Performances of multivariable models in characterizing csPCa-A in PZ.

NIAID Data Ecosystem2026-03-10 收录

数据链接：

https://figshare.com/articles/dataset/Performances_of_multivariable_models_in_characterizing_csPCa-A_in_PZ_/5098240 数据链接链接失效反馈

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资源简介：

Performances of multivariable models in characterizing csPCa-A in PZ.

创建时间：

2017-06-10

table_2.xlsx

前列腺癌诊断

类固醇生物标志物

BackgroundSerum steroids are crucial molecules altered in prostate cancer (PCa). Mass spectrometry (MS) is currently the elected technology for the analysis of steroids in diverse biological samples.

NIAID Data Ecosystem50

Odds of prostate cancer with PLA2G16 methylation alone or with clinical factors in prostate biopsies.

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甲基化DNA生物标志物

Odds of prostate cancer with PLA2G16 methylation alone or with clinical factors in prostate biopsies.

NIAID Data Ecosystem40

Diagnostic accuracy and safety of image-guided transperineal versus transrectal prostate biopsy for clinically significant prostate cancer: a GRADE-assessed systematic review and meta-analysis

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医疗活检安全性

This systematic review and meta-analysis compared the diagnostic performance and complication profiles of transperineal biopsy (TPBx) versus transrectal biopsy (TRBx) for prostate cancer, incorporatin

Figshare2025-12-16 更新20

Plasma circulating MiRNAs: a new potential biomarker for prostate cancer diagnosis.. Plasma circulating MiRNAs: a new potential biomarker for prostate cancer diagnosis.

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miRNA生物标志物

Purpose: PCa is the second most commonly diagnosed malignancy in men. PCa Diagnosis are based on biopsy sampling that is an invasive, expensive procedure and does not accurately represent multifocal d

NIAID Data Ecosystem50

OTHER AND UNSPECIFIED MALE GENITAL ORGANS, Vas deferens - Male, 60 - Tissue image [3300730069422361]

男性生殖病理学

前列腺癌诊断

This is the histopathological image of OTHER AND UNSPECIFIED MALE GENITAL ORGANS tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pann

DataCite Commons2022-05-24 更新30

Performances of multivariable models in characterizing csPCa-A in PZ.

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