Li J, Zhu C, Yang S, Mao Z, Lin S, Huang H, Xu S. Non-Invasive Diagnosis of Prostate Cancer and High-Grade Prostate Cancer Using
Multiparametric Ultrasonography and Serological Examination.
Ultrasound Med Biol 2024;
50:600-609. [PMID:
38238199 DOI:
10.1016/j.ultrasmedbio.2024.01.003]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/26/2023] [Accepted: 01/01/2024] [Indexed: 02/17/2024]
Abstract
OBJECTIVES
This study aimed to assess the efficacy of multiparametric ultrasonography (mpUS) combined with serological examination, as a non-invasive method, in detecting prostate cancer (PCa) or high-grade prostate cancer (HGPCa) respectively.
METHODS
A cohort of 245 individuals with clinically suspected PCa were enrolled. All subjects underwent a comprehensive evaluation, including basic data collection, serological testing, mpUS and prostate biopsy. Random Forest (RF) models were developed, and the mean area under the curve (AUC) in 100 cross-validations was used to assess the performance in distinguishing PCa from HGPCa.
RESULTS
mpUS features showed significant differences (p < 0.001) between the PCa and non-PCa groups, as well as between the HGPCa and low-grade prostate cancer (LGPCa) groups including prostate-specific antigen density (PSAD), transrectal real-time elastography (TRTE) and intensity difference (ID). The RF model, based on these features, demonstrated an excellent discriminative ability for PCa with a mean area under the curve (AUC) of 0.896. Additionally, another model incorporating free prostate-specific antigen (FPSA) and color Doppler flow imaging (CDFI) achieved a high accuracy in predicting HGPCa with a mean AUC of 0.830. The nomogram derived from these models exhibited excellent individualized prediction of PCa and HGPCa.
CONCLUSION
The RF models incorporating mpUS and serological variables achieved satisfactory accuracies in predicting PCa and HGPCa.
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