Improving prostate cancer detection in veterans through the development of a clinical decision rule for prostate biopsy

Prediction models for prostate cancer to be used in the primary care setting: a systematic review

OBJECTIVE

To identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.

DESIGN

Systematic review.

DATA SOURCES

MEDLINE and Embase databases combined from inception and up to the end of January 2019.

ELIGIBILITY

Studies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

DATA EXTRACTION AND SYNTHESIS

Relevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.

RESULTS

An initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.

CONCLUSION

Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.

Electrochemical enzyme-linked immunosorbent assay (ELISA) for α-fetoprotein based on glucose detection with multienzyme-nanoparticle amplification

Since glucose biosensors are one of the most popular and widely used point-of-care testing devices, a novel electrochemical enzyme-linked immunosorbent assay (ELISA) for protein biomarkers has been developed based on a glucose detection strategy. In this study, α-fetoprotein (AFP) was used as the target protein. An electrochemical ELISA system was constructed using anti-AFP antibodies immobilized on microwell plates as the capture antibody (Ab1) and multi-label bioconjugates as signal tracer. The bioconjugates were synthesized by attaching glucoamylase and the secondary anti-AFP antibodies (Ab2) to gold nanoparticles (AuNPs). After formation of the sandwich complex, the Ab2-glucoamylase-AuNPs conjugates converted starch into glucose in the presence of AFP. The concentration of AFP can be calculated based on the linear relation between AFP and glucose, the concentration of which can be detected by the glucose biosensor. When the AFP concentration ranged from 0.05 to 100 ng/mL, a linear calibration plot (i (µA) = 13.62033 - 2.86252 logCAFP (ng/mL), r = 0.99886) with a detection limit of 0.02 ng/mL was obtained under optimal conditions. The electrochemical ELISA developed in this work shows acceptable stability and reproducibility, and the assay for AFP spiked in human serum also shows good recovery (97.0%-104%). This new method could be applied for detecting any protein biomarker with the corresponding antibodies.