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. 2021 Feb 18;11(2):335.
doi: 10.3390/diagnostics11020335.

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

Francesco Gentile  1   2 , Matteo Ferro  2   3 , Bartolomeo Della Ventura  2   4 , Evelina La Civita  2   5 , Antonietta Liotti  2   5 , Michele Cennamo  5 , Dario Bruzzese  2   6 , Raffaele Velotta  2   4 , Daniela Terracciano  2   5
Affiliations

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

Francesco Gentile et al. Diagnostics (Basel). .

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.

Keywords: PSA density; PSA molecular forms; artificial neural network; prostate cancer; tumor markers.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Summary of included patients.
Figure 2
Figure 2
Gleason score reported as a function of several different clinical variables and age used in this study (a). The artificial neural network (ANN) used to analyze data and build a predictive model of PC aggressiveness. The network receives as an input the values of the 4 clinical variables of the study. These variables are combined together through successive layers and transmitted to an output layer which determines the grade of the cancer (b).
Figure 3
Figure 3
The prediction of the AI model on a cohort of 43 patients used as a validation set compared to the true values of GS measured from those patients (a). Using a cut-off value of 7, the model achieved 86% sensitivity and 74% specificity (b).
Figure 4
Figure 4
Visual representation of the output of the AI model as a function of the values of the clinical variables tPSA, fPSA, p2PSA, and PSA density. In the upper (lower) row, we report the combination of variables associated with aggressive (non-aggressive) forms of PC, with GS ≥ 7 (GS < 7).
Figure 5
Figure 5
We measured the performance of the model using 4 different metrics, i.e., accuracy (A), sensitivity (Se), specificity (Sp), and precision (P). The error (e) is the mean of these 4 metrics. We report in the diagrams the values of A, Se, Sp, P, and e determined for the AI model receiving as an input different combinations of the clinical variables used in the study plus age. The model achieved maximum efficiency using as input variables: tPSA, fPSA, p2PSA, and PSA density.
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