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european urology 54 (2008) 728–739
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Review – Prostate Cancer
Does the Extent of Carcinoma in Prostatic Biopsies Predict
Prostate-Specific Antigen Recurrence? A Systematic Review
Patricia Harnden a,*, Michael D. Shelley b, Brian Naylor c, Bernadette Coles b,
Malcolm D. Mason d
a
Cancer Research UK Clinical Centre, St James’s University Hospital, Beckett Street, Leeds LS9 7TF, United Kingdom
Cochrane Urological Cancers Unit, Velindre NHS Trust, Cardiff, CF14 2TL, United Kingdom
c
Histopathology, Airedale General Hospital, Keighley, United Kingdom
d
Cardiff University, Cardiff, CF10 1AA, United Kingdom
b
Article info
Abstract
Article history:
Accepted June 16, 2008
Published online ahead of
print on June 26, 2008
Context: The biologic potential of prostate cancer (pCA) is variable, and the ability to
identify tumours that might cause morbidity and mortality is limited.
Objective: This systematic review sought to establish whether measurement of
tumour extent in biopsies provides additional prognostic information on the risk of
disease progression.
Evidence acquisition: A comprehensive 31-step search strategy was run in MEDLINE,
EMBASE, and the Web of Knowledge (January 1990–July 2007) and supplemented by the
hand-searching of references in retrieved articles and relevant journals to identify
publications related to the measurement of the length of cancer in biopsies and
biochemical or clinical recurrence or pCA death. Thirteen papers reporting on at least
100 patients were identified and included patients treated by watchful waiting or
hormonal therapy (n = 1), radical prostatectomy (n = 11), or radiotherapy (n = 1). Only
two studies reported on clinical progression or mortality. Sources of bias included
patient selection and missing data resulting from the retrospective nature of the
studies. Confounding factors included differences in biopsy strategies and measurement methods.
Evidence synthesis: The percentage of cancer in biopsies (overall percentage or the
greatest percentage in the most involved core) was an independent predictor of
prostate-specific antigen (PSA) and clinical outcomes regardless of the form of treatment and was generally superior to simply counting the number of positive cores. The
marked variability in study design, conduct, and reporting precluded meta-analysis of
the data and precise risk estimation.
Conclusions: Tumour quantitation is a promising prognostic tool in the assessment of
risk of pCA progression. However, well-designed, population-based studies, controlling for confounding factors, are required to provide more accurate risk estimation and
develop management strategies. This review highlights the need for new approaches
in the assessment of pathologic prognostic factors to reach the level of evidence
achieved in other areas of medical practice.
# 2008 Published by Elsevier B.V. on behalf of European Association of Urology.
Keywords:
Prostate cancer
Biopsy prognostic factors
Tumour extent
* Corresponding author. Bexley Wing, St James’s University Hospital, Leeds LS9 7TF,
United Kingdom. Tel. +44 (0) 113 2067531; Fax: +44 (0) 113 2067610.
E-mail address: [email protected] (P. Harnden).
0302-2838/$ – see back matter # 2008 Published by Elsevier B.V. on behalf of European Association of Urology.
doi:10.1016/j.eururo.2008.06.068
european urology 54 (2008) 728–739
1.
Introduction
One of the major problems facing urologic oncologists and urologists today is how and when to treat
asymptomatic men with prostate cancer (pCA).
Offering radical therapy is associated with potential
morbidity resulting from urinary, bowel, and sexual
dysfunction [1–4]. Active surveillance, whereby
treatment with curative intent is only instituted if
there are signs of progression, is seen as a viable
option to avoid ‘‘over-treatment’’ [5] and is most
appropriate for patients at low risk of progressive
disease. Assessing the magnitude of this risk in
individual patients remains a challenge, as current
nomograms [6–8] and risk tables [9,10], incorporating pretreatment prostate-specific antigen (PSA)
levels, Gleason score, and stage of disease provide
relatively wide estimates. More detailed biopsy
information, particularly the percentage of positive
cores, is used in clinical practice to refine risk
assessment, particularly for low-risk patients [11–
18], and these predictive tools have been validated
[19,20]. However, methods of estimating the linear
length of cancer have varied among studies, with no
consistency in approach even among dedicated
uropathologists [21], and these measurements are
used with caution by urologists [22].
The aim of this systematic review was to identify
and summarize all studies relating linear extent of
cancer on biopsy with recurrence, progression, or
death from pCA and to determine which, if any, of
these pathologic variables should be reported in
routine practice to stratify patients into groups at
different risk of progression. It also sought to
establish whether these more complex measurement methods were superior to the simple measure
of counting the number of positive cores.
2.
729
Methods
A comprehensive 31-step search strategy was developed
composed of a thesaurus and text-word terms describing
pCA, outcomes, and pathology to identify studies reporting the
pathology of pCA [23]. There were no language restrictions.
The initiation of the search date (1990) coincided with the
establishment of transrectal ultrasound (TRUS)-guided biopsy
procedures and was updated to July 2007. The resulting
bibliographic database (Fig. 1) was then searched using biopsy
as a search term. Two authors scanned titles and abstracts for
relevant studies. Only fully published studies were retrieved
for further assessment. The electronic search was supplemented by hand-searching bibliographies of the retrieved
papers and relevant recent cancer journals.
A sample size of 100 patients was considered a minimum
for meaningful statistical analyses. Papers addressing the
specific issue of microfocal carcinoma were reviewed sepa-
Fig. 1 – Flow diagram for evidence search.
rately [24]. The selection process resulted in 13 papers
examining the relationship of the absolute length (in millimetres) [25–27] or the relative length (percent) [28–37] of cancer
in biopsy material with pCA mortality, clinical recurrence, or
PSA recurrence. Two groups reported initial [29,34] and
extended [30,33] follow-up data on overlapping groups of
patients. The early reports are included, as they took a
different analytic approach and contain comparative data on
the significance of the number of positive cores. All were
retrospective studies except one in which some of the data
were collected prospectively [33].
Data were extracted and verified by at least two authors
and consisted of study design and location, patient characteristics, details of biopsy pathology and method, types of
interventions, statistical analysis, and clinical and/or PSA
outcomes. Additional information concerning the measurement method was requested from the authors when required
and was received in all but one case [35].
3.
Results
3.1.
Measurements expressed in millimetres
The study characteristics and findings of the three
relevant papers [25–27] are summarized in Tables 1
730
european urology 54 (2008) 728–739
Table 1 – Characteristics of studies examining the length of cancer (in millimetres) in prostatic biopsies
Study
Institution (dates)
n
Stage
Age (years)
178
T1 = 92
T2 = 70
T3 = 16
Mean: 64.9
(49–80)
Median: 7.9
(1.2–136.0)
<6 = 4%
6 = 54%
>6 = 42%
6 = 44%
7 = 35%
8–10 = 21%
Median: 41.5
(2.0–82.0)
King
et al,
2005 [26]
Kitasato and
Kurashiki
Hospitals.
1992–1999
Stanford University and
Palo Alto Veterans
Hospital 1992–2001
124
ND
Mean: 60.6
(39.9–73.1)
6 = 67%
8 = 19%
7–13 = 14%
6 = 58%
7 = 25%
8–10 = 17%
ND
Noguchi
et al,
2003 [27]
Stanford
University
1998–1997
222
T1c
Mean: 62.5
(43–79)
4 = 9%
4.1–10 = 65%
10.1–20 = 17%
>20 = 10%
4 = 2%
4.1–10 = 72%
10.1–20 = 25%
>20 = 1%
Median: 6 (6–13)
ND
Mean: 52.4
(6–152)
Egawa
et al,
2001 [25]
PSA ng/ml
Biopsy
number
Gleason
score
Follow-up
(months)
ND, no data; PSA, prostate-specific antigen.
and 2. All patients were treated by radical prostatectomy.
3.4.
Relative value of the number of positive cores versus
cancer length (in millimetres)
3.2.
One study found that only the number of positive
cores was significantly associated with biochemical
recurrence [27] and another that it was also
predictive [25]. Both of these studies dichotomized
this variable, whereas the third, reporting negative
results, did not [26]. The values were widely spread
in this study (range: 1–11), and there were relatively
few cases with three (19%) or four or more (15%)
positive cores [26].
Cumulative cancer length (sum of cancer in all cores)
In the report by King et al [26], values were spread
across a wide range (1–44 mm), with cumulative
lengths of cancer >9 mm in 43% of patients, and only
this measure was significantly associated with
biochemical recurrence (Table 2). Noguchi et al
reported negative results [27]; 36% of patients had a
cumulative cancer length of 3 mm, 30% had values
of 3.1–6 mm, 13% had values of 6.1–9 mm, and only
21% had >9 mm of cancer. In the first study,
cumulative length was analyzed as a continuous
variable, but in the negative study, the mean value
(6 mm) was used as the cut-off point. The skewed
distribution and the choice of cut-off points may
explain the negative findings.
3.3.
Maximum length of cancer in any one core
Maximum length of cancer in any one core was
predictive of biochemical failure in the study by
Egawa et al [25] but not that of King et al [26]. In this
case, the use of a cut-off point resulted in a
significant result [25], which was not reproduced
when data were analyzed as continuous variables
[26]. When patients were then stratified into three
risk groups depending on clinical stage, presenting
serum PSA levels, and biopsy Gleason score [25]—
which were further subdivided (one positive biopsy
and <3 mm of cancer, one positive biopsy or <3 mm,
two or more positive biopsies, and 3 mm of
cancer)—there was no improvement in risk stratification for PSA recurrence. However, the maximum
number of patients in any of these subgroups was
39, and the statistical power may have been
insufficient to detect a significant difference.
3.5.
Cancer lengths expressed as a percentage
The key characteristics of these 10 studies [28–37]
are summarised in Table 3. The different methodologies are detailed in Table 4, and the findings are
presented in Table 5.
3.6.
Total percentage of cancer
Cuzick et al reported on pCA mortality in patients
treated conservatively after a diagnosis made either
in transurethral resections or biopsy cores [28].
There were no data specific to measurement in
biopsy material, but overall, the total percentage of
cancer was more predictive for pCA death at 10 yr
than clinical stage in multivariate models, although
the major factor predictive of cancer mortality was
Gleason grade followed by PSA levels. In the
remaining papers, all patients underwent radical
prostatectomy.
Using the Shared Equal Access Regional Centre
Hospital (SEARCH) database, Freedland and coworkers [29] reported that the total percentage of
cancer was the strongest predictor of PSA recurrence when analyzed as a continuous value but was
similar to other predictors as a categorical variable
Number
positive:
2.54 (1.04–6.20),
p = 0.041
3.7.
Relative value of the percentage of positive cores
versus total percentage of cancer
PSA, prostate-specific antigen; CI, confidence interval; mm, millimetres.
Multivariate
Cox proportional
hazards model
0.07 ng/ml, increasing in
subsequent samples/39 (18%)
Noguchi et al,
2003 [27]
Multivariate logistic
regression analysis
Detectable postoperative
PSA >0.05 ng/ml/28 (24%)
1. Total cancer length
on biopsy (mm)
2. Maximum tumour
length (mm)
3. Number of positive cores
4. Percent of positive cores
1. Total cancer length
on biopsy (<6, 6 mm)
2. Number of positive
cores (<2, 2)
King et al,
2005 [26]
731
(Table 5). When added to baseline PSA levels and
Gleason score data to predict PSA recurrence, the area
under the receiver operating characteristic curve
(AUC) increased significantly from 0.653 to 0.722
( p = 0.020). The optimized cut-off points (Table 4) also
significantly improved risk stratification for PSA
recurrence within risk groups defined by serum
PSA and biopsy Gleason score (low risk: PSA
10 ng/ml and biopsy Gleason score <7; intermediate
risk: PSA 10–20 ng/ml and biopsy Gleason score 7;
high risk: PSA >20 ng/ml or biopsy Gleason score >7).
These researchers then developed a preoperative
model for the 2-yr risk of PSA recurrence following
radical prostatectomy [30], using cut-off values for
the percent of cancer on biopsy (<20%, 20–40%, and
>40%), serum PSA (<10, 10–20, >20 ng/ml), and biopsy
Gleason score (<7, 3 + 4, 4 + 3). For a patient with a
Gleason sum score of 3 + 4 and a preoperative serum
PSA level of <10 ng/ml, the risk of biochemical failure
increased from 13% (95% CI, 10–16) if <20% of cancer
was present on biopsy to 30% (95% CI, 13–47) if >40%
of the tissue was involved.
Sebo et al [37] and Ravery et al [36] demonstrated
that the total percentage of cancer was predictive
of PSA recurrence in univariate analysis [37] or
by log-rank test [36]. Except for PSA density, all
other preoperative parameters (age, clinical stage,
PSA, Gleason score, the percentage of positive cores,
and whether cancer was unilateral or bilateral)
were not significantly associated with biologic
progression.
Neither factor significant
in univariate analysis,
number positive significant
on multivariate analysis
No details
No details
Both measures in conjunction
with PSA, stage, and Gleason
score significantly improved
prediction of biochemical
failure ( p = 0.009)
Only total cancer length
significant ( p = 0.05)
Likelihood ratio test
2 consecutive rises
0.1 ng/ml, 1 mo
apart/58 (33%)
1. Maximum length of
cancer (<3 or 3 mm)
2. Number of positive
cores (1 or >1)
Egawa et al,
2001 [25]
Statistical
analyses
Definition of PSA Recurrence/
number (%) of recurrences
Measures
reported
Study
Table 2 – Summary of findings of papers investigating the length (in millimetres) of cancer on biopsy
Correlation with
PSA recurrence
Hazard ratio
(95% CI), p value
european urology 54 (2008) 728–739
One group compared the predictive value of the total
percentage of biopsy tissue containing cancer with
the percentage of positive cores, analyzing both
variables as continuous and categorical variables
[29]. There was a significant correlation between the
two measures (Spearman rank: r = 0.778, p < 0.001),
and the difference in the area under the receiver
operating characteristic curve (AUC) was slight
(percentage of cancer: 0.697; percentage of positive
cores: 0.644) although statistically significant in
favour of the percentage of cancer (no confidence
intervals given, p = 0.022). The percentage of positive
cores was also not independently predictive in
another study [36] and was not tested in another
multivariate model [37].
3.8.
Greatest percentage of cancer
An initial study by Nelson and colleagues [34] found
that the preoperative variables influencing recur-
732
Table 3 – Characteristics of studies examining the percentage of cancer in prostatic biopsies
Study
Institution (dates)
Stage
1656, but ND for
biopsy
subgroup
355
ND for biopsy
subgroup
Median:
70.1 (44–76)
T1 = 165
T2 = 184
T3 = 1
T1 = 224
T2 = 228
T3 = 1
All T1c
Mean: 61.8
4 = 23%
4–10 = 21%
>10 = 56%
Median: 7.9
Mean: 61.8
Median: 7.8
ND
Cuzick
et al, 2006 [28]
Six UK registries,
1990–1996
Freedland
et al, 2003 [29]
SEARCH database,
1990–2002
Freedland
et al, 2004 [30]
SEARCH
database, 1990–2002
459
Gretzer
et al, 2002 [31]
John Hopkins
Hospital,
Baltimore, MD,
USA, 1988–2000
Brigham & Women’s
Hospital, Boston, MA,
USA, 1989–2000
University of Michigan
Hospitals, Ann Arbor,
MI, USA, 1994–1998
University of Michigan
Hospitals, Ann Arbor, MI,
USA, 1994–2002
Memorial
Sloan Kettering,
New York, NY,
USA, 1992–1999
Bichat Hospital, Paris,
France, 1988–1998
Mayo Clinic, Rochester,
MN, USA, 1995–1998
243 for
percentage
of cancer
Linson
et al, 2002 [32]
Nelson
et al, 2002 [34]
Nelson
et al, 2003 [33]
Potters
et al, 2002 [35]
Ravery
et al, 2000 [36]
Sebo
et al, 2002 [37]
184
588
T1c = 125
T2a = 31
T2b = 28
T1 = 413
T2 = 175
Age
(years)
PSA ng/ml
Biopsy
number
Gleason
score
Primary
treatment
6 = 32%
7 = 21%
8–10 = 18%
Median: 6
Hormones/
WW (670/1663)
Median:
117 (88–180)
RP
Median: 34
Median:
8 (4–19)
Mean: 6
RP
Median: 34
ND for
relevant
subgroup
ND
ND for
relevant
subgroup
RP
ND
ND
>10– <20
6
<6–7
RP
Median:
33 (6–120)
<55 = 28%
55–66 = 50%
>66 = 22%
Mean:
60.2 7.6
10 = 82%
>10 = 18%
ND
6–9
RP
Mean: 16 (4–48)
Mean:
7.7 7.2
ND
2–10
RP
Median:
37 (2–99)
ND
Median:
8 (4–18)
Follow-up
(months)
1414
T1 = 898
>T1 = 502
1073
T1c = 641
T2a = 377
T2b = 55
Median: 69.8
Median:
7.6 (0.17–112)
6
2–10
Brachytherapy
Median:
34.7 (6–91)
143
T1–T2
Median: 63
ND
Median: 7
RP
454
97.7%
confined
clinical stage
Median: 65
Median:
10.5 (1.4–61.8)
Median:
6.3 (0.6–112.0)
Mean: 6.6 (2–14)
Median:
6 (4–9)
RP
Median:
40 (6–112)
Mean: 3.4
(17 d–5.8 yr)
PSA, prostate-specific antigen; ND, no data; RP, radical prostatectomy; WW, watchful waiting.
european urology 54 (2008) 728–739
n
733
european urology 54 (2008) 728–739
Table 4 – Methods for the measurement of the percentage of cancer
Study
Terminology
Measurement
method
Intervening
benign tissue
excluded
Cuzick
et al, 2006 [28]
Percent of cancer in biopsy
Sum of cancer lengths in
each core/sum of each core length
Yes
Freedland
et al, 2003
and 2004 [29,30]
Gretzer
et al, 2002 [31]
Percent cancer in biopsy
Sum of cancer lengths in
each core/sum of each core length
Yes
Maximal percentage of
biopsy core with cancer
No
Linson
et al, 2002 [32]
Percentage of core
length with cancer
Nelson
et al, 2002 and
2003 [33,34]
Greatest percentage
of cancer
Estimate of the linear amount
of cancer in the single
most involved core
Sum of lengths of cancer in
each core/number of cores
(assumed that all are
of the same length)
Estimate to the nearest 5%
of the linear percent of cancer
in the single-most involved core
Potters
et al, 2002 [35]
1. Mean percent cancer
in an involved core
2. Maximum percent cancer
in any core
3. Percent cancer at apex,
midgland, or base
4. Maximum percent of cancer
at apex, midgland, or base
Length of core invaded by cancer
ND
ND
Sum of cancer lengths in
each core/sum of
each core length
Visual estimate in 5%
increments of the amount
of tumour relative to the
total length of cores
Yes, yes, also
periprostatic
tissues
Yes, yes
Ravery
et al, 2000 [36]
Sebo
et al, 2002 [37]
Percent surface area
involved with cancer
Yes
Yes
Cut-off
points
6%, >6–20%,
>20–40%, >40–75%,
>75–100%
Continuous variable
and <20%,
20–40%, >40%
Continuous
variable and
<50% vs >50%
25% vs >25%
2002: Stratification
by 10% intervals
and <10%, 10–59%,
60%
2003: 60–100% vs 0–59%
Continuous variables
<15%, 15–29%,
30–49%, 50–100%
Continuous variables
ND, no data.
rence-free survival after radical prostatectomy
were serum PSA ( p < 0.01) as a continuous variable,
biopsy Gleason score (<7, 7, >7, p = 0.04), and the
greatest percentage of cancer ( p < 0.01). Using these
three parameters, a multivariate model was highly
predictive of recurrence-free survival ( p = 0.0001).
For a patient with Gleason sum score of 7 and a
preoperative serum PSA level of 10 ng/ml, the 2-yr
risk of biochemical failure increased from 5% (95% CI,
1–10), if the greatest percentage was <10%, to 16%
(95% CI, 10–21), if it was 60%. The extended study
[33] also demonstrated that the greatest percentage
Table 5 – Summary of findings of papers investigating the percentage of cancer on biopsy
Study
Cuzick et al,
2006 [28]
Freedland et al,
2003 [29]
Reported
outcomes;
number (%)
Death from pCA;
ND (24%)
Biochemical failure:
PSA >2 ng/ml
or two values of
2 ng/ml; 89 (25%)
Statistical
analyses
Multivariate
PHM
Multivariate CPHM
Correlation with
reported
outcomes
Percent cancer significant in
univariate and multivariate analysis
Percent cancer as a continuous
variable stronger independent
predictor of PSA recurrence
than biopsy Gleason score and
serum PSA. Percent of positive
cores not independent predictor.
Hazard
ratio (95% CI),
p value
No details for
biopsy subgroup
8.25 (3.06–22.22),
p < 0.001
compared to:
Gleason score
1.34 (1.11–1.62)
p = 0.003
PSA 1.03
(1.01–1.05),
p = 0.003
734
european urology 54 (2008) 728–739
Table 5 (Continued )
Study
Reported
outcomes;
number (%)
Statistical
analyses
Correlation with
reported
outcomes
Freedland et al,
2004 [30]
Biochemical failure:
PSA >2 ng/ml
or 2 values of 2 ng/
ml; 118 (26%)
Logistic regression
and CPHM
Using cut-offs, percent of cancer
significant independent predictor
of PSA failure similar to biopsy
Gleason score and serum PSA
Gretzer et al,
2002 [31]
Biochemical failure:
0.2 ng/ml PSA; ND
Recursive partitioning,
Kaplan-Meier, and
multivariate Cox
regression
Linson et al,
2002 [32]
Biochemical failure:
2 consecutive levels
>1.0 ng/ml; ND
Univariate and
multivariate Cox
regression
Nelson et al,
2002 [34]
Biochemical failure:
PSA >0.2 ng/ml; 71 (12%)
Cox regression
Nelson et al,
2003 [33]
Biochemical failure:
PSA >0.2 ng/ml; 183 (13%)
Multivariate CPHM
Maximum percentage of cancer
(<50% or 50%) independent
predictor of PSA failure; PSA,
Gleason score, and number of
positive cores not significant
Both cancer volume measures
significant predictors of PSA
recurrence in univariate analysis;
only percent of positive cores
significant in multivariate
analysis ( p = 0.03)
PSA, Gleason, and greatest percent
cancer highly predictive of PSA
recurrence ( p = 0.0001), number of
positive cores not significant
GPC significant predictor of PSA
recurrence-free survival with
natural log PSA and biopsy
Gleason score; number of positive
cores not significant
GPC significant predictor of clinical
recurrence or metastasis independent
of Gleason score ( p = 0.0065)
Clinical recurrence or
metastasis; 26 (1.8%)
Potters et al, 2002 [35]
Biochemical failure:
3 rises, not necessarily
consecutive; 88 (8%)
Principal components
analysis and Somers
D rank correlation
Ravery et al, 2000 [36]
Biochemical failure:
PSA >0.5 ng/ml
before September 1991:
then PSA >0.1 ng/ml; ND
Biochemical failure:
PSA 0.4 ng/ml or local
recurrence or systemic
progression; 73 (16%)
Kaplan Meier CPHM
Sebo et al, 2002 [37]
Kaplan-Meier
Significant in univariate analysis:
mean percent cancer in an involved
core, percent cancer at apex and
midgland, maximum percent at base;
percent positive cores not significant
Percent core length invaded by cancer
and PSA density significantly predict
biological progression, percent
positive cores not significant
Both percent positive cores
(RR 1.02, 95% CI, 1.01–1.03)
and surface area (RR 1.03,
95% CI, 1.02–1.04) significant
predictors of cancer progression
on univariate analysis ( p < 0.001)
Hazard
ratio (95% CI),
p value
1.64 (1.25–2.15)
compared with:
Gleason score
1.62 (1.28–2.05)
PSA 1.51
(1.17–1.97)
(all p 0.001)
1.02 (1.01–1.03),
p = 0.008
ND
ND
1.449
(1.025–2.049),
p = 0.0357
compared to:
Gleason
score 7–2.29
(1.60–3.27), score
8–3.17 (1.79–5.61)
PSA 2.67
(2.15–3.32),
all p < 0.0001
ND
Log-rank test 30.1
Compared with
PSA density 35.5,
both p = 0.0001
Not included in
multivariate
analysis
CI, confidence interval; pCA, prostate cancer; PHM, Proportional Hazards Model; PSA, prostate-specific antigen; ND, no data; CPHM, Cox
Proportional Hazard Model; GPC, greatest percentage of cancer; RR, relative risk.
was significantly associated with clinical recurrence
or metastases independently of Gleason score.
As reported by Gretzer and associates [31], the
greatest percentage of cancer was also predictive of
recurrence in a subgroup of patients with T1c pCA
after radical prostatectomy. Using a PSA cut-off of
10 ng/ml and a biopsy Gleason sum of 7, two groups
were defined with biochemical recurrence-free
probabilities at 10 yr of 96% (95% CI, 94–98%, group
T1cI) and 73% (95% CI, 62–81%, group T1cII;
p = 0.0001). The first group was not investigated
further because biopsy data were unlikely to
improve prognostication; in the second group, the
5- and 10-yr PSA recurrence-free probability was
european urology 54 (2008) 728–739
significantly higher (90% and 85%, respectively) if
there was <50% involvement of the length of any
one core compared to >50% (75% and 56%, p = 0.03).
The greatest percentage of cancer was amongst
the 23 biopsy variables investigated in a paper by
Potter et al [35] reporting on patients treated by
brachytherapy alone (78% of patients) or with
additional external beam radiation therapy (22% of
patients). When taken as the maximum percentage
in any core, it was not significant in univariate
analysis; but if restricted to biopsies taken from the
base, it was significant.
3.9.
Relative value of the number of positive cores versus
the greatest percentage of cancer
One group found no association between the
number of positive biopsies and PSA recurrence
using cut-off points of one (57% of patients), two
(29% of patients), or three or more positive cores
(14% of patients) [34] or using cut-off points of one
versus two or more positive cores (patient distribution not given) [33]. The number of positive cores
was not significant in another study, although it was
recognized that the fragmented nature of some of
the cores and the lack of record of the total number
of cores taken limited interpretation [31]. Finally, no
significance for the percentage of positive cores was
found in patients treated by radiotherapy [35]. Using
a model including PSA, Gleason score, and clinical
stage, the Somers D rank coefficient was 0.32
(0 represents no discriminating ability, 1 is perfect
discrimination). Adding the percentage of positive
cores to this model increased the coefficient to 0.34.
However, the best predictive model using principal
component analysis was where all 23 pathologic
variables, ranked by significance into component
groups, were included and gave a coefficient of 0.39.
3.10.
Mean percentage of cancer and its significance
relative to the percentage of positive cores
Linson and colleagues [32] specifically examined
whether the mean percentage of core length with
cancer provided significantly more information
than the percentage of positive cores in predicting
postoperative PSA outcome and found that only the
percentage of positive cores was independently
predictive. This study was restricted to men with
intermediate-risk pCA defined as clinical stage 2b
(1992 American Joint Consensus Committee criteria), biopsy Gleason score 7, or PSA 10–20 ng/ml,
and 80% of patients had 50% positive cores.
The radiotherapy study [35] did not clearly define
‘‘mean percentage of cancer in an involved core,’’
735
and no clarification could be obtained. This measure
could be interpreted as representing the total
amount of cancer divided by the number of positive
cores rather than by the total number of cores, as in
the other study [32]. It was a significant predictor of
PSA outcome ( p = 0.008), whereas the percentage of
positive cores was not.
3.11.
Limitations on interpretation
All studies were biased because of the retrospective
nature of data collection and the availability of
quantitative biopsy data, in particular. There were
variations in the spread of clinical stages (Tables 1
and 3), the definition of PSA recurrence, the number
of recorded recurrences (Table 4), and length of
follow-up, which was sometimes short (Tables 1 and
3). Furthermore, biopsy strategies and the number of
cores obtained varied between and within studies
(Tables 1 and 3), potentially affecting the amount of
carcinoma sampled in a given patient. There was
little information on laboratory handling of the
biopsies, but separate embedding of cores was not
mentioned, and fragmentation of multiple cores
embedded together would make the assessment of
the number of cores involved or the maximum
amount of cancer in any core less reliable. Only
three groups explicitly set quality criteria. Patients
were excluded if <4 [29,30] or 6 [27] biopsies were
taken, or a core was not measured if it measured
<10 mm [36].
Methods of measurement also varied among
studies (Tables 2 and 4), further limiting comparability among studies and the possibility of performing a meta-analysis. Two groups estimated the
greatest percentage of cancer in the most involved
core, but one excluded intervening benign tissue
[33,34], whereas the other group included it [31].
Measurement methods were not tested for reproducibility, yet data from the External Quality Assurance Scheme (EQA) in Prostatic Pathology Reporting
found that individual pathologists’ measurements
were spread along a range of at least 10 mm in as
much as 63% of cases [38]. Statistical methods
varied, including the determination of cut-off
points, which can distort the significance of results.
4.
Discussion
This systematic review set out to determine whether
measures of tumour extent could significantly
improve the estimation of risk in individual patients
with pCA. There is a concern about using PSA
recurrence as a surrogate for progression because it
736
european urology 54 (2008) 728–739
may not equate with clinical progression or cancerspecific death. However, the two studies that
reported clinical outcomes found that tumour
extent was an independent predictor of clinical
recurrence or metastases [33] or cancer-specific
survival [28], thereby establishing proof of principle
of the potential of measures of tumour extent to
refine prognostication.
The results of the few studies [25–27] measuring
tumour extent in millimetres were inconsistent,
possibly because of variations in study design and
statistical analyses. The cumulative length of cancer
was a significant predictor when analyzed as a
continuous [26] but not as a dichotomous variable
[27], whereas the converse was true for the maximum length of cancer [25]. The distribution of
measurements was also variably skewed, with 43%
of patients with 9 mm cancer in one study [26]
falling to 21% in the other [27]. Given the other
limitations on interpretation already described, no
firm conclusions can be reached regarding the value
of providing measurements as absolute figures.
By contrast, the studies that expressed tumour
extent as a percentage were much more consistent,
whether using the overall percentage of cancer in
the biopsy tissue [28–30,36,37], the greatest percentage in any one core [31,33,34], or other measures
[32,35]. Univariate analyses may be of limited
relevance given the established value of clinical
factors such as presenting PSA, but all except one
[32] of the multivariate models [29–33,36], found that
the percentage of cancer was independently predictive of PSA recurrence. The dissenting paper
reported the average percentage of cancer, which
may therefore not be as predictive as the greatest or
the total percentage of cancer.
The percentage of positive cores is an established predictor of PSA recurrence following radical
prostatectomy [11–20], although a large study by
Briganti et al [39] found that the number rather
than the percentage of positive biopsies may be
more informative. However, these measures may
not be the best indicators of tumour burden [13].
Eleven of the 13 studies reviewed here included
comparative data on the number or percentage of
positive cores and the linear extent of cancer. In
two, number [27] or percentage [32] was the
independent predictor of PSA outcome, whereas
the linear or surface area of cancer was not; in a
further two, both parameters were significant—at
least in univariate analysis [25,37]. However,
number or percentage was not a significant factor
in the other reports [26,29,31,33–36]. Possibly, the
spread of values was too skewed detect differences.
In the few papers with precise information in this
regard [26,27,34], at least two-thirds of patients had
only one or two positive cores. Furthermore, the
issue of fragmentation was not specifically
addressed, so that one fragmented core with three
separate foci of cancer may have been recorded as
one or three cores depending on the interpretation
of different pathologists. Nevertheless, the weight
of current evidence suggests that the linear
percentage of cancer on biopsy may be more
valuable in predicting PSA recurrence compared
to the number or percentage of positive cores
alone. The issue, therefore, is which method
should be adopted in clinical practice, although
demonstration of the limits of reproducibility—a
requirement for any clinical test—could be a
problem [38].
There is no evidence to support the independent
value of the mean percentage of cancer [32,35], but
there are consistent data to support the use of either
the total percentage of cancer [28–30,36,37] or the
greatest percentage of cancer in any one core
[31,33,34]. A limited number of studies provided
hazard ratios in a multivariate setting [29–31,33].
The method producing the highest reported hazard
ratio (8.25) was the measurement of the total
percentage of cancer analyzed as a continuous
variable [29], which also appeared to have a superior
predictive power compared with preoperative
serum PSA levels or biopsy Gleason score. However,
models using cut-off points for risk stratification
rather than continuous variable are easier to adapt
to clinical practice, and this was the approach
subsequently taken [30]. Hazard ratios were then
broadly similar [30] to those reported in papers using
the greatest percentage of cancer [31,33] (Table 5).
The hazard ratio was higher in the study in which
intervening benign tissue was excluded from the
measurement of cancer [33] compared to the
method including intervening benign stroma [31],
but the use of different cut-off points could account
for some of this difference.
The greatest percentage may underestimate
tumour burden if several cores are involved. There
are also theoretical advantages to using the total
percentage of cancer, as the issue of fragmentation
is not critical. Identification of individual cores is
required for the assessment of the greatest percentage of cancer, but processing and sectioning
individual cores separately has resource implications. However, fragmentation can be reduced by
careful handling; individual cores can be separated
between foam pads or other devices, and flat
embedding can ensure that each core is fully
represented. The disadvantage of measuring the
overall amount of cancer is that the result will be
european urology 54 (2008) 728–739
more strongly influenced by the amount of tissue
sampled. In addition to the number of cores, which
can vary quite widely (Tables 1 and 3), the needle
direction is important, as shown by a recent
systematic review of the effect of taking additional
biopsies: Laterally directed cores increased the yield
of pCA significantly ( p = 0.003), whereas centrally
directed cores did not [40]. Additional negative cores
will reduce the total percentage of cancer measurement, which may then underestimate large
tumours—particularly if unilateral. This limitation
might be overcome by measuring the total cancer
percentage on the most involved side rather than
overall, in the same way that the percentage of
positive cores on the dominant side had stronger
independent predictive value than the total percentage [41].
5.
Conclusions
Until there is better evidence, reporting both the
total and greatest percentage of cancer may
identify patients who are definitely at low risk of
progression if they satisfy the criteria established
by both predictive models [30,33]. Reporting the
number of positive cores may provide additional
information [39] and is recommended in the
European Association of Urology (EAU) guidelines
on pCA [17]. These different measurement methods are currently being tested within the prostatic
biopsy EQA scheme developed in the United
Kingdom.
Cumulatively, the studies reviewed have
included >7000 patients, but conclusions are limited by the retrospective nature of the studies, the
variations in patient characteristics, and the lack of
standardized methodology. All have used the
traditional method of investigating pathological
prognostic factors, which is to identify all patients
within an institution or database, select those who
have been treated in a specific way, and then
perform the analysis on the data available. These
observational studies are valuable for generating
hypotheses but not for producing definitive
answers [42], as has been documented in previous
systematic reviews of pathological prognostic factors [23,24,43]. Quality criteria have been established for the investigation of molecular markers
[44], but equal rigor should be applied to the more
traditional pathological factors. A well-designed
prospective trial using current best-practice biopsy
protocols [45], well-defined measurement methods,
and standardized outcome reporting is the only way
to determine the true significance of biopsy quantitative data and how these data should be used to
737
advise patients on the most appropriate management option.
Author contributions: Patricia Harnden had full access to all the
data in the study and takes responsibility for the integrity of
the data and the accuracy of the data analysis.
Study concept and design: Coles, Harnden, Mason, Naylor,
Shelley.
Acquisition of data: Coles, Harnden, Naylor, Shelley.
Analysis and interpretation of data: Harnden, Naylor, Shelley.
Drafting of the manuscript: Harnden, Naylor, Shelley.
Critical revision of the manuscript for important intellectual content:
Coles, Harnden, Mason, Naylor, Shelley.
Statistical analysis: None.
Obtaining funding: Coles, Harnden, Mason, Shelley.
Administrative, technical, or material support: Coles, Harnden,
Mason, Naylor, Shelley.
Supervision: None.
Other (specify): None.
Financial disclosures: I certify that all conflicts of interest,
including specific financial interests and relationships and
affiliations relevant to the subject matter or materials
discussed in the manuscript (eg, employment/affiliation,
grants or funding, consultancies, honoraria, stock ownership
or options, expert testimony, royalties, or patents filed,
received, or pending), are the following: None.
Funding/Support and role of the sponsor: Cancer Research UK
and Cancer Research Wales for collection and management of
the data.
Acknowledgment statement: This work was initiated for the
revision of the guidance on reporting of urological cancers for
the Royal College of Pathologists, United Kingdom. Cancer
Research Wales contributed to library facilities and Cancer
Research UK to salary costs (PH). We are grateful to D Berney, AV
D’Amico, JI Epstein, SJ Freedland, CP Nelson, A Renshaw, V
Ravery, and TJ Sebo for their prompt and helpful clarifications of
measurement methodology.
References
[1] Burnett AL, Aus G, Canby-Hagino ED, et al. Erectile function outcome reporting after clinically localized prostate
cancer treatment. J Urol 2007;178:597–601.
[2] Dubbelman YD, Dohle GR, Schröder FH. Sexual function
before and after radical retropubic prostatectomy: a systematic review of prognostic indicators for a successful
outcome. Eur Urol 2006;50:711–20.
[3] Gambacorta MA, Manfrida S, D’Agostino G, et al. Impact
of dose and volume on rectal tolerance. Rays 2005;30:
181–7.
[4] Stone NN, Stock RG. Complications following permanent
prostate brachytherapy. Eur Urol 2002;41:427–33.
[5] Klotz LH. Active surveillance for good risk prostate
cancer: rationale, method, and results. Can J Urol 2005;
12(Suppl 2):21–4.
738
european urology 54 (2008) 728–739
[6] Kattan MW, Eastham JA, Stapleton AM, Wheeler TM,
Scardino PT. A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer.
J Natl Cancer Inst 1998;90:766–71.
[7] Kattan MW, Zelefsky MJ, Kupelian PA, et al. Pretreatment
nomogram that predicts 5-year probability of metastasis
following three-dimensional conformal radiation therapy
for localized prostate cancer. J Clin Oncol 2003;21:4568–71.
[8] Kattan MW, Zelefsky MJ, Kupelian PA, Scardino PT, Fuks Z,
Leibel SA. Pretreatment nomogram for predicting the outcome of three-dimensional conformal radiotherapy in
prostate cancer. J Clin Oncol 2000;18:3352–9.
[9] Makarov DV, Trock BJ, Humphreys EB, et al. Updated
nomogram to predict pathologic stage of prostate cancer
given prostate-specific antigen level, clinical stage, and
biopsy Gleason score (Partin tables) based on cases from
2000 to 2005. Urology 2007;69:1095–101.
[10] Partin AW, Kattan MW, Subong EN, et al. Combination of
prostate-specific antigen, clinical stage, and Gleason
score to predict pathological stage of localized prostate
cancer. A multi-institutional update. JAMA 1997;277:
1445–51.
[11] Antunes AA, Srougi M, Dall’Oglio MF, Crippa A, Campagnari JC, Leite KR. The percentage of positive biopsy cores
as a predictor of disease recurrence in patients with
prostate cancer treated with radical prostatectomy. BJU
Int 2005;96:1258–63.
[12] Fröhner M. Editorial comment on: Chun FK-H, Karakiewicz PI, Briganti A, et al. Prostate cancer nomograms: an
update. Eur Urol 2006;50:914–26.
[13] Cooperberg MR, Pasta DJ, Elkin EP, et al. The University of
California, San Francisco Cancer of the Prostate Risk
Assessment score: a straightforward and reliable preoperative predictor of disease recurrence after radical prostatectomy. J Urol 2005;173:1938–42.
[14] Crippa A, Srougi M, Dall’Oglio MF, et al. A new nomogram
to predict pathologic outcome following radical prostatectomy. Int Braz J Urol 2006;32:155–64.
[15] D’Amico AV, Whittington R, Malkowicz SB, et al. Combination of the preoperative PSA level, biopsy Gleason
score, percentage of positive biopsies, and MRI T-stage
to predict early PSA failure in men with clinically localized
prostate cancer. Urology 2000;55:572–7.
[16] Gancarczyk KJ, Wu H, McLeod DG, et al. Using the percentage of biopsy cores positive for cancer, pretreatment PSA,
and highest biopsy Gleason sum to predict pathologic stage
after radical prostatectomy: The center for prostate disease
research nomograms. Urology 2003;61:589–95.
[17] Heidenreich A, Aus G, Bolla M, et al. EAU guidelines on
prostate cancer. Eur Urol 2008;53:68–80.
[18] San Francisco IF, Regan MM, Olumi AF, DeWolf WC.
Percent of cores positive for cancer is a better preoperative predictor of cancer recurrence after radical prostatectomy than prostate specific antigen. J Urol
2004;171:1492–9.
[19] Cooperberg MR, Freedland SJ, Pasta DJ, et al. Multiinstitutional validation of the UCSF cancer of the prostate risk
assessment for prediction of recurrence after radical
prostatectomy. Cancer 2006;107:2384–91.
[20] May M, Knoll N, Siegsmund M, et al. Validity of the
CAPRA score to predict biochemical recurrence-free
survival after radical prostatectomy. Results from a European multicenter survey of 1,296 patients. J Urol 2007;
1957–62.
[21] Egevad L, Allsbrook Jr WC, Epstein JI. Current practice of
diagnosis and reporting of prostate cancer on needle
biopsy among genitourinary pathologists. Hum Pathol
2006;37:292–7.
[22] Parsons JK, Partin AW. Clinical interpretation of prostate
biopsy reports. Urology 2006;67:452–7.
[23] Harnden P, Shelley MD, Clements H, et al. The prognostic
significance of perineural invasion in prostatic cancer
biopsies: a systematic review. Cancer 2007;109:13–24.
[24] Harnden P, Naylor B, Shelley MD, Clements H, Coles B,
Mason MD. The clinical management of patients with a
small volume of prostatic cancer on biopsy: what are the
risks of progression? A systematic review and meta-analysis. Cancer 2008;112:971–81.
[25] Egawa S, Suyama K, Arai Y, et al. Treatment outcome by
risk group after radical prostatectomy in Japanese men.
Int J Urol 2001;8:295–300.
[26] King CR, Patel DA, Terris MK. Prostate biopsy volume
indices do not predict for significant Gleason upgrading.
Am J Clin Oncol 2005;28:125–9.
[27] Noguchi M, Stamey TA, McNeal JE, Nolley R. Prognostic
factors for multifocal prostate cancer in radical prostatectomy specimens: lack of significance of secondary
cancers. J Urol 2003;170:459–63.
[28] Cuzick J, Fisher G, Kattan MW, et al. Long-term outcome
among men with conservatively treated localised prostate cancer. Br J Cancer 2006;95:1186–94.
[29] Freedland SJ, Aronson WJ, Csathy GS, et al. Comparison of
percentage of total prostate needle biopsy tissue with
cancer to percentage of cores with cancer for predicting
PSA recurrence after radical prostatectomy: results from
the SEARCH database. Urology 2003;61:742–7.
[30] Freedland SJ, Terris MK, Csathy GS, et al. Preoperative
model for predicting prostate specific antigen recurrence
after radical prostatectomy using percent of biopsy tissue
with cancer, biopsy Gleason grade and serum prostate
specific antigen. J Urol 2004;171:2215–20.
[31] Gretzer MB, Epstein JI, Pound CR, Walsh PC, Partin AW.
Substratification of stage T1C prostate cancer based on
the probability of biochemical recurrence. Urology
2002;60:1034–9.
[32] Linson PW, Lee AK, Doytchinova T, et al. Percentage of
core lengths involved with prostate cancer: does it add to
the percentage of positive prostate biopsies in predicting
postoperative prostate-specific antigen outcome for men
with intermediate-risk prostate cancer? Urology 2002;59:
704–8.
[33] Nelson CP, Dunn RL, Wei JT, Rubin MA, Montie JE, Sanda
MG. Contemporary preoperative parameters predict cancer-free survival after radical prostatectomy: a tool to
facilitate treatment decisions. Sem Urol Oncol 2003;21:
213–8.
[34] Nelson CP, Rubin MA, Strawderman M, Montie JE, Sanda
MG. Preoperative parameters for predicting early prostate
european urology 54 (2008) 728–739
[35]
[36]
[37]
[38]
[39]
cancer recurrence after radical prostatectomy. Urology
2002;59:740–6.
Potters L, Purrazzella R, Brustein S, Fearn P, Leibel SA,
Kattan MW. A comprehensive and novel predictive modeling technique using detailed pathology factors in
men with localized prostate carcinoma. Cancer
2002;95:1451–6.
Ravery V, Chastang C, Toublanc M, Boccon-Gibod L, Delmas V. Percentage of cancer on biopsy cores accurately
predicts extracapsular extension and biochemical relapse
after radical prostatectomy for T1-T2 prostate cancer. Eur
Urol 2000;37:449–55.
Sebo TJ, Cheville JC, Riehle DL, et al. Perineural invasion
and MIB-1 positivity in addition to Gleason score are
significant preoperative predictors of progression after
radical retropubic prostatectomy for prostate cancer.
Am J Surg Pathol 2002;26:431–9.
Harnden P, Coleman D, Moss S, Kodikara S, Patnick J,
Melia J. Prostatic pathology reporting in the UK: development of a national external quality assurance scheme.
Histopathology 2008;52:147–57.
Briganti A, Chun FK-H, Hutterer GC, et al. Systematic
assessment of the ability of the number and percentage
of positive biopsy cores to predict pathologic stage and
biochemical recurrence after radical prostatectomy. Eur
Urol 2007;52:733–45.
739
[40] Eichler K, Hempel S, Wilby J, Myers L, Bachmann LM,
Kleijnen J. Diagnostic value of systematic biopsy methods
in the investigation of prostate cancer: a systematic
review. J Urol 2006;175:1605–12.
[41] Freedland SJ, Aronson WJ, Terris MK, et al. The percentage
of prostate needle biopsy cores with carcinoma from the
more involved side of the biopsy as a predictor of prostate
specific antigen recurrence after radical prostatectomy:
results from the Shared Equal Access Regional Cancer
Hospital (SEARCH) database. Cancer 2003;98:2344–50.
[42] Moore A, McQuay H. Making sense of the medical evidence. Oxford, United Kingdom: Oxford University Press;
2006.
[43] Harnden P, Shelley MD, Coles B, Staffurth J, Mason MD.
Should the Gleason grading system for prostate cancer be
modified to account for high-grade tertiary components?
A systematic review and meta-analysis. Lancet Oncol
2007;8:411–9.
[44] McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M,
Clark GM. Reporting recommendations for tumor MARKer
prognostic studies (REMARK). Nat Clin Pract Urol
2005;2:416–22.
[45] Scattoni V, Zlotta A, Montironi R, Schulman C, Rigatti P,
Montorsi F. Extended and saturation prostatic biopsy in
the diagnosis and characterisation of prostate cancer: a
critical analysis of the literature. Eur Urol 2007;52:1309–22.