High Incidence of Prostate Cancer Detected by Saturation Biopsy after Previous Negative Biopsy Series

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1 european urology 50 (2006) available at journal homepage: Prostate Cancer High Incidence of Prostate Cancer Detected by Saturation Biopsy after Previous Negative Biopsy Series Jochen Walz a, *, Markus Graefen c, Felix K.-H. Chun a, Andreas Erbersdobler b, Alexander Haese a, Thomas Steuber a, Thorsten Schlomm a, Hartwig Huland a, Pierre I. Karakiewicz d a Department of Urology, University Medical Centre Eppendorf, Hamburg, Germany b Department of Pathology, University Medical Centre Eppendorf, Hamburg, Germany c Martini-Clinic, Prostate Cancer Centre, Hamburg d Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, Quebec, Canada Article info Article history: Accepted March 13, 2006 Published online ahead of print on March 29, 2006 Keywords: Prostate Biopsy Cancer Saturation Diagnosis Abstract Objectives: We explored the yield of saturation biopsy and developed a nomogram predicting the probability of prostate cancer (PCa) on the basis of saturation biopsy. Materials and methods: Between 2001 and 2004, saturation biopsies (average of 24 cores) were performed in 161 men with persistently elevated prostate specific antigen (PSA) level (median, 9 ng/ml). All had at least two previously negative, eight-core biopsy sessions. PCa predictors on saturation biopsy were integrated within multivariate nomograms. Results: PCa detection was 41% (n = 66 of 161). PSA density and transition zone volume were the most significant predictors of PCa on saturation biopsy. The accuracy of the nomogram with the best performance characteristics was 72%. Conclusions: Saturation biopsy may be indicated in men with a persistent suspicion of PCa. High-risk individuals can be identified accurately with our nomogram. # 2006 European Association of Urology. Published by Elsevier B.V. All rights reserved. * Corresponding author. Department of Urology, University Medical Centre Eppendorf, Martnistr 52, Hamburg, Germany. Tel ; Fax: address: jwalz@uke.uni-hamburg.de (J. Walz). 1. Introduction Elevated serum prostate specific antigen (PSA) represents an indication for prostate biopsy. However, there is controversy regarding the nature and the extent of diagnostic workup in men with a persistently elevated serum PSA after several negative prostate biopsies. The yield of serial prostate biopsies decreases substantially after the first two sessions. For example, Keetch [1] showed that detection rates drop from 34% to 19%, to 8% and finally to 7%, when up to four consecutive biopsies sessions are performed. Djavan [2] showed detection rates that were 22%, 10%, 5% and 4%, respectively /$ see back matter # 2006 European Association of Urology. Published by Elsevier B.V. All rights reserved. doi: /j.eururo

2 european urology 50 (2006) from biopsy session one to four. However, these series relied on six- or eight-core biopsy schemes and may not reflect detection rates associated with more detailed gland sampling, such as multicore or saturation biopsy. The saturation biopsy scheme was introduced initially by Borboroglu et al. [3] and consists of at least 20 biopsy cores. In most saturation biopsy series, between 20% and 34% of men have prostate cancer (PCa) despite two previously negative biopsies [3 8]. However, a substantially lower detection rate also has been reported, and overdetection of clinically insignificant PCa is a concern [8,9]. We report our experience with saturation biopsy in 161 men with at least two previously negative biopsies and persistently elevated serum PSA. We describe detection rates of PCa, as well as the pathologic characteristics of the tumours that were treated with radical prostatectomy (RP). Finally, we assess predictors of PCa on saturation biopsy and provide a nomogram to assist with selection of patients for saturation biopsy. 2. Material and methods From November 2001 to September 2004, 161 consecutive men (mean age, 63.7 years; range: years) underwent saturation biopsy at our institution. Saturation biopsy was performed on an outpatient basis with antibiotic prophylaxis and under intravenous sedation. Our indications for saturation biopsy are given in Table 1; biopsies were performed when the main criterion and one of the secondary criteria were fulfilled. Previous biopsies were performed predominantly at outside institutions and consisted of at least eight cores. The majority of included patients represented referrals. Except for total gland and transition zone (TZ) volumes, as well as the number of previously negative biopsies, we did not have information pertaining to detailed PSA history or previous biopsy schemes. Consequently, this information could not be considered in risk factor analyses. At initial or repeat biopsy, total gland and TZ volumes were measured routinely and were provided on the referral sheet. Saturation biopsies were performed by using a modified Stewart [4] scheme and consisted of at least 18 cores. The number of cores taken at saturation biopsy was adjusted according to the preference of the attending urologist and/or according to prostate size. Biopsy specimens were stored individually in 5% formalin solution. Patients were discharged the same day. A subset of patients (n = 32 of 66) with PCa underwent RP at our institution. Histologic analysis of the RP specimens was performed by using the 3-mm Stanford step-section technique [10]. The 2002 TNM classification and the Gleason system were used [11]. Pathologic tumour volume was calculated as described previously [19]. The location of the tumour was determined according to its predominant location, within either the peripheral zone (PZ) or the TZ. If >70% of the tumour was found within the peripheral zone, the tumour was considered a PZ cancer. Of 161 patients, 115 (71.4%) records were fully evaluable and were included in univariate and multivariate logistic regression models predicting the presence of PCa on saturation biopsy. Of 46 exclusions, three had high-grade prostatic intraepithelial neoplasia (HGPIN) and 10 had atypical small acinar proliferation (ASAP) on saturation biopsy; neither condition represented frank PCa or benign pathology. The remaining 33 exclusions were due to missing variables: percent free PSA (%fpsa, n = 27), PSA density (n = 4), TZ PSA density (n = 6), prostate volume (n = 4) and TZ volume (n = 6). Statistics were performed with S-Plus Professional, version 1 (MathSoft Inc, Seattle, WA, USA). Two-sided tests and a significance level of 0.05 were used in all statistics. Univariate and multivariate logistic regression models were built to predict the probability of cancer on saturation biopsy. Reduced model selection was performed by using a backward step-down selection process, which used the Akaike s information criterion (AIC) as the stopping rule [13,14]. Regression coefficients were used to generate nomograms. The predictive accuracy of each nomogram was quantified with the area under the receiver operating curve (AUC). Two hundred bootstrap resamples were used to reduce over fit bias of the AUC estimates. Performance of individual and combined predictors was explored graphically with calibration plots. 3. Results Saturation biopsy detected 66 (41%) new cancers. Ten (6.2%) patients harbored ASAP, and 3 (1.8%) had HGPIN. In a subgroup of 13 patients with ASAP on previous biopsy, 10 had PCa on saturation biopsy, and 4 PCas were diagnosed in 12 patients with previous HGPIN. Table 2 shows the descriptive statistics for the entire cohort (n = 161), as well as for the subset of Table 1 Indication for saturation biopsy and number of patients with this indication Indication No. of patients 1. Criteria (fulfilled) At least two sets of previous negative multicore (8 cores) biopsy series n = Criteria (one fulfilled) High PSA level (10 ng/ml) n =81 Significant rising PSA level (0.75 ng/ml) n =55 ASAP in previous biopsy n =13 HGPIN in previous biopsy n =12 ASAP = atypical small acinar proliferation; HGPIN = high-grade prostatic intraepithelial neoplasia; PSA = prostate specific antigen.

3 500 european urology 50 (2006) Table 2 Patient characteristics Variables Patients included in nomogram No. of patients (%) All patients No. of patients (%) Preoperative PSA (ng/ml) Mean (median) 13.2 (9.4) 13.5 (10) Range %fpsa Mean (median) 15.8 (14.9) 15.7 (14.7) Range PSA density (ng/ml/cc) Mean (median) 0.2 (0.2) 0.2 (0.2) Range Transition zone density (ng/ml/cc) Mean (median) 0.4 (0.3) 0.4 (0.3) Range Age (yr) Mean (median) 63.4 (64.0) 63.7 (64.0) Range No. of previous biopsies series (%) 2 72 (62.6) 97 (60.2) 3 30 (26.1) 35 (21.7) 4 8 (7) 12 (7.5) 5 4 (3.5) 4 (2.5) 6 1 (0.6) 7 10 (6.2) Prostate volume (cc) Mean (median) 66.4 (58.0) 66.6 (56) Range Transition zone volume (cc) Mean (median) 39.3 (33.0) 40.2 (32) Range No. of cores Mean (median) 24.5 (24.0) 24.2 (24) Range Cancer (%) Absence of cancer 64 (55.7) 82 (50.9) Presence of cancer 51 (44.3) 66 (41.0) 2 (HGPIN) (%) 3 (1.9) 2 (ASAP) (%) 10 (6.2) Total ASAP = atypical small acinar proliferation; fpsa = free prostate specific antigen; HGPIN = high-grade prostatic intraepithelial neoplasia; PSA = prostate specific antigen. evaluable patients with complete records, who were included in regression models (n = 115). Positive cores were mainly in the far lateral zone (79%), the medio-lateral zone (36%) and the TZ (18%). Of all, 37% of patients PCa were found within a single biopsy sector. In 21%, two sectors harbored PCa. In 17%, three sectors were involved, and, finally, PCa was present in four or more sectors in 25%. Three patients (4.5%) had exclusive TZ pathology, without coexistent PZ pathology. Thus it can be Table 3 Characteristics of patients treated with radical prostatectomy or radiation therapy Variables Radical prostatectomy Radiation therapy n = PSA (ng/ml) (median) %fpsa (median) PSA density (ng/ml/cc) Biopsy-Gleason 6 (n [%]) 25 (75%) 11 (73%) Biopsy-Gleason 7 (n [%]) 11 (25%) 4 (27%) No. of previous biopsies (average) No. of positive cores (average) %PCa per core (average) fpsa = free prostate specific antigen; PCa = prostate cancer; PSA = prostate specific antigen. postulated that the omission of TZ biopsies would have resulted in the lack of detection of three of 66 (4.5%) PCas and would have decreased the overall detection rate from 41 to 39%. Thus, TZ cores may not always be necessary. Of 66 patients with PCa, 32 (48%) underwent RP at our institution. Nine patients underwent high-dose brachytherapy, six had external beam radiotherapy and six were treated with hormonal therapy. The remaining 13 patients were undecided or were lost to follow-up. The characteristics of the patients treated with RP and radiotherapy are shown in Table 3. There were no significant clinical differences between these two groups. RP pathologic stages were as follows: pt2a in 16% (n = 5), pt2b in 3% (n =1),pT2c in 56% (n = 18), pt3a in 12.5% (n = 4), pt3b in 9% (n =3)andpT4in3%(n = 1). Twenty-four patients (75%) had organ-confined PCa. Gleason pattern 4 or 5 was identified in 14 (43.7%). Mean tumour volume was 3.0 cc (range, ). Clinically insignificant PCa 13 was defined as absence of high-grade components, tumour volume less than 0.5 cc and pathologic organ confinement. It was identified in five of 32 (15.6%) men. The predominant tumour location was within the PZ in 43.3%, within the TZ in 36.6% and in both locations in 20%. The overall saturation biopsy complication rate was 2.5% and consisted of two acute urinary retentions, one acute prostatitis and one reactive syncope. Two hospital admissions were necessary for intravenous antibiotics. Table 4 shows univariate and multivariate logistic regression models predicting PCa on saturation biopsy. In univariate analyses, %fpsa, TZ density and TZ volume emerged as the most significant variables, followed by PSA density and prostate volume. %fpsa, PSA density and TZ volume were

4 european urology 50 (2006) Table 4 Univariate and multivariate logistic regression model for prediction of prostate cancer on saturation biopsy Variables Univariate model Multivariate OR; p value Predictive accuracy (%) Full model OR; p value Reduced model OR; p value Age 1.030; ; ; Preoperative PSA 1.009; ; %fpsa 0.909; ; ; PSA density ; ; Transition zone density 9.702; ; Prostate volume 0.977; ; ; Transition zone volume 0.965; ; ; No. of previous biopsies 0.876; ; No. of cores 0.936; ; Predictive accuracy (%) fpsa = free prostate specific antigen; OR = odds ratio; PSA = prostate specific antigen. Fig. 1 Model calibration plots. (A) Calibration plot for prediction of cancer on saturation biopsy according to percent of free PSA level, where x-axis represents predicted probability of prostate cancer and y-axis represents observed fraction with evidence of prostate cancer on saturation biopsy according to percent of free PSA. Calibration plot for prediction of prostate cancer on saturation biopsy using the full (B) and reduced (C) prediction model, where x-axis represents the full (B) or reduced (C) model, respectively, and predicted probability of prostate cancer on saturation biopsy; y-axis represents observed fraction with evidence of prostate cancer on biopsy. Perfect prediction would correspond to the 45-degree line. Points estimated below the 45-degree line correspond to nomogram overprediction, whereas points situated above the 45-degree line correspond to nomogram underprediction.

5 502 european urology 50 (2006) correlated negatively with the risk of PCa. Conversely, TZ density and prostate volume were correlated positively. Examination of nomogram axes reveals that the risk of PCa increases in men with high TZ densities and large total gland volume. This implies that high-risk individuals may have large PZ volumes. Of all risk factors, %fpsa demonstrated the highest predictive accuracy (74.3%), followed by TZ density (73.1%) and TZ volume (72.8%). All variables subsequently were included in a multivariate model, which was 72.2% accurate in predicting PCa on saturation biopsy. This full multivariate model was between 0.6% and 2.1% less accurate than one of the three most informative univariate predictors. Assessment of the performance characteristics of univariate predictors revealed important departures from ideal predictions (Fig. 1A). For example the most informative univariate predictor, %fpsa, substantially overestimated the probability of cancer for predicted values between 20% and 40%. Similarly important departures from ideal predictions were noted for all other univariate predictors (data not shown). Assessment of the full-model calibration plot demonstrated minimal departures from ideal predictions (Fig. 1B). In an attempt to reconcile predictive accuracy and model performance, we applied fast backwards variable elimination to the full model, with the intent of identifying the most parsimonious and the most accurate model [13,14]. Removal of PSA, PSA density, TZ density, number of previous biopsy session and number of cores resulted in the most accurate model. This reduced model included patient age, %fpsa, prostate volume and TZ volume. Its 200 bootstrap-corrected predictive accuracy (75.2%) exceeded the 200 bootstrap-corrected accuracy of the full model (72.2%). Moreover, its accuracy exceeded the 200 bootstrap-corrected accuracy of each individual predictor (range, %). The calibration plot of this final model (Fig. 1C) revealed departures from ideal predictions, which exceeded those noted for the full model. For example, for predicted values between 0% and 30%, the model underestimated the observed values. Conversely, predicted values between 30% and 60% overestimated the observed values. The departures from ideal predictions were less pronounced than those noted for the best individual predictor (i.e., %fpsa). 4. Discussion At saturation biopsy (average, 24 cores), 41% of the men were diagnosed with PCa. In the most previous saturation biopsy series (Table 5), the yield of saturation biopsy ranged from 20% to 34% and suggests that saturation biopsy represents an effective method for detecting previously missed PCas [3 8]. Our cancer detection rate exceeds those reported in all previous series, in which the highest (34%) rate was reported by Stewart et al. [4] with 23 biopsy cores. Except for one series [9], previous series reported median PSA values from 8.6 to 9.4 ng/ml, which are comparable to the 9.4 ng/ml in the current series (Table 4). Unfortunately, the referral pattern of our cohort makes detailed assessment of other patient characteristics impossible. Detection of insignificant PCa is a risk of any initial or repeat biopsy. It may be as high as 33.5% with 12 cores versus 22.7% with sextant biopsy [20]. We report the presence of insignificant PCa, defined as absence of high-grade components, organ confinement and tumour volume less than 0.5 cc [12] in only 5 (15.6%) of 32 men treated with RP. Unfortunately, the assessment of clinical significance may be performed only for those treated with RP and cannot be assessed for the remaining 34 patients. However, the clinical parameters did not differ significantly between those treated with RP and those treated with radiation (Table 3). In addition to reporting the yield of saturation biopsy, our goal was to develop a model capable of accurately predicting the probability of PCa on saturation biopsy. Our model is 72% accurate and relies on variables that can be readily obtained prior to saturation biopsy. Table 5 Comparison of saturation biopsy studies Author Year Sample size PCa detection rate (%) No. of cores No. of previous biopsies Average PSA level (ng/ml) Borboroglu et al (mean) Stewart et al (median) Fleshner et al (median) Rabets et al (mean) Patel et al (mean) Current series (median) PCa = prostate cancer; PSA = protein specific antigen.

6 european urology 50 (2006) Several interesting observations may be derived from close examination of the axes defining the nomogram risk variables. PSA and %fpsa represent the most commonly used predictors of biopsy outcome. PSA is of only limited value in our cohort. Most commonly encountered PSA values are located on the low side of the risk point scale and contribute to few risk points. Thus, in most patients, PSA exerted a minimal effect on the probability of a positive saturation biopsy. %fpsa contributes more importantly to risk points than PSA. Assessment of other nomogram axes shows that other variables, Fig. 2 Nomograms. To obtain nomogram predicted probability of saturation biopsy (for full and reduced model), locate the patient s variable values at each axis. Draw a vertical line to the Point axis to determine how many points are attributed for each variable value. Sum the points for all variables. Locate the sum on the Total Points line. Draw a vertical line towards the Prob. of CA axis to determine the patient cancer probability. PSA = prostate specific antigen; %fpsa = percent free PSA; PSAD = PSA density; TZ-D = transition zone density; Tot.Vol = total prostatic volume; TZVol = transition zone volume; Prev. bx = number of previous biopsies; # cores = planned number of cores; Prob. of PCa = probability of prostate cancer.

7 504 european urology 50 (2006) such as total gland and TZ volume as well as their PSA-to-volume ratios, represent highly informative predictors of saturation biopsy outcome. PSA density and TZ volume represent the most important and informative predictors in this population. The capacity of these variables to predict the risk may be explained by the relative inability of previous biopsy schemes to detect cancer in large glands. Our results indicate that, when saturation biopsy is contemplated after one or several negative biopsies, the risk of finding cancer is highest in men with large prostates and small TZ volumes. These men have large PZ volumes. Besides important clinical observations, our regression models provided important insight about the combined effect of saturation biopsy risk factors. We have shown that, when variables are used alone, they might be associated with high predictive accuracy. For example, the accuracy rates of %fpsa, TZ PSA density and TZ volume in predicting cancer on saturation biopsy were74.3%, 73.1% and 72.8%, respectively. However, their predictive accuracy was associated with suboptimal performance characteristics, manifested by important differences between observed and predicted probabilities of PCa at saturation biopsy. These differences rendered those seemingly accurate predictors undesirable, when risk variables were used in isolation. Our data demonstrate that the combined contribution of all predictors within the full model (Fig. 2A), substantially improved the performance characteristics, as evidenced by calibration plots (Fig. 1A C). Despite its better ability to predict the observed rate of PCa at saturation biopsy, the full model was less accurate (72.2%) than selected highly significant and informative factors, such as %fpsa (74.3%). Assessment of the calibration plot in Fig. 1 demonstrates that the curve depicting %fpsa is substantially farther away from the ideal 45-degree prediction line than the performance curve of the full model. Better predictive accuracy of %fpsa relative to the whole model can be explained by areas of underprediction (curve above ideal 45-degree prediction line) and areas of overprediction (curve below ideal 45-degree prediction line), which cancel out each other in a more symmetric fashion than seen for the whole model. Therefore, it appears that, in the case of saturation biopsy, a more complete model (Fig. 2A) is superior to a model with fewer variables (Fig. 2B), in which higher predictive accuracy is traded for worse performance characteristics. In consequence, we recommend the use of the full model. The compromise between discriminant and calibration properties of the full model still yielded highly accurate predictions, relative to accuracy reported for two previously validated repeat biopsy nomograms [15,16]. The first revealed predictive accuracy of 70%, and the second was 71% accurate. Therefore, 72.2% accuracy of the current full model is superior to those two previous studies and confirms that the outcome of repeat biopsy, either saturation or regular, may be predicted in a highly accurate fashion. The predictive accuracy of repeat biopsy models (range, 70 72%) is on average inferior to models predicting the outcome of initial biopsy, which range from 69% to 0.91% [15 17]. It appears therefore, that a repeat biopsy outcome is more difficult to predict than the outcome of an initial biopsy. By extrapolation, it might be postulated that prediction of saturation biopsy, which represents the third or fourth biopsy, is even more difficult. In this light, predictive accuracy of 72.2% appears acceptable. Moreover, recent reports suggest that nomograms are more accurate than clinical estimates [18]. Finally, a model with nonperfect accuracy may be better than no model at all. There are a number of limitations in our study. For example, we were unable to assess whether presence of HGPIN and/or ASAP represents a risk factor for PCa on saturation biopsy. Our sample was limited by a small number of patients with information on these two conditions prior to saturation biopsy because of the referral pattern of the population. Other nomogram limitations include its suboptimal accuracy, a flaw shared with virtually all prognostic models [13,14]. 5. Conclusion Saturation biopsy represents a necessary and a safe diagnostic procedure for men with persistent suspicion of PCa after several negative biopsies. The detection rate of PCa is high (41%) and the likelihood of clinically insignificant cancers is not increased (15.6%) relative to initial or repeat biopsy. Finally, our nomogram can accurately (72%) identify those at risk of having PCa at saturation biopsy. Conflicts of interest Pierre I. Karakiewicz is partially supported by the Fonds de la Recherche en Santé du Québec, the CHUM Foundation, the Department of Surgery and Les Urologues Associés du CHUM.

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