A Novel Machine Learning-based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator

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Autor(es): dc.contributorTauranga Public Hospital-
Autor(es): dc.contributorUniversity of Auckland-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFlinders University-
Autor(es): dc.contributorUniversity Hospital of Zurich-
Autor(es): dc.contributorIcahn School of Medicine at Mount Sinai-
Autor(es): dc.creatorVasconcelos Ordones, Flavio-
Autor(es): dc.creatorKawano, Paulo Roberto-
Autor(es): dc.creatorVermeulen, Lodewikus-
Autor(es): dc.creatorHooshyari, Ali-
Autor(es): dc.creatorScholtz, David-
Autor(es): dc.creatorGilling, Peter John-
Autor(es): dc.creatorForeman, Darren-
Autor(es): dc.creatorKaufmann, Basil-
Autor(es): dc.creatorPoyet, Cedric-
Autor(es): dc.creatorGorin, Michael-
Autor(es): dc.creatorBarbosa, Abner Macola Pacheco-
Autor(es): dc.creatorda Rocha, Naila Camila-
Autor(es): dc.creatorde Andrade, Luis Gustavo Modelli-
Data de aceite: dc.date.accessioned2025-08-21T23:44:03Z-
Data de disponibilização: dc.date.available2025-08-21T23:44:03Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2025-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.urology.2024.11.001-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307058-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307058-
Descrição: dc.descriptionObjective: To create a machine-learning predictive model combining prostate imaging-reporting and data system (PI-RADS) score, PSA density, and clinical variables to predict clinically significant prostate cancer (csPCa). Methods: We evaluated a cohort of patients who underwent prostate biopsy for suspected prostate cancer (PCa) in New Zealand, Australia, and Switzerland. We collected data on age, body mass index (BMI), PSA level, prostate volume, PSA density (PSAD), PI-RADS scores, previous biopsy, and corresponding histology results. The dataset was divided into derivation (training) and validation (test) sets using random splits. An independent dataset was obtained from the Harvard Dataverse for external validation. A cohort of 1272 patients was analyzed. We fitted a Lasso model, XGBoost, and LightGBM to the training set and assessed their accuracy. Results: All models demonstrated ROC-AUC values ranging from 0.830 to 0.851. LightGBM was considered the superior model, with an ROC of 0.851 (95%CI: 0.804-0.897) in the test set and 0.818 (95% CI: 0.798-0.831) in the external dataset. The most important variable was PI-RADS, followed by PSA density, history of previous biopsy, age, and BMI. Conclusion: We developed a predictive model for detecting csPCa that exhibited a high ROC-AUC value for internal and external validations. This suggests that the integration of the clinical parameters outperformed each individual predictor. Additionally, the model demonstrated good calibration metrics, indicative of a more balanced model than the existing models.-
Descrição: dc.descriptionTauranga Public Hospital, Bay of Plenty-
Descrição: dc.descriptionUniversity of Auckland-
Descrição: dc.descriptionUrology Department UNESP São Paulo State University, SP-
Descrição: dc.descriptionCollege of Medicine and Public Health Flinders University-
Descrição: dc.descriptionDepartment of Urology University Hospital of Zurich-
Descrição: dc.descriptionDepartment of Urology Icahn School of Medicine at Mount Sinai-
Descrição: dc.descriptionDepartment of Internal Medicine UNESP São Paulo State University, SP-
Descrição: dc.descriptionUrology Department UNESP São Paulo State University, SP-
Descrição: dc.descriptionDepartment of Internal Medicine UNESP São Paulo State University, SP-
Formato: dc.format20-26-
Idioma: dc.languageen-
Relação: dc.relationUrology-
???dc.source???: dc.sourceScopus-
Título: dc.titleA Novel Machine Learning-based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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