Background: Prostate-specific antigen (PSA) is widely used for prostate cancer screening, but low specificity results in high false positive rates of prostate biopsies.

Objective: To develop new risk assessment models to overcome the diagnostic limitation of PSA and reduce unnecessary prostate biopsies in North Chinese patients with 4-50 ng/mL PSA.

Methods: A total of 702 patients in seven hospitals with 4-10 and 10-50 ng/mL PSA, respectively, who had undergone transrectal ultrasound-guided prostate biopsies, were assessed. Analysis-modeling stage for several clinical indexes related to prostate cancer and renal function was carried out. Multiple logistic regression analyses were used to develop new risk assessment models of prostate cancer for both PSA level ranges 4-10 and 10-50 ng/mL. External validation stage of the new models was performed to assess the necessity of biopsy.

Results: The new models for both PSA ranges performed significantly better than PSA for detecting prostate cancers. Both models showed higher areas under the curves (0.937 and 0.873, respectively) compared with PSA alone (0.624 and 0.595), at pre-determined cut-off values of 0.1067 and 0.6183, respectively. Patients above the cut-off values were recommended for immediate biopsy, while the others were actively observed. External validation of the models showed significantly increased detection rates for prostate cancer (4-10 ng/mL group, 39.29% vs 17.79%, p=0.006; 10-50 ng/mL group, 71.83% vs 50.0%, p=0.015).

Conclusions: We developed risk assessment models for North Chinese patients with 4-50 ng/mL PSA to reduce unnecessary prostate biopsies and increase the detection rate of prostate cancer.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5354782PMC
http://dx.doi.org/10.18632/oncotarget.14214DOI Listing

Publication Analysis

Top Keywords

prostate cancer
20
risk assessment
16
assessment models
16
prostate biopsies
16
north chinese
12
chinese patients
12
patients 4-50
12
4-50 ng/ml
12
ng/ml psa
12
10-50 ng/ml
12

Similar Publications

Importance: Incidence of distant stage prostate cancer is increasing in the United States. Research is needed to understand trends by social and geographic factors.

Objective: To examine trends in prostate cancer incidence and mortality rates in California by stage, age, race and ethnicity, and region.

View Article and Find Full Text PDF

The word "cancer" evokes myriad emotions, ranging from fear and despair to hope and determination. Cancer is aptly defined as a complex and multifaceted group of diseases that has unapologetically led to the loss of countless lives and affected innumerable families across the globe. The battle with cancer is not only a physical battle, but also an emotional, as well as a psychological skirmish for patients and for their loved ones.

View Article and Find Full Text PDF

Due to the emergence of drug resistance, androgen receptor (AR)-targeted drugs still pose great challenges in the treatment of prostate cancer, and it is urgent to explore an innovative therapeutic strategy. MK-1775, a highly selective WEE1 inhibitor, is shown to have favorable therapeutic benefits in several solid tumor models. Recent evidence suggests that the combination of MK-1775 with DNA-damaging agents could lead to enhanced antitumor efficacy.

View Article and Find Full Text PDF

Harnessing machine learning to predict prostate cancer survival: a review.

Front Oncol

January 2025

Department of Urology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.

The prediction of survival outcomes is a key factor in making decisions for prostate cancer (PCa) treatment. Advances in computer-based technologies have increased the role of machine learning (ML) methods in predicting cancer prognosis. Due to the various effective treatments available for each non-linear landscape of PCa, the integration of ML can help offer tailored treatment strategies and precision medicine approaches, thus improving survival in patients with PCa.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!