An analysis of three different prostate cancer risk calculators applied prior to prostate biopsy: A Turkish cohort validation study

Yıldızhan, Mehmet; Balcı, Melih; Eroglu, Unsal; Asil, Erem; Coser, Seref; Ozercan, Ali Yasin; Köseoğlu, Burak; Güzel, Özer; Asfuroglu, Ahmet; Tuncel, Altuğ

doi:10.1111/and.14329

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…We compared six methods that have been proposed for handling missing data with the objective of finding the method most likely to perform well in multiple external validation studies of a globally accessible online risk tool. As with all online risk tools, online publication of the original PBCG continues to result in published external validation studies providing evidence for or against its generalizability to other populations, particularly in comparison to other published tools [4,[21][22][23][24][25][26]. To date, by exclusion of prostate volume, the original PBCG tool has competed less favorably with the other tools incorporating this information.…”

Section: Discussionmentioning

confidence: 99%

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

Neumair

Kattan

Freedland

et al. 2022

BMC Med Res Methodol

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Background We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. Methods Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. Results Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history. Conclusion Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.

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Section: Discussionmentioning

confidence: 99%

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

Neumair

Kattan

Freedland

et al. 2022

BMC Med Res Methodol

View full text Add to dashboard Cite

show abstract

“…We compared six methods that have been proposed for handling missing data with the objective of nding the method most likely to perform well in multiple external validation studies of a globally accessible online risk tool. As with all online risk tools, online publication of the original PBCG continues to result in published external validation studies providing evidence for or against its generalizability to other populations, particularly in comparison to other published tools [21,22,23,24,25,26,27]. To date, by exclusion of prostate volume, the original PBCG tool has competed less favorably with the other tools incorporating this information.…”

Section: Discussionmentioning

confidence: 99%

Globally accessible end-user-friendly prostate cancer risk prediction tools based on contemporary cohorts with heterogeneous missing risk factors

Neumair

Kattan

Freedland

et al. 2022

Preprint

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Background: Missing risk factors, whether random or not measured at all, across different hospitals and patients provide challenges, both for the developers of online clinical risk tools and the patients trying to use such tools. This paper provides a development and end-user solution to the commonly encountered limitations of clinical risk tool based decision making due to missing information.Methods: Six state-of-the-art logistic regression approaches accommodating missing data were compared using prostate cancer data from ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG). An additional large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach.Results: Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history.Conclusion: Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.

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“…Therefore, when an elevated PSA is found during PCa screening, a prostate biopsy is often performed for further diagnosis. Prostate biopsy is still the gold standard for PCa diagnosis [ 6 ]. Early PCa is an indolent tumor.…”

Section: Introductionmentioning

confidence: 99%

ZNF692 promotes cell proliferation, invasion and migration of human prostate cancer cells by targeting the EMT signaling pathway

Chen,

Li,

et al. 2024

Eur J Med Res

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Background Prostate cancer poses a considerable threat to human health. At present, the mechanism of tumor progression remains unclear. ZNF692 is overexpressed in many tumors, and the high expression of ZNF692 is correlated with tumor aggressiveness and tumor phenotype of prostate cancer, suggesting that ZNF692 may play an important role in tumor biology of prostate cancer. This paper aims to elucidate the relationship between them. Methods The expression level of ZNF692 was verified in normal prostate cells (RWPE-1) and prostate cancer cells (LNCaP, PC3, DU145). PC3 cells were selected to construct the ZNF692 knockout prostate cancer cell line. The changes of cell proliferation, apoptosis, invasion and metastasis were detected by CCK8, Edu staining, Transwell assay and scratch assay. The expression levels of related proteins were detected by Western blot. Results At the cellular level, ZNF692 was overexpressed to varying degrees in prostate cancer cell lines, with the highest expression in PC3 cell lines. CCK8 and Edu results showed that the proliferation of prostate cancer PC3 cells that knocked down ZNF692 was slowed. Transwell assay and scratch assay showed reduced invasion and migration of prostate cancer PC3 cells that knocked out ZNF692. Flow cytometry showed that the apoptosis rate of prostate cancer PC3 cells after ZNF692 knockout was increased. In addition, after ZNF692 silencing, the expression level of epithelial phenotype E-cadherin increased in PC3 cells, while the expression level of interstitial phenotype N-cadherin, Vimentin, c-Myc, and CyclinA1 decreased. The state of prostate cancer PC3 cells that overexpressed ZNF692 was reversed from the state after ZNF692 was knocked down. Conclusion ZNF692 can be used as a new prognostic marker and a potential biologic therapeutic target for PCa. By inhibiting the expression of c-myc and cyclinA1, the EMT signaling pathway is regulated to provide evidence for its potential molecular mechanism.

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An analysis of three different prostate cancer risk calculators applied prior to prostate biopsy: A Turkish cohort validation study

Cited by 4 publications

References 25 publications

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

Globally accessible end-user-friendly prostate cancer risk prediction tools based on contemporary cohorts with heterogeneous missing risk factors

ZNF692 promotes cell proliferation, invasion and migration of human prostate cancer cells by targeting the EMT signaling pathway

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