Negative influence of changing biopsy practice patterns on the predictive value of prostate-specific antigen for cancer detection on prostate biopsy

Decision models for distinguishing between clinically insignificant and significant tumors in prostate cancer biopsies: an application of Bayes' Theorem to reduce costs and improve outcomes

Prostate cancer is the second leading cause of death from cancer, behind lung cancer, for men in the U. S, with nearly 30,000 deaths per year. A key problem is the difficulty in distinguishing, after biopsy, between significant cancers that should be treated immediately and clinically insignificant tumors that should be monitored by active surveillance. Prostate cancer has been over-treated; a recent European randomized screening trial shows overtreatment rates of 40%. Overtreatment of insignificant tumors reduces quality of life, while delayed treatment of significant cancers increases the incidence of metastatic disease and death. We develop a decision analysis approach based on simulation and probability modeling. For a given prostate volume and number of biopsy needles, our rule is to treat if total length of cancer in needle cores exceeds c, the cutoff value, with active surveillance otherwise, provided pathology is favorable. We determine the optimal cutoff value, c*. There are two misclassification costs: treating a minimal tumor and not treating a small or medium tumor (large tumors were never misclassified in our simulations). Bayes' Theorem is used to predict the probabilities of minimal, small, medium, and large cancers given the total length of cancer found in biopsy cores. A 20 needle biopsy in conjunction with our new decision analysis approach significantly reduces the expected loss associated with a patient in our target population about to undergo a biopsy. Longer needles reduce expected loss. Increasing the number of biopsy cores from the current norm of 10-12 to about 20, in conjunction with our new decision model, should substantially improve the ability to distinguish minimal from significant prostate cancer by minimizing the expected loss from over-treating minimal tumors and delaying treatment of significant cancers.

Outcomes and trends of prostate biopsy for prostate cancer in Chinese men from 2003 to 2011

Background

Prostate-specific antigen (PSA) screening is growing in popularity in China, but its impact on biopsy characteristics and outcomes are poorly understood.

Objective

Our objective was to characterize prostate biopsy outcomes and trends in Chinese men over a 10-year period, since the increasing use of PSA tests.

Methods

All men (n = 1,650) who underwent prostate biopsy for PCa at Huashan Hospital, Shanghai, China from 2003–2011 were evaluated. Demographic and clinical information was collected for each patient, including age, digital rectal examination (DRE), transrectal ultrasound (prostate volume and nodule), total prostate-specific antigen (tPSA) levels and free PSA ratio (fPSA/tPSA) prior to biopsy. Prostate biopsy was performed using six cores before October 2007 or ten cores thereafter. Logistic regression and multivariate analysis were used to evaluate our data.

Results

The overall positive rate of prostate biopsy for PCa was 47% and the rate decreased significantly over the years from 74% in 2003 to 33% in 2011 (P-trend = 0.004) . Age at diagnosis was slightly increased (P-trend = 0.04) while fPSA/tPSA was significantly decreased (P-trend = 1.11×10-5). A statistically significant trend was not observed for tPSA levels, prostate volume, or proportion of positive nodule. The model including multiple demographic and clinical variables (i.e., age, DRE, tPSA, fPSA/tPSA and transrectal ultrasound results) (AUC = 0.93) statistically outperformed models that included only PSA (AUC = 0.85) or fPSA/tPSA (AUC = 0.66) to predict PCa risks (P<0.05). Similar results were observed in a subgroup of men whose tPSA levels were lower than 20 ng/mL (AUC = 0.87, vs. AUC of tPSA  = 0.62, P<0.05).

Conclusions

Detection rates of PCa and high-grade PCa among men that underwent prostate biopsy at the institution has decreased significantly in the past 10 years, likely due to increasing use of PSA tests. Predictive performance of demographic and clinical variables of PCa was excellent. These variables should be used in clinics to determine the need for prostate biopsy.

The relationship between prostate-specific antigen and prostate cancer risk: the Prostate Biopsy Collaborative Group

PURPOSE

The relationship between prostate-specific antigen (PSA) level and prostate cancer risk remains subject to fundamental disagreements. We hypothesized that the risk of prostate cancer on biopsy for a given PSA level is affected by identifiable characteristics of the cohort under study.

EXPERIMENTAL DESIGN

We used data from five European and three U.S. cohorts of men undergoing biopsy for prostate cancer; six were population-based studies and two were clinical cohorts. The association between PSA and prostate cancer was calculated separately for each cohort using locally weighted scatterplot smoothing.

RESULTS

The final data set included 25,772 biopsies and 8,503 cancers. There were gross disparities between cohorts with respect to both the prostate cancer risk at a given PSA level and the shape of the risk curve. These disparities were associated with identifiable differences between cohorts: for a given PSA level, a greater number of biopsy cores increased the risk of cancer (odds ratio for >6- versus 6-core biopsy, 1.35; 95% confidence interval, 1.18-1.54; P < 0.0005); recent screening led to a smaller increase in risk per unit change in PSA (P = 0.001 for interaction term) and U.S. cohorts had higher risk than the European cohorts (2.14; 95% confidence interval, 1.99-2.30; P < 0.0005).

CONCLUSIONS

Our results suggest that the relationship between PSA and risk of a positive prostate biopsy varies, both in terms of the probability of prostate cancer at a given PSA value and the shape of the risk curve. This poses challenges to the use of PSA-driven algorithms to determine whether biopsy is indicated.

Electrical properties of prostatic tissues: I. Single frequency admittivity properties

PURPOSE

Electrical properties of the prostate may provide sufficient contrast for distinguishing malignant and benign formations in the gland. We evaluated how well these electrical properties discriminate cancer from noncancer tissues in the prostate.

MATERIALS AND METHODS

Electrical admittivity (conductivity and permittivity) was recorded at 31 discrete frequencies of 0.1 to 100 kHz from each of 50 ex vivo human prostates. A specifically designed admittivity probe was used to gauge these electrical properties from sectioned prostate specimens. The specific tissue area probed was marked to provide precise colocalization between tissue histological assessment and recorded admittivity spectra.

RESULTS

Adenocarcinoma, benign prostatic hyperplasia, nonhyperplastic glandular tissue and stromal tissue were the primary tissue types probed. Mean cancer conductivity was significantly less than that of glandular and stromal tissues at all frequencies (p <0.05), while mean cancer permittivity was significantly greater than that of all benign tissues at 100 kHz (p <0.0001). ROC curves showed that permittivity at 100 kHz was optimal for discriminating cancer from all benign tissues. This parameter had 77% specificity at 70% sensitivity and an ROC AUC of 0.798.

CONCLUSIONS

The contrast in electrical admittivity properties of different prostate tissues shows promise for distinguishing cancer from benign tissues. Sensitivity and specificity exceed those reported for current prostate specific antigen screening practices at low prostate specific antigen, making this an attractive addition to the clinical armamentarium for identifying prostate cancer.

Techniques and predictive models to improve prostate cancer detection

The use of prostate-specific antigen (PSA) as a screening test remains controversial. There have been several attempts to refine PSA measurements to improve its predictive value. These modifications, including PSA density, PSA kinetics, and the measurement of PSA isoforms, have met with limited success. Therefore, complex statistical and computational models have been created to assess an individual's risk of prostate cancer more accurately. In this review, the authors examined the methods used to modify PSA as well as various predictive models used in prostate cancer detection. They described the mathematical underpinnings of these techniques along with their intrinsic strengths and weaknesses, and they assessed the accuracy of these methods, which have been shown to be better than physicians' judgment at predicting a man's risk of cancer. Without understanding the design and limitations of these methods, they can be applied inappropriately, leading to incorrect conclusions. These models are important components in counseling patients on their risk of prostate cancer and also help in the design of clinical trials by stratifying patients into different risk categories. Thus, it is incumbent on both clinicians and researchers to become familiar with these tools. Cancer 2009;115(13 suppl):3085-99. (c) 2009 American Cancer Society.

Dynamic contrast-enhanced MR imaging in the evaluation of patients with prostate cancer

Prostate cancer is a common tumor among men, with increasing diagnosis at an earlier stage and a lower volume of disease because of screening with prostate-specific antigen (PSA). The need for imaging of the prostate stems from a desire to optimize treatment strategy on a patient and tumor-specific level. The major goals of prostate imaging are (1) staging of known cancer, (2) determination of tumor aggressiveness, (3) diagnosis of cancer in patients who have elevated PSA but a negative biopsy, (4) treatment planning, and (5) the evaluation of therapy response. This article concentrates on the role of dynamic contrast-enhanced MR imaging in the evaluation of patients who have prostate cancer and how it might be used to help achieve the above goals. Various dynamic contrast enhancement approaches (quantitative/semiquantitative/qualitative, high temporal versus high spatial resolution) are summarized with reference to the relevant strengths and compromises of each approach.