Prostate-specific antigen kinetics contributes to decision making for biopsy referral: the predictive implication for PSA retest in patients with elevated PSA levels

Kobayashi, Minoru; Kijima, Toshiki; Yashi, Masahiro; Kamai, Takao

doi:10.1016/j.prnil.2022.08.001

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…Our finding that PSA normalisation is particularly common in younger men agrees well with the results from the German screening study, in which as many as 48% of 45-year-old men had a PSA < 3 µg/L at a repeat PSA test 2 weeks after the first [8] and a recent Japanese study [9]. The latter study identified ejaculation and infection as possible causes of transiently rising PSA values.…”

Section: Discussionsupporting

confidence: 90%

Normalised repeat serum prostate-specific antigen: associations with age and magnetic resonance imaging results

Dang,

Huang,

Bratt

2024

SJU

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Objective: To assess the value of a repeat prostate-specific antigen measurement (PSA2) before magnetic resonance imaging (MRI) in men with a raised PSA (PSA1) <10 µg/L. Method: Medical records of men aged < 75 years referred in 2021 for PSA1 3.0–9.9 µg/L (< 70 years) or 5.0–9.9 µg/L (70–74 years) were reviewed. PSA2 was sampled before MRI within 60 days from PSA1. Odds ratios (ORs) were calculated with logistic regression. Chi-square and trend-test were used for categorical variables. Results: A total of 341 men were included. Median time between PSA1 and PSA2 was 28 days (interquartile range 20–35 days). PSA normalised in 16% (95% confidence interval [CI]: 13–21). Younger men were more likely to have a normal PSA2 (OR: 0.95 per year older, 95% CI: 0.92–0.99). Among men aged < 70 years, those with PSA1 < 5 µg/L were more likely to have normalised PSA2 than those with PSA1 ≥ 5 µg/L (21% vs. 10%, p = 0.01). A greater proportion of men with normalised PSA2 had a Prostate Imaging Data and Reporting System MRI score of 1–3 than men with non-normalised PSA2 (93% vs. 77%, p = 0.01). Conclusions: A clinically significant proportion of men with a moderately raised PSA value have a normal PSA2. Younger men and men with lower PSA1 were more likely to have a normal PSA2. Few men with normalised PSA2 had suspicious MRI findings. Routine repeat PSA-testing may be motivated in men with a moderately raised PSA value to save MRI resources, particularly in younger men.

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Section: Discussionsupporting

confidence: 90%

Normalised repeat serum prostate-specific antigen: associations with age and magnetic resonance imaging results

Dang,

Huang,

Bratt

2024

SJU

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“…To mitigate diagnostic bias, leveraging the precise anatomical information provided by MRI is invaluable, as it offers non-invasive insights. This is particularly crucial since PSA levels can fluctuate and be influenced by various factors ( 165 , 166 ). Furthermore, the role of mpMRI in assessing PCa recurrence has gained importance ( 167 ), underscoring the need for comprehensive investigation into MRI-based PCa recurrence prediction.…”

Section: Application Of Mpmri-based Dla On Pcamentioning

confidence: 99%

Research progress on deep learning in magnetic resonance imaging–based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

He,

Cao,

Chi

et al. 2023

Front. Oncol.

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Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.

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“…26,27 Therefore, analyzing the correlation between WWI and PSA in PCa patients would be beneficial not only for preventing the bias of PSA testing but also for predicting the PSA level to some extent when diagnosing prostate-related diseases. In addition, the PSA level is known to be affected by multiple factors that may lead to fluctuations in the PSA result, 28 which can confuse the subsequent diagnosis and treatment planning. Calculating WWI could assist physicians in determining whether repeated PSA tests are necessary to a certain extent, as WWI is readily accessible and adequately stable, with the ultimate goal of reducing the risk of overdiagnosis and overtreatment.…”

Section: Discussionmentioning

confidence: 99%

Can weight-adjusted waist circumference index become a single anthropometric predictor of prostate-specific antigen concentration? A National Health and Nutrition Examination Survey analysis (2003–2010)

Yang,

Cui,

Cao

et al. 2024

Journal of Investigative Medicine

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Recent studies have introduced the weight-adjusted waist circumference index (WWI) as a viable obesity indicator that may better reflect centripetal obesity and its associated risks. In examining the connection between WWI and prostate-specific antigen (PSA), this study leveraged data from the National Health and Nutrition Examination Survey (NHANES) 2003-2010, including 5732 participants. Our initial analysis indicated a significant positive association between WWI and PSA levels. However, subsequent models that adjusted for covariates such as age, race, and a range of metabolic and cardiovascular health-related factors revealed that the strength and significance of this relationship were attenuated. Model 1 showed a highly significant correlation (P < 0.0001). Yet, in Model 2, which accounted for age and race, the association softened (P = 0.0520). Moreover, when a full spectrum of health covariates was included in Model 3, the association was no longer significant (P = 0.9775). These findings suggest that while an unadjusted correlation exists, its potential use as a diagnostic predictor is limited without considering the broader health context. Therefore, it is crucial to review such data with multiple considerations in mind, and extensive attention should be paid to the evaluation of covariates.

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Prostate-specific antigen kinetics contributes to decision making for biopsy referral: the predictive implication for PSA retest in patients with elevated PSA levels

Cited by 8 publications

References 35 publications

Normalised repeat serum prostate-specific antigen: associations with age and magnetic resonance imaging results

Normalised repeat serum prostate-specific antigen: associations with age and magnetic resonance imaging results

Research progress on deep learning in magnetic resonance imaging–based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Can weight-adjusted waist circumference index become a single anthropometric predictor of prostate-specific antigen concentration? A National Health and Nutrition Examination Survey analysis (2003–2010)

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