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Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model

Authors :
Antonietta Liotti
Dario Bruzzese
Bartolomeo Della Ventura
Francesco Gentile
Matteo Ferro
Raffaele Velotta
Evelina La Civita
Daniela Terracciano
Michele Cennamo
Gentile, Francesco
Ferro, Matteo
Della Ventura, Bartolomeo
La Civita, Evelina
Liotti, Antonietta
Cennamo, Michele
Bruzzese, Dario
Velotta, Raffaele
Terracciano, Daniela
Source :
Diagnostics, Vol 11, Iss 335, p 335 (2021), Diagnostics, Volume 11, Issue 2
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

After skin cancer, prostate cancer (PC) is the most common cancer among men. The gold standard for PC diagnosis is based on the PSA (prostate-specific antigen) test. Based on this preliminary screening, the physician decides whether to proceed with further tests, typically prostate biopsy, to confirm cancer and evaluate its aggressiveness. Nevertheless, the specificity of the PSA test is suboptimal and, as a result, about 75% of men who undergo a prostate biopsy do not have cancer even if they have elevated PSA levels. Overdiagnosis leads to unnecessary overtreatment of prostate cancer with undesirable side effects, such as incontinence, erectile dysfunction, infections, and pain. Here, we used artificial neuronal networks to develop models that can diagnose PC efficiently. The model receives as an input a panel of 4 clinical variables (total PSA, free PSA, p2PSA, and PSA density) plus age. The output of the model is an estimate of the Gleason score of the patient. After training on a dataset of 190 samples and optimization of the variables, the model achieved values of sensitivity as high as 86% and 89% specificity. The efficiency of the method can be improved even further by training the model on larger datasets.

Details

ISSN :
20754418
Volume :
11
Database :
OpenAIRE
Journal :
Diagnostics
Accession number :
edsair.doi.dedup.....f0b82b5af475d445f6aec4b41fd81fd6
Full Text :
https://doi.org/10.3390/diagnostics11020335