaele Velotta 3 and Daniela Terracciano 4,*. In their comment “Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer.

Results

Conclusion