| Autor: |
Yin Lei, Tian Jie Li, Peng Gu, Yu kun Yang, Lei Zhao, Chao Gao, Juan Hu, Xiao Dong Liu |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Frontiers in oncology. 12 |
| ISSN: |
2234-943X |
| Popis: |
Globally, Prostate cancer (PCa) is the second most common cancer in the male population worldwide, but clinically significant prostate cancer (CSPCa) is more aggressive and causes to more deaths. The authors aimed to construct the risk category based on Prostate Imaging Reporting and Data System score version 2.1 (PI-RADS v2.1) in combination with Prostate-Specific Antigen Density (PSAD) to improve CSPCa detection and avoid unnecessary biopsy. Univariate and multivariate logistic regression and receiver-operating characteristic (ROC) curves were performed to compare the efficacy of the different predictors. The results revealed that PI-RADS v2.1 score and PSAD were independent predictors for CSPCa. Moreover, the combined factor shows a significantly higher predictive value than each single variable for the diagnosis of CSPCa. According to the risk stratification model constructed based on PI-RADS v2.1 score and PSAD, patients with PI-RADS v2.1 score of ≤2, or PI-RADS V2.1 score of 3 and PSA density of 2, can avoid unnecessary of prostate biopsy and does not miss clinically significant prostate cancer. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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