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24.11.2017 | Ausgabe 7/2018 Open Access

Abdominal Radiology 7/2018

The diagnostic accuracy of high b-value diffusion- and T2-weighted imaging for the detection of prostate cancer: a meta-analysis

Zeitschrift:
Abdominal Radiology > Ausgabe 7/2018
Autoren:
Tom J. Syer, Keith C. Godley, Donnie Cameron, Paul N. Malcolm
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s00261-017-1400-4) contains supplementary material, which is available to authorized users.
With a crude incidence of 134.3 per 100,000, prostate cancer is the most common cancer in men, and the second-biggest cause of cancer mortality [1, 2]. The quoted incidence has increased in recent years; however, this may be due to the use of prostate-specific antigen (PSA) blood testing. The majority of suspected cases with either a high PSA, abnormal digital rectal examination (DRE), or suggestive symptoms, will undergo a transrectal ultrasound guided biopsy (TRUS) to confirm and grade a histopathologic diagnosis [3]. If this is positive and the patient is a candidate for radical treatment, they will receive multiparametric magnetic resonance imaging (mpMRI) to assess the extent of cancer growth. However, there are now a substantial number of centers choosing pre-biopsy mpMRI followed by a more-targeted biopsy.
Multiparametric MRI is a well-established imaging modality for assessing prostate cancer, predominately to exclude extra-glandular spread and to judge how much of the prostate is involved. It consists of multiple sequences, including T1- and T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and, in some instances, Dynamic contrast-enhanced (DCE) imaging. Multiple meta-analyses have proven DWI to have good diagnostic accuracy [46]; its contrast is governed by numerous technical parameters, one of the most important of these is the diffusion-weighting factor, or ‘b-value’. The b-value reflects the strength and timings of magnetic field gradients applied to the patient, and acquisition of multiple b-values permits calculation of an apparent diffusion coefficient (ADC) map, which gives a quantitative measure of tissue diffusion that has been shown to have an inverse correlation with tumor Gleason score [7]. Currently the recommendation is to use at least two b-values, one of 50–100 s/mm2, 800–1000 s/mm2 and if possible 1400–2000 s/mm2 [8, 9]. Theoretically, increasing the maximum b-value results in a better contrast-to-noise ratio (CNR) because there is greater suppression of normal prostate tissue signal, so resulting tumors are more apparent. However, the tradeoff is a reduced signal-to-noise ratio (SNR). Even though b-values > 1400 is recommended, there is little evidence supporting this and there is no widely accepted optimal “high b-value.” In a previous meta-analysis, Wu et al. showed no benefit from increasing b-value but only one paper in the analysis used b-values of over 1000 [4]. A multitude of recent studies have shown high sensitivity and specificity with higher b-values using both visual and ADC value assessments [1012]. For clinical relevance, we hope to investigate the diagnostic accuracy achievable by visual assessment of DWI in combination with T2WI at high b-values > 1000 s/mm2.

Materials and methods

This review was registered with the PROSPERO International prospective register of systematic reviews (reference number: 42016036196) prior to commencement [13]. The review was carried out in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidance [14].
A systematic review of the literature was independently undertaken by two reviewers, who identified studies that investigated the diagnostic accuracy of DWI and T2WI MRI in the detection of prostate cancer. Searches were performed using MEDLINE and EMBASE electronic databases, as well as OpenSIGLE to explore sources of unpublished gray literature. The Science Citation Index was used to identify articles which cite those identified with the original search terms. Once eligible studies were found, their reference lists were manually searched for further potential papers. The search strategy for MEDLINE, including Boolean operators and MeSH terms, is presented in Table 1; the same search strategy was used for each database with alterations to suit. All studies were included up to the date of the search: 1st of September 2017.
Table 1
MEDLINE search terms and strategy
1
Exp prostate* neoplasm*/
2
Prostat* cancer*.mp.
3
Prostat* carcinoma*.mp.
4
Or/1–3
5
Exp diffusion magnetic resonance/
6
DW magnetic resonance imaging.mp.
7
DWI.mp.
8
DW-MRI.mp.
9
Or/5–6
10
4 and 9
11
10 Limit to human studies
12
11 Limit to english language

Eligibility

The eligibility criteria for the studies included within the systematic review were that they used both DWI and T2WI MRI in combination for the assessment of prostate cancer; they were applied for the assessment of the pretreatment patient population with a histopathologic reference standard, be that biopsy or radical prostatectomy; they reported sufficient information to produce a 2 × 2 table (true positives, false positives, false negatives, and true negatives) for calculation of sensitivity and specificity; they were published in English; and they assessed more than ten individual patients. To be included, both T2WI and DWI sequences needed to be assessed visually, with both sequences used to assess for tumor presence rather than just for localization. The choice of scoring system, such as Likert or PI-RADS, and whether a sector-based or whole gland assessment was conducted did not affect eligibility. Articles were excluded if they did not satisfy the inclusion criteria above, or if they used a combination of imaging sequences other than DWI and T2WI so that individual data for the desired combination could not be extracted. They were also excluded if an ADC cutoff value was used to discriminate malignant from benign tissue as opposed to visual assessment by certified radiologists. Studies were not excluded by country of origin, age of patients or study design.

Study identification

Initially papers were reviewed by relevancy of title and then abstract. Residual articles had their full text reviewed against the inclusion and exclusion criteria. This was also done independently by the same two reviewers. Any disagreement was solved by consensus or a third expert reviewer if necessary.

Data extraction

The following data were extracted from each eligible study: year of publication, country of origin, patient group, number of patients, average age, and PSA, study design (retrospective or prospective) and the histopathologic reference standard used. Further information on the imaging specifications was also gathered: field strength, coil used, field-of-view, b-value set, and whether they visually assessed DWI source images, ADC maps, or both, for each patient. True positives, false positives, false negatives, and true negatives were also extracted for pooling results. In the case of multireader studies, the most experienced was chosen for data extraction. When insufficient data were available, reviewers manually calculated them from other reported statistics, when possible. All data extraction was independently verified by two reviewers.

Quality assessment

The quality of the individual included paper’s methodology was assessed with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, a validated tool specifically designed to critically appraise diagnostic accuracy studies [15]. This was also undertaken independently by two reviewers and disagreement resolved with consensual discussion consulting a third expert reviewer if a consensus could not be met.

Statistical analysis

The sensitivity and specificity with 95% confidence intervals (CIs) were calculated for each included study using the extracted details of the 2 × 2 tables, and forest plots produced.
Initially, heterogeneity of studies was examined visually using the data extraction tables. Then, statistical analysis was performed using the inconsistency value (I 2) and Q statistics of the Chi squared value, for which an I 2 value > 50% or p value < 0.10, respectively, represents significant statistical heterogeneity. In these cases, a random-effects model was applied to data pooling. Pooled results for sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% CIs, and a summary receiver-operating characteristic (sROC) curve were also presented.
To explore predictable sources of heterogeneity between the included studies, sensitivity and 1-specificity were plotted on an ROC plane to visually assess the presence or absence of a ‘shoulder arm’ shape, which indicates a threshold effect. This was also tested statistically with the Spearman correlation coefficient of the logit of sensitivity and logit of (1-specificity), with a p-value < 0.05 suggesting a threshold effect. Subgroup analysis was performed for; b-values (< 1000, 1000 and > 1000 s/mm2), field strength (1.5T and 3T), coil type (endorectal and body), method of assessment (DWI source images, ADC or both), reference standard (biopsy and radical prostatectomy), tumor zone (peripheral or transitional zone) and study design (retrospective and prospective). If possible raw data was separated from individual papers for each subgroup. Pooled sensitivities, specificities, positive and negative likelihood ratios, and meta-regression of diagnostic odds ratios were performed for these subgroups with a p-value < 0.05 deemed as statistically significant.
Publication bias was not assessed as there is currently no recognized or appropriate method that does so with sufficient power for diagnostic accuracy studies, and the impact of publication bias is presently unknown for studies of this type [16].
All statistical analysis was performed using Meta-DiSc (version 1.4, Javier Zamora).

Results

Search results

With the above-presented search strategy, 2825 citations were discovered, and after duplicates were removed, there were left 1880 unique articles. A total of 33 studies were included in the final analysis after reviewing against the eligibility criteria. The PRISMA flowchart of the search results is presented in Fig. 1.

Quality assessment

The full results of the QUADAS-2 appraisal are presented in Table 2. The strengths across the included studies were that the vast majority used consecutive patient selection with appropriate inclusion and exclusion criteria. However, two studies [17, 18] limited their investigation to transitional zone tumors and another [19] to patients with ‘low risk’ cancer. Therefore a subgroup analysis was deemed particularly important to assess the differences between peripheral and transitional zone tumors. Another strength was that all index tests used were applicable to clinical practice, without any nonstandard imaging methods. All but one study imaged patients after a positive biopsy, while patients studied by Tanimoto et al. had a pre-biopsy MRI [20]. A number of studies did not state the timings between biopsy and MRI [2124], which could have implications if the timing was too long causing a disparity between the images and histopathology correlation or too short resulting in an increased incidence of post-biopsy hemorrhage which might limit accuracy. Kitajima et al. [25] and Morgan et al. [26] reported delays between biopsy and imaging much less than the recommended six weeks [27]. The predominant weakness of included studies was applicability of the patient groups, as studies were often limited to patients who underwent radical prostatectomy. These patients tend to be younger, with a narrower range of tumor staging. However, this is acceptable to obtain a reference test with low bias.
Table 2
QUADAS-2 quality assessment of included studies
Study
Risk of bias
Applicability
Patient selection
Index test
Reference test
Flow and timing
Patient selection
Index test
Reference test
Agha [43]
?
Bains [44]
Baur [45]
?
Brendle [46]
Costa [47]
?
Doo [48]
Haider [49]
Hoeks [17]
Isabaert [21]
?
Iwazawa [50]
Jung [18]
Katahira [51]
Kim [19]
Kitajima [25]
?
Kuhl [33]
?
Lim [52]
Loggitsi [53]
Morgan [26]
Ohgiya [54]
Petrillo [22]
?
Rosenkrantz [55]
Rosenkrantz [56]
Shimofusa [57]
Shinmoto [58]
?
Stanzione [59]
?
?
Tanimoto [60]
?
Thestrup [61]
Ueno 2013 [12]
Ueno 2013 [23]
?
Ueno 2015 [62]
?
Vargas [63]
Yoshimitsu [64]
Yoshizako [65]
✓ Low risk; ✗ high risk; ? unclear risk

Study characteristics

The data extracted for study characteristics are described in Tables 3, 4, and 5. There were 2949 patients across the 33 studies. The mean age (range) was 65.1 (41–86) years, and PSA was 9 (0.4–130) ng/mL, respectively. The majority of studies (n = 20) used a retrospective study design as opposed to prospective (n = 13). Most of the studies (n = 19) used 3T field strength, thirteen studies used 1.5 T, and one study used both. Maximum b-values across the studies ranged from 600 to 2000 with the majority using 1000. Nine studies used an endorectal coil. Nine studies used DWI source images for diagnosis, while seven used ADC maps and seventeen used both. Most studies (n = 20) used radical prostatectomy as the reference standard while seven used TRUS biopsy, two MRI guided biopsy, one transperineal biopsy and another used a mixture of TRUS biopsy and radical prostatectomies.
Table 3
Principle characteristics of included studies
Study
Year
Country
No. of patients
Age (range)
PSA (range)
Design
Agha [43]
2015
Egypt
20
n/a
n/a
Pro
Bains [44]
2014
Switzerland
111
64a (43–82)
n/a (0.7–112.2)
Pro
Baur [45]
2016
Germany
44
66 (46–81)
12.3 (5.2–70)
Pro
Brendle [46]
2016
Germany
15
66 (52–76)
11.8 (3.3–65.4)
Pro
Costa [47]
2016
USA
49
63 (49–79)
11.2 (2.5–48.5)
Pro
Doo [48]
2012
South Korea
51
63a (50–72)
11.5 (4.2–43.8)
Retro
Haider [49]
2007
Canada
49
61a (46–75)
5.4a (0.9–26)
Pro
Hoeks [17]
2013
Netherlands
28
n/a (45–73)
n/a (1.9–44)
Retro
Isabaert [21]
2013
Belgium
75
66a (49–64)
10.4 (1.5–70.9)
Pro
Iwazawa [50]
2011
Japan
178
69 (41–86)
n/a
Retro
Jung [18]
2013
South Korea
156
59a (42–75)
4.9 (0.4–93.7)
Retro
Katahira [51]
2011
Japan
201
69 (43–80)
13.2 (2.6–114)
Retro
Kim [19]
2014
South Korea
100
63a (51–76)
6.5a (2.2–9.5)
Retro
Kitajima [25]
2010
Japan
53
69a (56–84)
11.1a (4.2–112.1)
Retro
Kuhl [33]
2017
Germany
542
64.8 (42–80)
8.5 (3.2–67.5)
Pro
Lim [52]
2009
South Korea
52
65 (48–76)
10.5 (1.2–79.6)
Retro
Loggitsi [53]
2017
Greece
26
63.7 (48–73)
8.1 (2–21.9)
Pro
Morgan [26]
2007
UK
54
68 (52–80)
10 (n/a)
Pro
Ohgiya [54]
2012
Japan
73
70 (n/a)
11.7a (n/a)
Retro
Petrillo [22]
2014
Italy
136
66 (n/a)
6.8 (n/a)
Pro
Rosenkrantz [55]
2011
USA
42
62 (47–76)
6.2 (1.3–32.5)
Retro
Rosenkrantz [56]
2015
USA
106
62 (56–81)
6.9 (n/a)
Retro
Shimofusa [57]
2005
Japan
37
71 (54–82)
21.8 (4.5–130)
Retro
Shinmoto [58]
2015
Japan
87
n/a (51–75)
n/a (2.8–35.2)
Retro
Stanzione [59]
2016
Italy
82
65 (n/a)
8.8 (n/a)
Pro
Tanimoto [60]
2007
Japan
83
67 (53–87)
19.4 (n/a)
Pro
Thestrup [61]
2016
Denmark
204
64.1 (45–75)
14 (2.2–120)
Retro
Ueno [12]
2013
Japan
73
67 (50–77)
9.51 (2.9–49)
Retro
Ueno [23]
2013
Japan
80
67 (50–77)
9.51 (2.9–49)
Retro
Ueno [62]
2015
Japan
31
65 (51–81)
8.6 (4.7–16.5)
Retro
Vargas [63]
2011
USA
51
56a (46–74)
5.3 (0.4–62.2)
Retro
Yoshimitsu [64]
2008
Japan
37
66 (56–75)
11.9 (0.7–54.8)
Retro
Yoshizako [65]
2008
Japan
23
65a (52–76)
n/a
Retro
aMedian; N/A, not available; Pro, prospective; PSA, prostate-specific antigen (ng/mL); Retro, retrospective)
Table 4
Imaging and methodological characteristics of included studies
Study
Field strength
Endorectal coil
FOV (cm)
b-value
Reference
AS
Method
Agha [43]
3T
N
30 × 30
0, 1000
Bx
U
Both
Bains [44]
3T
N
n/a
0, 500, 1000
RP
Y
Both
Baur [45]
3T
Both
20 × 20
0, 100, 500, 1000
MR
N
Both
Brendle [46]
3T
N
27.6 × 28
50, 800
RP
U
Both
Costa [47]
3T
Both
16 × 16
0–2000
Mix
U
Both
Doo [48]
3T
N
28 × 28
0, 1000
RP
U
ADC
Haider [49]
1.5T
Y
14 × 14
0, 600
RP
U
ADC
Hoeks [17]
3T
Y
20.4 × 20.4
0, 50, 500, 800
RP
U
Both
Isabaert [21]
1.5T
N
30.9 × 38
0, 50, 100, 500, 1000
RP
U
DWI
Iwazawa [50]
1.5T
N
30 × 30
0, 1000
Bx
U
DWI
Jung [18]
1.5T/3T
Y
12 × 12/14 × 14
0, 1000
RP
U
ADC
Katahira [51]
1.5T
N
35 × 35
0, 1000, 2000
RP
U
DWI
Kim [19]
3T
N
34 × 16.8
0, 100, 1000
RP
Y
Both
Kitajima [25]
3T
N
35 × 25
0, 1000
Bxa
N
Both
Kuhl [33]
3T
N
21 × 21
0, 800, 1000, 1400
MR
U
Both
Lim [52]
1.5T
Y
22 × 22
0, 1000
RP
Y
ADC
Loggitsi [53]
1.5T
N
10 × 10
0, 250, 500, 750, 1000
RP
U
Both
Morgan [26]
1.5T
Y
20 × 20
0, 300, 500, 800
Bxa
Y
ADC
Ohgiya [54]
3T
N
35 × 35
0, 500, 1000, 2000
Bx
U
DWI
Petrillo [22]
1.5T
Y
13.6 × 16
0, 50, 100, 150, 300, 600, 800
Bx
N
Both
Rosenkrantz [55]
1.5T
N
30 × 24.4
0, 500, 1000
RP
U
DWI
Rosenkrantz [56]
3T
N
20 × 20/28 × 21.8
50, 1000, 2000
RP
U
Both
Shimofusa [57]
1.5T
N
20 × 20
0, 1000
Mix
U
DWI
Shinmoto [58]
3T
N
24 × 24
0, 1000
RP
Y
ADC
Stanzione [59]
3T
N
20 × 20
0, 400, 2000
Bx
U
Both
Tanimoto [60]
1.5T
N
36 × 36
0, 1000
Bx
U
Both
Thestrup [61]
3T
U
19 × 19
0, 100, 800, 2000
Mix
Y
Both
Ueno [12]
3T
N
45 × 45
0, 1000, 2000
RP
Y
DWI
Ueno [23]
3T
N
n/a
0, 1000, 2000
RP
Y
DWI
Ueno [62]
3T
N
45 × 36
0, 2000
RP
Y
DWI
Vargas [63]
3T
Y
14 × 14
0, 700/0, 1000
RP
U
ADC
Yoshimitsu [64]
1.5T
N
24 × 24
0, 500, 1000
RP
U
Both
Yoshizako (65)
1.5T
N
42 × 21
0, 1000
RP
Y
Both
aTransperineal biopsy; AS, antispasmodic; Bx, biopsy; FOV, field-of-view; Mix, mixture of Bx and RP; MR, Magnetic resonance imaging guided biopsy; N, no; RP, radical prostatectomy; T, tesla; U, unclear; Y, yes
Table 5
Diagnostic performance of included studies
Study
TP
FP
FN
TN
Sens
Spec
Notes
Agha [43]
10
1
5
4
0.67
0.80
 
Bains [44]
73
7
7
24
0.91
0.77
 
Baur [45]
14
11
0
18
0.97
0.62
Body coil
 
10
7
1
21
0.91
0.75
Endorectal coil
Brendle [46]
17
2
12
149
0.59
0.99
 
Costa [47]
20
19
06
73
0.44
0.79
Body coil
 
76
51
22
145
0.78
0.74
Endorectal coil
Doo [48]
113
21
58
216
0.66
0.91
 
Haider [49]
120
39
29
204
0.81
0.84
 
Hoeks [17]
65
39
47
101
0.58
0.72
TZ
Isabaert [21]
444
79
546
731
0.45
0.90
 
Iwazawa [50]
238
223
80
883
0.75
0.80
 
Jung [18]
91
62
84
699
0.52
0.92
TZ
Katahira [51]
971
559
616
2669
0.61
0.83
b max = 1000
 
1162
332
425
2896
0.73
0.90
b max = 2000
Kim [19]
17
7
22
72
0.44
0.91
 
Kitajima [25]
75
19
24
306
0.76
0.94
 
Kuhl [33]
138
49
9
346
0.94
0.88
 
Lim [52]
199
49
28
348
0.88
0.88
 
Loggitsi [53]
43
33
62
330
0.41
0.91
 
Morgan [26]
64
56
78
126
0.45
0.69
 
Ohgiya [54]
25
5
30
13
0.45
0.72
b max = 500
 
43
4
12
14
0.78
0.78
b max = 1000
 
42
2
13
16
0.76
0.89
b max = 2000
Petrillo [22]
18
48
7
63
0.72
0.57
 
Rosenkrantz 2011 [55]
61
29
59
103
0.51
0.78
 
Rosenkrantz 2015 [56]
34
13
28
561
0.55
0.98
b max = 1000
 
46
10
16
564
0.74
0.98
b max = 2000
Shimofusa [57]
96
11
15
56
0.86
0.84
 
Shinmoto [58]
93
12
58
185
0.62
0.94
 
Stanzione [59]
29
1
5
52
0.85
0.98
 
Tanimoto [60]
37
6
7
33
0.84
0.85
 
Thestrup [61]
65
116
3
20
0.96
0.15
 
Ueno 2013 [12]
258
87
83
156
0.76
0.64
b max = 1000
 
276
79
65
164
0.81
0.68
b max = 2000
Ueno 2013 [23]
270
119
57
194
0.83
0.62
b max = 1000
 
275
105
52
208
0.84
0.66
b max = 2000
 
272
95
55
218
0.83
0.70
b max = c2000
Ueno 2015 [62]
101
63
20
64
0.83
0.50
b max = 2000
 
86
51
35
76
0.71
0.60
b max = c2000
Vargas [63]
65
10
42
157
0.61
0.94
 
Yoshimitsu [64]
105
29
42
46
0.71
0.61
 
Yoshizako (65)
21
2
5
14
0.81
0.88
TZ
b, b-value; c, computed; FN, false negative; FP, false positive; PZ, peripheral zone; sens, sensitivity; spec, specificity; TN, true negative; TP, true positive; TZ, transitional zone

Meta-analysis

Visual assessment of the data extraction tables indicated they were homogeneous enough to undertake a meta-analysis with pooling. The pooled sensitivity (Fig. 2) and specificity (Fig. 3) of all included studies were 0.69 (95% CI 0.68–0.69) and 0.84 (95% CI 0.83–0.85), respectively. The pooled DOR was 12.27 (95% CI 9.60–15.68). The sROC (Fig. 4) gave an AUC of 0.839, indicating good diagnostic accuracy.
The I 2 value and Chi-square Q were 94.6% and 882.53 (p < 0.001), respectively, for sensitivity and 96.7% and 1446.59 (p < 0.001) for specificity, indicating significant statistical heterogeneity. The ROC plane (Supplementary Fig. 1) did not show a ‘shoulder-arm’ shape; however, the Spearman rank coefficient of the logit of sensitivity against logit of (1-specificity) was 0.335 (p = 0.018), indicating there could be heterogeneity due to a threshold effect.

Sub-group analysis

The highest DORs were obtained when using ADC maps with or without DWI for tumors assessment and for b-values > 1000 s/mm2. Significantly higher sensitivity was achieved using b-values > 1000 s/mm2, 3T field strength, assessing PZ tumors, studies with a retrospective design and those using biopsy as a reference standard. Specificity improved significantly with a 1.5T field strength, assessing TZ tumors, using ADC maps with or without DWI and those studies using radical prostatectomy as the reference standard. The complete subgroup analysis is shown in Table 6.
Table 6
Subgroup analysis and meta-regression
Group (number of studies)
Sensitivity
Specificity
DOR
I 2 (%)
P-value
Total
0.69 (0.68–0.69)
0.84 (0.83–0.85)
12.268 (9.60–15.68)
90.7
 
b-value
    
0.068
 < 1000 (n = 7)
0.60 (0.56–0.64)
0.80 (0.78–0.83)
8.02 (3.18–20.26)
91.4
 
 1000 (n = 23)
0.64 (0.62–0.65)
0.85 (0.84–0.85)
12.56 (9.56–16.50)
86.0
 > 1000 (n = 13)
0.78(0.76–0.79)
0.83 (0.82–0.84)
14.32 (9.06–22.65)
92.0
Field strength
    
0.418
 1.5 T (n = 14)
0.64 (0.63–0.65)
0.85 (0.84–0.86)
10.68 (7.34–15.55)
94.0
 
 3 T (n = 28)
0.76 (0.75–0.78)
0.81 (0.79–0.82)
13.77 (9.54–19.88)
87.6
Coil
    
0.597
 Body (n = 33)
0.68 (0.67–0.69)
0.85 (0.84–0.85)
13.06 (10.05–16.97)
90.4
 
 Endorectal (n = 9)
0.68 (0.65–0.71)
0.84 (0.84–0.85)
10.40 (4.87–22.24)
93.1
Tumor zone
    
0.239
 PZ (n = 6)
0.71 (0.70–0.73)
0.84 (0.82–0.85)
12.64 (7.13–22.41)
94.4
 
 TZ (n = 11)
0.66 (0.64–0.68)
0.88 (0.87–0.88)
13.46 (8.08–22.44)
92.6
Assessment method
    
0.070
 DWI (n = 12)
0.68 (0.67–0.69)
0.82 (0.81–0.83)
8.91 (6.8–11.68)
90.2
 
 ADC map (n = 7)
0.66 (0.64–0.69)
0.89 (0.87–0.90)
15.44 (6.8–35.05)
93.8
 Both (n = 20)
0.72 (0.70–0.75)
0.86 (0.85–0.87)
18.58 (9.77–35.30)
90.0
Design
    
0.918
 Prospective (n = 15)
0.59 (0.56–0.61)
0.81 (0.80–0.83)
11.93 (6.61–21.54)
89.2
 
 Retrospective (n = 28)
0.71 (0.70–0.72)
0.84 (0.84–0.85)
12.56 (9.57–16.49)
91.3
Reference standard
    
0.420
 RP (n = 26)
0.67 (0.66–0.68)
0.85 (0.85–0.86)
12.09 (9.24–15.81)
91.4
 
 Biopsy (n = 13)
0.73 (0.70–0.76)
0.81 (0.80–0.83)
15.83 (7.27–34.44)
91.3
CI, confidence interval; DOR, diagnostic odds ratio; I 2, inconsistency value; T, tesla; PZ peripheral zone; TZ transitional zone; DWI diffusion-weighted imaging; ADC, apparent diffusion coefficient; RP, radical prostatectomy

Discussion

The findings from this study show the diagnostic accuracy of DWI and T2WI of prostate cancer is good when using visual assessment. The greatest diagnostic accuracy is achieved with b-values > 1000 s/mm2, and when assessing lesions with both DWI source images and ADC maps, although the interplay between sensitivity and specificity can be significantly altered by the choice of field strength and by whether tumors originate from the peripheral or transitional zone. The overall strength of the evidence on which this analysis was based was graded as good by the QUADAS-2 critical appraisal tool [15]. However, there was a high degree of unknown statistical heterogeneity, so care should be taken when interpreting these results, and even though this review cannot specify an optimal imaging protocol, it does highlight the likely important factors to be considered.
Our pooled results match those of meta-analyses investigating T2WI and DWI by Wu et al. and Tan et al.; this is likely due to the large overlap of included studies [28, 29]. Compared with Godley et al. and Jie et al. who analyzed the use of DWI alone, we observed a higher sensitivity but lower specificity [5, 30]. However, when we compare the results for just peripheral zone tumors, our pooled results are similar. This would suggest that the addition of T2WI improves the sensitivity for diagnosing transitional zone tumors; however, neither Godley nor Jie et al. presented a subgroup for TZ tumors or comparison [10]. This finding supports the present consensus that T2WI with DWI should be the predominant imaging protocol for diagnosing TZ tumors [9].
We observed a significant increase in sensitivity using a maximum b-value > 1000 s/mm2, and improved specificity with a maximum b-value of ≥ 1000 s/mm2. The improved contrast-to-noise ratio at higher b-values, resulting from the relative suppression of normal prostate tissue, would explain the increase in sensitivity by making tumors more visually apparent. Two of the studies [23, 24] also used computed high b-values. These synthetic data extrapolated from low b-value datasets showed relatively decreased sensitivity and increased specificity compared to the equivalent acquired b-values. There has been limited research comparing the diagnostic accuracy of computed DWI to standard DWI, but the method shows promise with reduced distortion and ghosting and improved tumor conspicuity [31, 32].
We also note that all studies using b-values > 1000 s/mm2 were limited to a maximum b-value of 2000 s/mm2, except the study by Kuhlet al [33]. Wang et al. and Metens et al. found b-values of 1500 s/mm2 gave a better tumor contrast and image quality than b-values of 1,000 or 2000 s/mm2and Kuhl et al. using a b-value of 1400 s/mm2, produced some of the highest sensitivities and specificities [3335]. However, more data on the diagnostic accuracy of b ≈ 1,500 DWI are required. Furthermore, the maximum b-value, the minimum b-value, and the number of b-values have all been shown to have a strong influence on the calculated ADC values [36]. However, there is little evidence about their impact on diagnostic accuracy with visual assessment [10, 36].
All but two of the included studies in this analysis used b = 0 s/mm2 as the minimum b-value, but the number of b-values ranged from two to seven. Thörmer et al. found that using just two b-values and a minimum b-value of 50 s/mm2 gave an improved qualitative image score versus data with a minimum b-value of 0 s/mm2 [37]. However, they tested only a limited number of combinations, and used a maximum b-value of just 800 s/mm2. The significant heterogeneity of b-value choice in the included studies makes it extremely difficult to provide a conclusion that high b-values are indeed superior for diagnostic accuracy. The individual studies that tested multiple b-value sets on the same cohort do, however, show improved diagnostic accuracy using b = 2,000 as opposed to 1000 or lower. Further studies directly comparing b-value sets of different maximum, minimum, and a number of intermediary b-values would be required to make a stronger recommendation of b-value choice.
DOR was not significantly different between 1.5T and 3T studies (p = 0.418), but 3T studies showed a significantly higher sensitivity and significantly lower specificity than those performed at 1.5T. Higher field strengths have the advantage of increased SNR, which can be traded for better spatial and temporal resolutions; they also lead to increased susceptibility artifact and signal heterogeneity, and there is conflicting evidence with respect to the categorical advantage of 3T over 1.5 T [38]. There is a trend toward better diagnostic accuracy with 3T in our study, although this may be because these systems allow the use of higher b-values, which improve diagnostic accuracy. This result reflects the recommendations of PIRADS v2 that 1.5T and 3T are both adequate, but 3T is regarded optimal if available [9].
For a few of the studies, it was possible to separate the results for PZ and TZ, and we found significantly higher sensitivity for the PZ, but higher specificity for TZ. Often TZ tumors are of a lower grade than those found in the PZ, so they may be less apparent on imaging [39, 40]. There is also difficulty in differentiating malignancy from benign nodules common in the TZ, which are often heterogeneous and can demonstrate restricted diffusion. Along with the relative rarity of TZ tumors this may explain the drop in sensitivity but the overall DOR was not significantly different. It may be that different imaging parameters are needed for optimal diagnosis of peripheral or transitional zone disease.
Our results showed a significant increase in both sensitivity and specificity when using ADC maps with or without DWI source images for diagnostic assessment, as opposed to using DWI source images alone. There are many advantages to using ADC maps which might explain this change. Firstly, ADC maps give a quantitative measure of tissue diffusion, and are particularly useful in differentiating areas which have high signal on DWI images due to T2 shine-through, such as post-biopsy hemorrhage; this leads to reduced false positives and improved specificity versus weighted images. The ADC value can also be used to help confirm malignant lesions, which have low ADCs due to restricted diffusion, and this would explain the higher sensitivity seen.
Retrospective studies investigated men with previously confirmed prostate cancer, and therefore the readers knew there was cancer present in each prostate examined. This may cause the readers to be more liberal with diagnosing suspicious lesions in borderline cases where there were no other lesions in the gland, explaining the significantly higher sensitivity.
Using radical prostatectomy as the reference standard allows the assessment of individual tumors within the gland and is a more accurate method of defining tumor. TRUS biopsy is ‘blind’ and only samples a small area of the prostate, with a 20–30% false negative rate. This would lead to increased false positives on imaging, decreasing the specificity as we observe in the subgroup analysis.
This systematic review has a few limitations. Our search was, first, limited by a finite number of databases although those chosen contain the majority of the relevant journals, and by exploring the gray literature and hand-searching references, we believe the search strategy was of sufficient sensitivity. Specific databases for the research question were sought, but none existed. Second, the search was limited to the English language. The majority of articles are published in English, but there may be data in other languages that we did not include in this meta-analysis. We did not assess for publication bias for reasons stated in the statistical analysis section. The degree to which publication bias impacts diagnostic tests is unknown [16]. We did not review the exact T2WI parameters for the included studies, which could explain some of the heterogeneity seen. Reader experience is another factor which we did not assess as it was often poorly reported and in different formats such as years practicing, years reporting prostate mpMRI, or number of prostate mpMRIs. It is recognized that reader experience is important in interpreting mpMRI and should be considered when implementing prostate imaging [41, 42]. Although diagnostic accuracy is very important for prostate cancer assessment, there are other aims of mpMRI which have not been assessed in this meta-analysis: for example, assessment of extracapsular extension, seminal vesicle or lymph node involvement, and the ability of mpMRI to quantify tumor size and volume. These findings are all used in staging of disease and are relevant to decisions about optimal imaging sequences.
In conclusion, the diagnostic accuracy of combined diffusion- and T2-weighted magnetic resonance imaging for prostate cancer detection is good, and our results support the PI-RADS v2 guidelines [9]. The use of b-values > 1000 s/mm2 seem to improve the sensitivity while maintaining specificity. However, due to large amounts of heterogeneity, we cannot categorically recommend using maximum b-values up to 2000 s/mm2 for all DWI protocols for prostate cancer assessment. Further large-scale study investigating optimal b-value maximum, minimum, and number of b-values for the visual assessment of prostate cancer is required.

Compliance with ethical standards

Funding

This research did not receive any specific grant from any funding agencies in the public, commercial, or not-for-profit sectors.

Conflict of interest

Author Tom Syer declares he has no conflict of interest. Author Keith Godley declares he has no conflict of interest. Author Donnie Cameron declares he has no conflict of interest. Author Paul Malcolm declares he has no conflict of interest.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://​creativecommons.​org/​licenses/​by/​4.​0/​), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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