Prostatic volume and volume-adjusted prostate-specific antigen as predictive parameters for prostate cancer patients with intermediate PSA levels

MRI-Based Nomogram of Prostate Maximum Sectional Area and Its Zone Area for Prediction of Prostate Cancer

Objective

To reduce unnecessary prostate biopsies, we designed a magnetic resonance imaging (MRI)-based nomogram prediction model of prostate maximum sectional area (PA) and investigated its zone area for diagnosing prostate cancer (PCa).

Methods

MRI was administered to 691 consecutive patients before prostate biopsies from January 2012 to January 2020. PA, central gland sectional area (CGA), and peripheral zone sectional area (PZA) were measured on axial T2-weighted prostate MRI. Multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curve were performed to evaluate and integrate the predictors of PCa. Based on multivariate logistic regression coefficients after excluding combinations of collinear variables, three models and nomograms were generated and intercompared by Delong test, calibration curve, and decision curve analysis (DCA).

Results

The positive rate of PCa was 46.74% (323/691). Multivariate analysis revealed that age, PSA, MRI, transCGA, coroPZA, transPA, and transPAI (transverse PZA-to-CGA ratio) were independent predictors of PCa. Compared with no PCa patients, transCGA (AUC = 0.801) was significantly lower and transPAI (AUC = 0.749) was significantly higher in PCa patients. Both of them have a significantly higher AUC than PSA (AUC = 0.714) and PV (AUC = 0.725). Our best predictive model included the factors age, PSA, MRI, transCGA, and coroPZA with the AUC of 0.918 for predicting PCa status. Based on this predictive model, a novel nomogram for predicting PCa was conducted and internally validated (C-index = 0.913).

Conclusions

We found the potential clinical utility of transCGA and transPAI in predicting PCa. Then, we firstly built the nomogram based on PA and its zone area to evaluate its diagnostic efficacy for PCa, which could reduce unnecessary prostate biopsies.

The roles of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen concentrations in predicting prostate cancer and high-risk prostate cancer

Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.

Prognostic value of preoperative carcinoembryonic antigen/tumor size in rectal cancer

BACKGROUND

Carcinoembryonic antigen (CEA) is a commonly used biomarker in colorectal cancer. However, controversy exists regarding the insufficient prognostic value of preoperative serum CEA alone in rectal cancer. Here, we combined preoperative serum CEA and the maximum tumor diameter to correct the CEA level, which may better reflect the malignancy of rectal cancer.

AIM

To assess the prognostic impact of preoperative CEA/tumor size in rectal cancer.

METHODS

We retrospectively reviewed 696 stage I to III rectal cancer patients who underwent curative tumor resection from 2007 to 2012. These patients were randomly divided into two cohorts for cross-validation: training cohort and validation cohort. The training cohort was used to generate an optimal cutoff point and the validation cohort was used to further validate the model. Maximally selected rank statistics were used to identify the optimum cutoff for CEA/tumor size. The Kaplan-Meier method and log-rank test were used to plot the survival curve and to compare the survival data. Univariate and multivariate Cox regression analyses were used to determine the prognostic value of CEA/tumor size. The primary and secondary outcomes were overall survival (OS) and disease-free survival (DFS), respectively.

RESULTS

In all, 556 patients who satisfied both the inclusion and exclusion criteria were included and randomly divided into the training cohort (2/3 of 556, n = 371) and the validation cohort (1/3 of 556, n = 185). The cutoff was 2.429 ng/mL per cm. Comparison of the baseline data showed that high CEA/tumor size was correlated with older age, high TNM stage, the presence of perineural invasion, high CEA, and high carbohydrate antigen 19-9 (CA 19-9). Kaplan-Meier curves showed a manifest reduction in 5-year OS (training cohort: 56.7% vs 81.1%, P < 0.001; validation cohort: 58.8% vs 85.6%, P < 0.001) and DFS (training cohort: 52.5% vs 71.9%, P = 0.02; validation cohort: 50.3% vs 79.3%, P = 0.002) in the high CEA/tumor size group compared with the low CEA/tumor size group. Univariate and multivariate analyses identified CEA/tumor size as an independent prognostic factor for OS (training cohort: hazard ratio (HR) = 2.18, 95% confidence interval (CI): 1.28-3.73, P = 0.004; validation cohort: HR = 4.83, 95%CI: 2.21-10.52, P < 0.001) as well as DFS (training cohort: HR = 1.47, 95%CI: 0.93-2.33, P = 0.096; validation cohort: HR = 2.61, 95%CI: 1.38-4.95, P = 0.003).

CONCLUSION

Preoperative CEA/tumor size is an independent prognostic factor for patients with stage I-III rectal cancer. Higher CEA/tumor size is associated with worse OS and DFS.

Comparison of cancer detection rates by transrectal prostate biopsy for prostate cancer using two different nomograms based on patient's age and prostate volume

Background

The aim of this study is to evaluate the efficacy of two different Nara Urological Research and Treatment Group (NURTG) nomograms allocating 6–12 biopsy cores based on age and prostate volume.

Materials and methods

From April 2006 to July 2014, a total of 1,605 patients who underwent initial prostate biopsy were enrolled. Based on a nomogram taking the patient’s age and prostate volume into consideration, 6–12 biopsy cores were allocated. Two types of nomogram were used, for the former group (before March 2009) and latter group (March 2009 onward). Cancer detection rates in all patients and those with prostate-specific antigen values in the gray zone (4.0–10 ng/mL) were compared. Predictive parameters for detection of prostate cancer in gray-zone patients were also investigated.

Results

The cancer detection rates in all patients and those in the gray zone were 48% and 38% in the former group and 54% and 41% in the latter group, respectively. The cancer detection rate in all patients was significantly higher in the latter group compared with the former group, but detection in gray-zone patients did not show a significant difference between the two groups (P=0.011 and P=0.37, respectively). Multivariate analysis indicated that age, digital rectal examination, prostate volume, transrectal ultrasonography findings, and volume/biopsy ratio were significant predictive parameters in gray-zone patients. The clinically insignificant cancer detection rate was significantly lower in the latter group compared with the former group (P=0.0008).

Conclusion

The latter nomogram provided more acceptable detection rates of clinically significant and insignificant cancer than the former one, and we consider that an initial maximum 12-core transrectal ultrasound-guided needle biopsy may be sufficient for prostate cancer diagnosis.

The application of p2PSA% and prostate health index in prostate cancer detection: A prospective cohort in a Tertiary Medical Center

BACKGROUND/PURPOSE

Prostate specific antigen (PSA) with low specificity that causes unnecessary prostate biopsies increases clinical morbidities, psychological stress, and medical expenses. We aimed to test the accuracy and cutoff value of Prostate Health Index (PHI) in men for prostate cancer detection.

METHODS

We prospectively enrolled 213 men who underwent prostate biopsy with PSA≦10 ng/ml or abnormal findings on digital rectal examination. Total PSA (tPSA), free PSA (fPSA) and p2PSA levels were measured by serum samples before prostate biopsy. PHI was calculated as (p2PSA/fPSA) × √tPSA. Multivariable logistic regression analyses were used to predict the risk of cancer and detect clinically significant prostate cancer.

RESULTS

33 (27.0%) patients were confirmed with the diagnoses of prostate cancer by prostate biopsy. The levels of p2PSA, %p2PSA, and PHI showed statistically significant differences between prostate cancer patients and non-cancer patients. %p2PSA and PHI had the highest area under the receiver operating characteristic curve (AUC) of 0.723 and 0.772 (both p < 0.001), respectively, predicting cancer detection at biopsy than other predictors (tPSA, fPSA, %fPSA, and PSA density (AUC: 0.544, 0.538, 0.593, and 0.664, respectively). In multivariable logistic regression, %p2PSA had a statistical significant odds ratio 8.51 (p = 0.003) and PHI had an odds ratio with marginal significance 4.18 (p = 0.06).

CONCLUSION

%p2PSA and PHI increased the diagnostic accuracy with significantly greater sensitivity and specificity than tPSA. We determined an optimal cut-off value of PHI among Taiwanese population. These findings support the usefulness in the decisional process of prostate biopsy.

Prostate specific antigen and acinar density: a new dimension, the "Prostatocrit"

Background

Prostate-specific antigen densities have limited success in diagnosing prostate cancer. We emphasise the importance of the peripheral zone when considered with its cellular constituents, the “prostatocrit”.

Objective

Using zonal volumes and asymmetry of glandular acini, we generate a peripheral zone acinar volume and density. With the ratio to the whole gland, we can better predict high grade and all grade cancer. We can model the gland into its acinar and stromal elements. This new “prostatocrit” model could offer more accurate nomograms for biopsy.

Materials and Methods

674 patients underwent TRUS and biopsy. Whole gland and zonal volumes were recorded. We compared ratio and acinar volumes when added to a “clinic” model using traditional PSA density. Univariate logistic regression was used to find significant predictors for all and high grade cancer. Backwards multiple logistic regression was used to generate ROC curves comparing the new model to conventional density and PSA alone.

Outcome and results

Prediction of all grades of prostate cancer: significant variables revealed four significant “prostatocrit” parameters: log peripheral zone acinar density; peripheral zone acinar volume/whole gland acinar volume; peripheral zone acinar density/whole gland volume; peripheral zone acinar density. Acinar model (AUC 0.774), clinic model (AUC 0.745) (P=0.0105). Prediction of high grade prostate cancer: peripheral zone acinar density (“prostatocrit”) was the only significant density predictor. Acinar model (AUC 0.811), clinic model (AUC 0.769) (P=0.0005).

Conclusion

There is renewed use for ratio and “prostatocrit” density of the peripheral zone in predicting cancer. This outperforms all traditional density measurements.

MRI evaluation of benign prostatic hyperplasia: Correlation with international prostate symptom score

PURPOSE

To investigate the correlation between magnetic resonance imaging (MRI)-derived prostate parameters and benign prostatic hyperplasia (BPH) type with the International Prostate Symptom Score (IPSS).

MATERIALS AND METHODS

In all, 61 patients (median age, 60; range, 41-81 years) who underwent preoperative MRI and prostatectomy were included in this retrospective study. The MRI-based parameters including total prostate volume (TPV), transition zone (TZ) volume (TZV), TZ index, intravesical prostatic protrusion (IPP), the anterior fibromuscular stroma (AFMS) distance, prostatic urethral angle, bladder wall thickness, urethral wall thickness, urethral compression, urethral wall changes, and BPH type were correlated with total IPSS, IPSS-storage symptom (IPSS-ss), IPSS-voiding symptom (IPSS-vs), and responses to the individual IPSS questions using Spearman (ρ) or Pearson (r) correlation coefficients, one-way analysis of variance (ANOVA), and multiple linear regression.

RESULTS

TPV (r = 0.414, P = 0.001), TZV (r = 0.405, P = 0.001), IPP (r = 0.270, P = 0.04), and AFMS distance (r = 0.363, P = 0.004) correlated with total IPSS. In multiple linear regression analysis, TZV was the only predictor for total IPSS (P = 0.001), IPSS-ss (P < 0.001), IPSS-vs (P = 0.03), and the scores for the IPSS questions 1 (P = 0.03) and 4 (P = 0.001). TPV was a predictor of the scores for questions 2 (P = 0.003), 3 (P = 0.009), and 7 (P < 0.001).

CONCLUSION

Several MRI-derived prostate measurements (TPV, TZV, IPP, AFMS distance) correlated with total IPSS. TZV was the only predictor for total IPSS based on multiple regression analysis.

LEVEL OF EVIDENCE

3 J. Magn. Reson. Imaging 2017;45:917-925.

The optimal number of initial prostate biopsy cores in daily practice: a prospective study using the Nara Urological Research and Treatment Group nomogram

Background

To elucidate the optimal number of prostate biopsy cores using a nomogram allocating 6–12 biopsy cores, the number generally used in daily practice, based on age and prostate volume (PV).

Methods

We enrolled 936 patients who received an initial prostate biopsy from April 2006 to January 2009. A number of 6–12 biopsy cores was allocated based on age and PV Nara Urological Research and Treatment Group (NURTG) nomogram. To elucidate the predictive parameters of cancer detection in patients with a prostate specific antigen (PSA) value in the gray zone, univariate and multivariate logistic regression analysis were carried out.

Results

The total cancer detection rate and the cancer detection rate in the PSA gray zone (4.1–10.0 ng/mL) were 48.0 and 37.6 %, respectively. The cancer detection rates in the gray zone stratified by patient age of ≤59, 60–64, 65–69, 70–74, 75–79, and ≥80 years were 28.4, 35.0, 26.9, 37.9, 45.7, and 54.8 %, respectively. The significant predictive parameters of cancer detection in the gray zone were age, volume biopsy ratio (VBR: PV divided by number of biopsy cores), PSA density (PSAD), digital rectal examination findings, and transrectal ultrasound findings in univariate analyses. Finally, age, VBR, and PSAD were independent parameters to predict cancer detection in the gray zone. The adverse event profile was acceptable.

Conclusions

Our present study revealed that the cancer detection rate using the NURTG nomogram allocating 6–12 biopsy cores, the number generally used in daily practice, based on age and PV, could provide similar efficacy as previous studies involving more biopsy cores. In older patients the number of biopsy cores could be reduced.

MRI-based prostate volume-adjusted prostate-specific antigen in the diagnosis of prostate cancer

PURPOSE

To determine whether prostate-specific antigen (PSA) levels adjusted by prostate and zonal volumes estimated from magnetic resonance imaging (MRI) improve the diagnosis of prostate cancer (PCa) and differentiation between patients who harbor high-Gleason-sum PCa and those without PCa.

MATERIALS AND METHODS

This retrospective study was Health Insurance Portability and Accountability Act (HIPAA)-compliant and approved by the Institutional Review Board of participating medical institutions. T2 -weighted MR images were acquired for 61 PCa patients and 100 patients with elevated PSA but without PCa. Computer methods were used to segment prostate and zonal structures and to estimate the total prostate and central-gland (CG) volumes, which were then used to calculate CG volume fraction, PSA density, and PSA density adjusted by CG volume. These quantities were used to differentiate patients with and without PCa. Area under the receiver operating characteristic curve (AUC) was used as the figure of merit.

RESULTS

The total prostate and CG volumes, CG volume fraction, and PSA density adjusted by the total prostate and CG volumes were statistically significantly different between patients with PCa and patients without PCa (P ≤ 0.007). AUC values for the total prostate and CG volumes, and PSA density adjusted by CG volume, were 0.68 ± 0.04, 0.68 ± 0.04, and 0.66 ± 0.04, respectively, and were significantly better than that of PSA (P < 0.02), for differentiation of PCa patients from patients without PCa.

CONCLUSION

The total prostate and CG volumes estimated from T2 -weighted MR images and PSA density adjusted by these volumes can improve the effectiveness of PSA for the diagnosis of PCa and differentiation of high-Gleason-sum PCa patients from patients without PCa.

Photodynamic diagnosis of shed prostate cancer cells in voided urine treated with 5-aminolevulinic acid

Background

Past attempts at detecting prostate cancer (PCa) cells in voided urine by traditional cytology have been impeded by undesirably low sensitivities but high specificities. To improve the sensitivities, we evaluate the feasibility and clinical utility of photodynamic diagnosis (PDD) of prostate cancer by using 5-aminolevulinic acid (5-ALA) to examine shed prostate cancer cells in voided urine samples.

Methods

One hundred thirty-eight patients with an abnormal digital rectal exam (DRE) and/or abnormal prostate-specific antigen (PSA) levels were recruited between April 2009 and December 2010. Voided urine specimens were collected before prostate biopsy. Urine specimens were treated with 5-ALA and imaged by fluorescence microscopy and reported as protoporphyrin IX (PPIX) positive (presence of cells demonstrating simultaneous PPIX fluorescence) or PPIX negative (lack of cells demonstrating fluorescence).

Results

Of the 138 patients, PCa was detected on needle biopsy in 81 patients (58.7%); of these 81 patients with PCa, 60 were PPIX-positive (sensitivity: 74.1%). Although 57 patients did not harbor PCa by conventional diagnostic procedures, 17 of these at-risk patients were found to be PPIX-positive (specificity: 70.2%). PPIX–PDD was more sensitive compared with DRE and transrectal ultrasound and more specific compared with PSA and PSA density. The incidence of PPIX–PDD positivity did not increase with increasing total PSA levels, tumor stage or Gleason score.

Conclusions

To our knowledge, this is the first successful demonstration of PPIX in urine sediments treated with 5-ALA used to detect PCa in a noninvasive yet highly sensitive manner. However, further studies are warranted to determine the role of PPIX–PPD for PCa detection.

Prostate volumes derived from MRI and volume-adjusted serum prostate-specific antigen: correlation with Gleason score of prostate cancer

OBJECTIVE

The purpose of this article is to study relationships between MRI-based prostate volume and volume-adjusted serum prostate-specific antigen (PSA) concentration estimates and prostate cancer Gleason score.

MATERIALS AND METHODS

The study included 61 patients with prostate cancer (average age, 63.3 years; range 52-75 years) who underwent MRI before prostatectomy. A semiautomated and MRI-based technique was used to estimate total and central gland prostate volumes, central gland volume fraction (central gland volume divided by total prostate volume), PSA density (PSAD; PSA divided by total prostate volume), and PSAD for the central gland (PSA divided by central gland volume). These MRI-based volume and volume-adjusted PSA estimates were compared with prostatectomy specimen weight and Gleason score by using Pearson (r) or Spearman (ρ) correlation coefficients.

RESULTS

The estimated total prostate volume showed a high correlation with reference standard volume (r = 0.94). Of the 61 patients, eight (13.1%) had a Gleason score of 6, 40 (65.6%) had a Gleason score of 7, seven (11.5%) had a Gleason score of 8, and six (9.8%) had a Gleason score of 9 for prostate cancer. The Gleason score was significantly correlated with central gland volume fraction (ρ = -0.42; p = 0.0007), PSAD (ρ = 0.46; p = 0.0002), and PSAD for the central gland (ρ = 0.55; p = 0.00001).

CONCLUSION

Central gland volume fraction, PSAD, and PSAD for the central gland estimated from MRI examinations show a modest but significant correlation with Gleason score and have the potential to contribute to personalized risk assessment for significant prostate cancer.

Peripheral zone prostate-specific antigen density: an effective parameter for prostate cancer prediction in men receiving 5α-reductase inhibitors

Purpose:

To evaluate the predictive performance of various parameters derived from volume-adjusted prostate-specific antigen (PSA) values in detecting prostate cancer (PCa) and high-grade (Gleason score≥7) PCa according to treatment with a 5α-reductase inhibitor (5ARI).

Methods:

The results of 3,520 prostate biopsies performed between May 2006 and January 2013 were retrospectively assessed. With adjustment for age, 291 patients who had received 5ARI treatment for more than 6 months were identified and matched 1:3 to patients naïve to 5ARIs, resulting in a total of 873 patients. Peripheral zone (PZ) and transition zone (TZ) volumes were determined by transrectal ultrasonography. Receiver-operating characteristic (ROC) curve analysis was used to compare predictive performances of PSA, PSA density (PSAD; PSA/prostate volume), PZPSAD (PSA/PZ volume), and TZPSAD (PSA/TZ volume) for detecting PCa and high-grade PCa for each group.

Results:

The area under the ROC curve (AUC) was higher for PSAD than for PSA in the 5ARI group (0.751 vs. 0.677) and in the 5ARI-naïve group (0.649 vs. 0.582), respectively (P<0.001). In the 5ARI group, the AUC for PZPSAD was even higher than that for PSAD (0.781 vs. 0.751, P=0.038); in the 5ARI-naïve group, however, PZPSAD failed to achieve significant superiority (0.652 vs. 0.649, P=0.321). All volume-adjusted PSA indexes showed higher predictive accuracies for detecting PCa than did PSA in both groups. For detecting high-grade cancer, PZPSAD also revealed the highest predictive value in the 5ARI group, whereas PSA revealed the highest predictive value in the 5ARI-naïve group.

Conclusions:

The diagnostic performance of PSAD in the detection of PCa is superior to that of PSA. For patients receiving 5ARI for more than 6 months, PZPSAD confers additional benefits for detecting both PCa and high-grade PCa.

Utility of the transition zone index for identification of prostate cancer in Chinese men with intermediate PSA levels

PURPOSE

We aimed to investigate the utility of the transition zone index (TZI) for identification of prostate cancer (PCa) in Chinese men with prostate-specific antigen (PSA) levels of 4-10 ng/mL.

METHODS

In this retrospective cohort study, results of transrectal ultrasonography (TRUS)-guided biopsy were assessed in 616 consecutive Chinese men; all subjects had intermediate serum PSA levels and normal digital rectal examination findings. The prostate and transition zone volumes were determined by TRUS. A TZI cutoff value of 0.47 produced the best sensitivity and specificity rates in receiver operating characteristic (ROC) curve analysis and thus was used to classify the study subjects into two subgroups: group 1 with TZI ≤ 0.47 and group 2 with TZI > 0.47. Logistic regression analysis was used to predict outcomes. The variables that were statistically significant in the stepwise logistic regression analysis were assessed using the ROC curve and the area under the curve.

RESULTS

Overall, 166 of the 616 patients (26.9%) had histologically confirmed PCa. A total of 238 (38.6%) patients were classified into group 1, of whom 97 (40.8%) exhibited a positive biopsy; and 378 (61.4%) patients were classified into group 2, of whom 69 (18.3%) exhibited a positive biopsy. The stepwise logistic regression analysis revealed that PSA density (PSAD) exhibited the strongest predictive value in the overall population and in group 1, whereas PSA transition zone density (PSATZD) was the optimal predictor in group 2. The ROC curve analysis revealed that when using the TZI-specific 100% sensitivity cutoffs, 17.7% and 25% of the biopsies were unnecessary and could be avoided in the overall patient population prior to and following the division into groups, respectively (P = 0.002). Using an individually generated 95% sensitivity cutoff of 0.12 ng/mL(2) for PSAD and a cutoff of 0.179 ng/mL(2) for PSATZD for TZI-stratified cohorts of TZI ≤ 0.47 and TZI > 0.47, a more consistent specificity of 44% and 46.9%, respectively, for each cohort was observed.

CONCLUSIONS

The optimal predictor for PCa differs between various TZI levels. The combination of PSAD in patients with TZI ≤ 0.47 and PSATZD in patients with TZI > 0.47 helps to identify potentially unnecessary biopsies compared to the use of a single PSAD for the entire patient population. The discrepancies regarding an optimal predictor in published reports are most likely due to the differing TZI levels among the cases. In this study, we demonstrated improved identification of PCa using TZI-adjusted cutoffs for PSAD and PSATZD.