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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

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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. / Gentile, Francesco; La Civita, Evelina; Ventura, Bartolomeo Della; Ferro, Matteo; Bruzzese, Dario; Crocetto, Felice; Tennstedt, Pierre; Steuber, Thomas; Velotta, Raffaele; Terracciano, Daniela.

in: CANCERS, Jahrgang 15, Nr. 5, 1355, 21.02.2023.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Gentile, F, La Civita, E, Ventura, BD, Ferro, M, Bruzzese, D, Crocetto, F, Tennstedt, P, Steuber, T, Velotta, R & Terracciano, D 2023, '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', CANCERS, Jg. 15, Nr. 5, 1355. https://doi.org/10.3390/cancers15051355

APA

Gentile, F., La Civita, E., Ventura, B. D., Ferro, M., Bruzzese, D., Crocetto, F., Tennstedt, P., Steuber, T., Velotta, R., & Terracciano, D. (2023). 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. CANCERS, 15(5), [1355]. https://doi.org/10.3390/cancers15051355

Vancouver

Gentile F, La Civita E, Ventura BD, Ferro M, Bruzzese D, Crocetto F et al. 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. CANCERS. 2023 Feb 21;15(5). 1355. https://doi.org/10.3390/cancers15051355

Bibtex

@article{dbc4f0401a7b4d38a769ee63611bc575,
title = "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",
abstract = "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.",
author = "Francesco Gentile and {La Civita}, Evelina and Ventura, {Bartolomeo Della} and Matteo Ferro and Dario Bruzzese and Felice Crocetto and Pierre Tennstedt and Thomas Steuber and Raffaele Velotta and Daniela Terracciano",
year = "2023",
month = feb,
day = "21",
doi = "10.3390/cancers15051355",
language = "English",
volume = "15",
journal = "CANCERS",
issn = "2072-6694",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "5",

}

RIS

TY - JOUR

T1 - 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

AU - Gentile, Francesco

AU - La Civita, Evelina

AU - Ventura, Bartolomeo Della

AU - Ferro, Matteo

AU - Bruzzese, Dario

AU - Crocetto, Felice

AU - Tennstedt, Pierre

AU - Steuber, Thomas

AU - Velotta, Raffaele

AU - Terracciano, Daniela

PY - 2023/2/21

Y1 - 2023/2/21

N2 - 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.

AB - 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.

U2 - 10.3390/cancers15051355

DO - 10.3390/cancers15051355

M3 - SCORING: Journal article

C2 - 36900150

VL - 15

JO - CANCERS

JF - CANCERS

SN - 2072-6694

IS - 5

M1 - 1355

ER -