Using clinical parameters to predict prostate cancer and reduce the unnecessary biopsy among patients with PSA in the gray zone

Liu, Junxiao; Dong, Biao; Qu, Wugong; Wang, Jian‐Ge; Xu, Yue; Yu, Shuanbao; Zhang, Xuepei

doi:10.1038/s41598-020-62015-w

Cited by 33 publications

(50 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The free PSA (fPSA) is the fraction of tPSA that does not bind in serum, whose concentration is lower in malignancy patients than in BPH patients, so detection of f/t PSA is most valuable for clinical application when PSA level are in the gray area [ 23 , 24 ]. Our results also show that increased f/t PSA level (OR=0.01, P <0.01) was negatively correlated with the risk of PCa, which was different from the results of Liu et al [ 19 ]. The unstable state of fPSA in the serum may be the reason for the inconsistent detection results of f/t PSA, and some scholars believe that f/t PSA may be affected by a variety of clinical factors [ 25 ].…”

Section: Discussioncontrasting

confidence: 99%

“…However, that study did not consider imageology examination. Liu et al constructed a prediction model of PCa by using PSA derivatives and mpMRI, and the multivariate models performed significantly better for detection of PCa (AUC=0.79) and CS-PCa (AUC=0.84), but the authors did not assess the sensitivity and specificity of these 2 regression models in the article [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

“…Our results also verified TRUS had little value for detection of PCa with PSA level in the gray area, while mpMRI including T1WI, T2WI, DWI, and DCE technologies was confirmed to have a perfect diagnostic value, which was similar to other studies [ 30 – 32 ]. Liu et al just reported the AUCs of mpMRI for PCa and CS-PCa were 0.69 and 0.79, respectively [ 19 ]. In our study, the sensitivities of mpMRI for predicting PCa and CS-PCa were 0.63 and 0.82, respectively, which is similar to the results of other scholars.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Diagnosis of Prostate Cancer in Patients with Prostate-Specific Antigen (PSA) in the Gray Area: Construction of 2 Predictive Models

Chen

et al. 2021

Med Sci Monit

View full text Add to dashboard Cite

Background Two diagnostic models of prostate cancer (PCa) and clinically significant prostate cancer (CS-PCa) were established using clinical data of among patients whose prostate-specific antigen (PSA) levels are in the gray area (4.0–10.0 ng/ml). Material/Methods Data from 181 patients whose PSA levels were in the gray area were retrospectively analyzed, and the following data were collected: age, digital rectal examination, total PSA, PSA density (PSAD), free/total PSA (f/t PSA), transrectal ultrasound, multiparametric magnetic resonance imaging (mpMRI), and pathological reports. Patients were diagnosed with benign prostatic hyperplasia (BPH) and PCa by pathology reports, and PCa patients were separated into non-clinically significant PCa (NCS-PCa) and CS-PCa by Gleason score. Afterward, predictor models constructed by above parameters were researched to diagnose PCa and CS-PCa, respectively. Results According to the analysis of included clinical data, there were 109 patients with BPH, 44 patients with NCS-PCa, and 28 patients with CS-PCa. Regression analysis showed PCa was correlated with f/t PSA, PSAD, and mpMRI ( P <0.01), and CS-PCa was correlated with PSAD and mpMRI ( P <0.01). The area under the receiver operating characteristic curves of 2 models for PCa (sensitivity=73.64%, specificity=64.23%) and for CS-PCa (sensitivity=71.41%, specificity=81.82%) were 0.79 and 0.87, respectively. Conclusions The prediction models had satisfactory diagnostic value for PCa and CS-PCa among patients with PSA in the gray area, and use of these models may help reduce overdiagnosis.

show abstract

Section: Discussioncontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Diagnosis of Prostate Cancer in Patients with Prostate-Specific Antigen (PSA) in the Gray Area: Construction of 2 Predictive Models

Chen

et al. 2021

Med Sci Monit

View full text Add to dashboard Cite

show abstract

“…If suspected malignant nodules by mpMRI and/or ultrasound, additional 1–5 needles were performed in regions with cognitive MRI-TURS fusion and/or abnormal ultrasound echoes. Biopsy cores were analyzed according to the standards of ISUP ( 21 ).…”

Section: Methodsmentioning

confidence: 99%

Multivariable Models Incorporating Multiparametric Magnetic Resonance Imaging Efficiently Predict Results of Prostate Biopsy and Reduce Unnecessary Biopsy

Hong

Jin

et al. 2020

Front. Oncol.

Self Cite

View full text Add to dashboard Cite

Purpose We sought to develop diagnostic models incorporating mpMRI examination to identify PCa (Gleason score≥3+3) and CSPCa (Gleason score≥3+4) to reduce overdiagnosis and overtreatment. Methods We retrospectively identified 784 patients according to inclusion criteria between 2016 and 2020. The cohort was split into a training cohort of 548 (70%) patients and a validation cohort of 236 (30%) patients. Age, PSA derivatives, prostate volume, and mpMRI parameters were assessed as predictors for PCa and CSPCa. The multivariable models based on clinical parameters were evaluated using area under the curve (AUC), calibration plots, and decision curve analysis (DCA). Results Univariate analysis showed that age, tPSA, PSAD, prostate volume, MRI-PCa, MRI-seminal vesicle invasion, and MRI-lymph node invasion were significant predictors for both PCa and CSPCa (each p ≤0.001). PSAD has the highest diagnostic accuracy in predicting PCa (AUC=0.79) and CSPCa (AUC=0.79). The multivariable models for PCa (AUC=0.92, 95% CI: 0.88–0.96) and CSPCa (AUC=0.95, 95% CI: 0.92–0.97) were significantly higher than the combination of derivatives for PSA ( p =0.041 and 0.009 for PCa and CSPCa, respectively) or mpMRI (each p <0.001) in diagnostic accuracy. And the multivariable models for PCa and CSPCa illustrated better calibration and substantial improvement in DCA at threshold above 10%, compared with PSA or mpMRI derivatives. The PCa model with a 30% cutoff or CSPCa model with a 20% cutoff could spare the number of biopsies by 53%, and avoid the number of benign biopsies over 80%, while keeping a 95% sensitivity for detecting CSPCa. Conclusion Our multivariable models could reduce unnecessary biopsy without comprising the ability to diagnose CSPCa. Further prospective validation is required.

show abstract

“…PCa is the most familiar malignant tumor with high morbidity and mortality among men in developed countries [23,24]. With the popularization of PSA screening in China, the number of patients with prostate cancer has increased year by year, causing a huge economic and health burden to the society [25]. Due to prostate cancer is a heterogeneous multifocal solid tumor, it is di cult to accurately predict the prognosis of each PCa patient.…”

Section: Discussionmentioning

confidence: 99%

KIF15 is An Oncogene of Prostate Cancer and is A Key Prognostic Factor in Patients with Prostate Cancer

Hao

Liu

et al. 2021

Preprint

View full text Add to dashboard Cite

Background KIF15, a member of kinesin superfamily proteins, has been found that play a of vital role in the carcinogenesis of various malignant tumor. But whether KIF15 can facilitate the evolution of prostate cancer (PCa) is still unknown. This study aims to explore its biological function in PCa cells and its relevance to prognosis and clinical features in PCa patients. Material and Methods KIF15 expression at mRNA and protein level in tumor and normal tissues was detected by quantitative real-time PCR (RT-PCR) and immunohistochemistry. Then the correlations between KIF15 expression and PCa patients’ clinical characteristics was analyzed. After inhibiting the expression of KIF15 by shRNA, the role of KIF15 on proliferative capacity of PCa was evaluated by using MTT assay. The function of KIF15 on metastatic potential of PCa was determined by using transwell assay. The prognostic value of KIF15 was determined by using bioinformatics analysis. Results Compared with normal tissues, KIF15 was overexpressed in PCa tissues. After knocking down KIF15 in C4-2 and Lncap cell lines, the proliferation (P < 0.001) and invasion (P < 0.001) capabilities of tumor cells are significantly reduced compared to the shCON group. The proliferation marker Ki67 and the metastasis-related marker MMP9 were also significantly reduced in two cell lines after silencing KIF15. Except that, increased KIF15 in tumor tissue is associated with clinical stage (P = 0.004), seminal vesicle invasion (P = 0.02), lymph node metastasis (P = 0.03), and poor disease-free survival (P < 0.05) in PCa patients. Conclusions The results proved that KIF15 might served as a prognostic factor and therapeutic target in prostate cancer, and play as a vital regulatory factor in tumorigenesis and cancer development of prostate cancer.

show abstract

Using clinical parameters to predict prostate cancer and reduce the unnecessary biopsy among patients with PSA in the gray zone

Cited by 33 publications

References 33 publications

Diagnosis of Prostate Cancer in Patients with Prostate-Specific Antigen (PSA) in the Gray Area: Construction of 2 Predictive Models

Diagnosis of Prostate Cancer in Patients with Prostate-Specific Antigen (PSA) in the Gray Area: Construction of 2 Predictive Models

Multivariable Models Incorporating Multiparametric Magnetic Resonance Imaging Efficiently Predict Results of Prostate Biopsy and Reduce Unnecessary Biopsy

KIF15 is An Oncogene of Prostate Cancer and is A Key Prognostic Factor in Patients with Prostate Cancer

Contact Info

Product

Resources

About