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A big data-based prediction model for prostate cancer incidence in Japanese men
To define a normal range for PSA values (ng/mL) by age and create a prediction model for prostate cancer incidence. We conducted a retrospective analysis using 263,073 observations of PSA values in Japanese men aged 18–98 years (2007–2017), including healthy men and those diagnosed with prostate can...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121595/ https://www.ncbi.nlm.nih.gov/pubmed/37085532 http://dx.doi.org/10.1038/s41598-023-33725-8 |
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author | Kato, Mineyuki Horiguchi, Go Ueda, Takashi Fujihara, Atsuko Hongo, Fumiya Okihara, Koji Marunaka, Yoshinori Teramukai, Satoshi Ukimura, Osamu |
author_facet | Kato, Mineyuki Horiguchi, Go Ueda, Takashi Fujihara, Atsuko Hongo, Fumiya Okihara, Koji Marunaka, Yoshinori Teramukai, Satoshi Ukimura, Osamu |
author_sort | Kato, Mineyuki |
collection | PubMed |
description | To define a normal range for PSA values (ng/mL) by age and create a prediction model for prostate cancer incidence. We conducted a retrospective analysis using 263,073 observations of PSA values in Japanese men aged 18–98 years (2007–2017), including healthy men and those diagnosed with prostate cancer. Percentiles for 262,639 PSA observations in healthy men aged 18–70 years were calculated and plotted to elucidate the normal fluctuation range for PSA values by age. Univariable and multivariable logistic regression analyses were performed to develop a predictive model for prostate cancer incidence. PSA levels and PSA velocity increased with age in healthy men. However, there was no difference in PSA velocity with age in men diagnosed with prostate cancer. Logistic regression analysis showed an increased risk of prostate cancer for PSA slopes ranging from 0.5 to 3.5 ng/mL/year. This study provides age-specific normal fluctuation ranges for PSA levels in men aged 18–75 years and presents a novel and personalized prediction model for prostate cancer incidence. We found that PSA slope values of > 3.5 ng/mL/year may indicate a rapid increase in PSA levels caused by pathological condition such as inflammation but are unlikely to indicate cancer risk. |
format | Online Article Text |
id | pubmed-10121595 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101215952023-04-23 A big data-based prediction model for prostate cancer incidence in Japanese men Kato, Mineyuki Horiguchi, Go Ueda, Takashi Fujihara, Atsuko Hongo, Fumiya Okihara, Koji Marunaka, Yoshinori Teramukai, Satoshi Ukimura, Osamu Sci Rep Article To define a normal range for PSA values (ng/mL) by age and create a prediction model for prostate cancer incidence. We conducted a retrospective analysis using 263,073 observations of PSA values in Japanese men aged 18–98 years (2007–2017), including healthy men and those diagnosed with prostate cancer. Percentiles for 262,639 PSA observations in healthy men aged 18–70 years were calculated and plotted to elucidate the normal fluctuation range for PSA values by age. Univariable and multivariable logistic regression analyses were performed to develop a predictive model for prostate cancer incidence. PSA levels and PSA velocity increased with age in healthy men. However, there was no difference in PSA velocity with age in men diagnosed with prostate cancer. Logistic regression analysis showed an increased risk of prostate cancer for PSA slopes ranging from 0.5 to 3.5 ng/mL/year. This study provides age-specific normal fluctuation ranges for PSA levels in men aged 18–75 years and presents a novel and personalized prediction model for prostate cancer incidence. We found that PSA slope values of > 3.5 ng/mL/year may indicate a rapid increase in PSA levels caused by pathological condition such as inflammation but are unlikely to indicate cancer risk. Nature Publishing Group UK 2023-04-21 /pmc/articles/PMC10121595/ /pubmed/37085532 http://dx.doi.org/10.1038/s41598-023-33725-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kato, Mineyuki Horiguchi, Go Ueda, Takashi Fujihara, Atsuko Hongo, Fumiya Okihara, Koji Marunaka, Yoshinori Teramukai, Satoshi Ukimura, Osamu A big data-based prediction model for prostate cancer incidence in Japanese men |
title | A big data-based prediction model for prostate cancer incidence in Japanese men |
title_full | A big data-based prediction model for prostate cancer incidence in Japanese men |
title_fullStr | A big data-based prediction model for prostate cancer incidence in Japanese men |
title_full_unstemmed | A big data-based prediction model for prostate cancer incidence in Japanese men |
title_short | A big data-based prediction model for prostate cancer incidence in Japanese men |
title_sort | big data-based prediction model for prostate cancer incidence in japanese men |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10121595/ https://www.ncbi.nlm.nih.gov/pubmed/37085532 http://dx.doi.org/10.1038/s41598-023-33725-8 |
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