A Neural Network Model Combining [-2]proPSA, freePSA, Total PSA, Cathepsin D, and Thrombospondin-1 Showed Increased Accuracy in the Identification of Clinically Significant Prostate Cancer.

Autor: Gentile F; Nanotechnology Research Centre, Department of Experimental and Clinical Medicine, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy.; ElicaDea, Spinoff of Federico II University, 80131 Naples, Italy., La Civita E; ElicaDea, Spinoff of Federico II University, 80131 Naples, Italy.; Department of Translational Medical Sciences, University of Naples 'Federico II', 80131 Naples, Italy., Ventura BD; ElicaDea, Spinoff of Federico II University, 80131 Naples, Italy.; Department of Physics 'Ettore Pancini', University of Naples 'Federico II', 80126 Naples, Italy., Ferro M; ElicaDea, Spinoff of Federico II University, 80131 Naples, Italy.; Division of Urology, European Institute of Oncology (IEO), IRCCS, 20141 Milan, Italy., Bruzzese D; ElicaDea, Spinoff of Federico II University, 80131 Naples, Italy.; Department of Public Health, Federico II University of Naples, 80131 Naples, Italy., Crocetto F; Department of Neurosciences, Sciences of Reproduction and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy., Tennstedt P; Martini-Klinik, University Hospital Hamburg-Eppendorf, 20246 Hamburg, Germany., Steuber T; Martini-Klinik, University Hospital Hamburg-Eppendorf, 20246 Hamburg, Germany., Velotta R; Department of Physics 'Ettore Pancini', University of Naples 'Federico II', 80126 Naples, Italy., Terracciano D; ElicaDea, Spinoff of Federico II University, 80131 Naples, Italy.; Department of Translational Medical Sciences, University of Naples 'Federico II', 80131 Naples, Italy.
Jazyk: angličtina
Zdroj: Cancers [Cancers (Basel)] 2023 Feb 21; Vol. 15 (5). Date of Electronic Publication: 2023 Feb 21.
DOI: 10.3390/cancers15051355
Abstrakt: Background: The Prostate Health Index (PHI) and Proclarix (PCLX) have been proposed as blood-based tests for prostate cancer (PCa). In this study, we evaluated the feasibility of an artificial neural network (ANN)-based approach to develop a combinatorial model including PHI and PCLX biomarkers to recognize clinically significant PCa (csPCa) at initial diagnosis.
Methods: To this aim, we prospectively enrolled 344 men from two different centres. All patients underwent radical prostatectomy (RP). All men had a prostate-specific antigen (PSA) between 2 and 10 ng/mL. We used an artificial neural network to develop models that can identify csPCa efficiently. As inputs, the model uses [-2]proPSA, freePSA, total PSA, cathepsin D, thrombospondin, and age.
Results: The output of the model is an estimate of the presence of a low or high Gleason score PCa defined at RP. After training on a dataset of up to 220 samples and optimization of the variables, the model achieved values as high as 78% for sensitivity and 62% for specificity for all-cancer detection compared with those of PHI and PCLX alone. For csPCa detection, the model showed 66% (95% CI 66-68%) for sensitivity and 68% (95% CI 66-68%) for specificity. These values were significantly different compared with those of PHI ( p < 0.0001 and 0.0001, respectively) and PCLX ( p = 0.0003 and 0.0006, respectively) alone.
Conclusions: Our preliminary study suggests that combining PHI and PCLX biomarkers may help to estimate, with higher accuracy, the presence of csPCa at initial diagnosis, allowing a personalized treatment approach. Further studies training the model on larger datasets are strongly encouraged to support the efficiency of this approach.
Databáze: MEDLINE
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