Keywords prostate cancer - intermediate-risk group - diffusion kurtosis imaging - intravoxel
incoherent motion imaging - stretched exponential model
Introduction
Prostate carcinoma (PCa) ranks among the more prevalent neoplasms in males, exhibiting
a frequency that escalates with advancing age.[1 ] Although PCa is often slow to progress and is potentially curable, its implications
from either excessive or insufficient treatment position it as a major worldwide cause
of oncological impairments and a critical factor in mortality among males due to cancer.[2 ]
[3 ] Selecting the most appropriate therapeutic strategy for a particular patient is
complex and demands evaluation of the patient's overall health status, the tumor's
properties, adverse effects from potential treatments, evidence from clinical trials
with comparable patient cohorts, and forecasts of their prospective outcomes. This
complexity is further intensified by the absence of readily accessible prognostic
instruments that could more effectively stratify patients by risk.
One of the most common systems used to risk-stratify patients worldwide is the National
Comprehensive Cancer Network (NCCN) guidelines of PCa, which uses a minimum of stage,
Gleason grade, and serum prostate-specific antigen (PSA) to assign patients to risk
groups.[4 ] This three-tier system forms the basis of treatment recommendations used for localized
prostate cancer throughout the world and some studies have shown that this system
provides better treatment recommendations and prognosis prediction results than guided
by the clinical stage or Gleason grade system alone.[5 ]
[6 ] Results of Zumsteg et al[7 ] showed that compared with the unfavorable intermediate-risk group (un-FIRG), the
favorable intermediate-risk group (FIRG) have better prognosis, the un-FIRG have lower
PSA recurrence-free survival, higher rates of distant metastasis, and PCa-specific
mortality than the FIRG.
Multiparametric magnetic resonance imaging (mpMRI) has become a common method for
screening, diagnosing, and monitoring PCa. In recent years, some studies have attempted
to differentiate PCa with Gleason pattern ≤ 3 + 4 and ≥ 3 + 4 by diffusion-weighted
imaging (DWI) and advanced DWI.[8 ]
[9 ]
[10 ]
[11 ] Tamada et al[12 ] studied 457 PCa patients and results showed that the apparent diffusion coefficient
(ADC) value of PCa with Gleason pattern ≥ 4 + 3 was significantly lower than those
of PCa with Gleason pattern ≤ 3 + 4. Shan et al[13 ] used diffusion kurtosis imaging (DKI) and intravoxel incoherent motion imaging (IVIM)
to study 121 PCa patients and results showed that both DKI and IVIM have the capability
to differentially diagnose PCa between Gleason pattern ≤ 3 + 4 and Gleason pattern
≥ 3 + 4. To our knowledge, there is currently no research that has attempted to combine
IVIM, stretched exponential model, and DKI to distinguish NCCN risk stratification
of PCa.
The aim of this study was to investigate the feasibility and power of the combination
of DKI, IVIM, and stretched exponential model in the differentiation of PCa between
the FIRG and the un-FIRG.
Materials and Methods
Patients
This study was approved by the ethics committee of the Second Hospital of Dalian Medical
University, Dalian, People Republic of China. A total of 372 PCa patients underwent
prostate mpMRI from December 2018 to January 2023. In total, 131 patients were diagnosed
with PCa in the intermediate-risk group according to the risk stratification criteria
of the NCCN clinical practice guidelines of PCa.[4 ] Exclusion criteria: (1) patients underwent prostate biopsy (n = 7) within 6 weeks before prostate MRI (reducing postbiopsy changes, including hemorrhage
and inflammation, may adversely affect the interpretation of prostate MRI for staging);
(2) patients have comorbidities (such as tumor history), prostate treatment history
(such as transurethral resection prostate), and other underlying conditions (prostatitis
or abscess of the prostate), which could confound the results (n = 2); and (3) the prostate mpMRI have artifacts, which affect the assessment (n = 4). Finally, 118 PCa patients in the intermediate-risk group were enrolled in this
study, including 51 FIRG patients and 67 un-FIRG patients ([Fig. 1 ]).
Fig. 1 Flow diagram of the study population.
MRI Techniques
Every participant was subjected to a prostate MRI examination utilizing a 3.0 T MRI
system (GE Discovery MR 750W) equipped with an eight-channel phased-array coil. The
scanning sequences included axial T1 -weighted imaging (T1 WI), T2 -weighted imaging (T2 WI) (axial T2 WI, coronal T2 WI, and sagittal T2 WI), DWI, dynamic contrast-enhanced imaging (DCE), IVIM, and DKI. The scanning parameter
of DCE-MRI included: three-phase T1 WI plain scans with a rotation angle of 3°6°9° were performed before the DCE-MRI scanning,
and the other parameters were consistent with those of the DCE-MRI scanning parameters.
A high-pressure syringe was used to inject gadodiamide at 0.25 mL/kg body mass through
the dorsal vein, which was rinsed with 20 mL normal saline to ensure complete injection
of the drug into the bloodstream. DCE-MRI was performed in 48 continuous scans with
52 layers in each phase, and the scanning time was 7 minutes. Flip degree was 12°
and the number of incentives was 0.73. IVIM scanning parameters were: repetition time/echo
time (TR/TE), 3500 to 4400/87.5 ms; slice thickness, 3.6 mm; space, 0.5 mm; field
of view (FOV), 28 × 28 cm; acquisition matrix, 128 × 128; 11 b -values (b -values [number of excitations] 0 [1], 25 [1], 50 [1], 75 [1], 100 [4], 150 [4], 200
[6], 400 [8], 800 [10], 1200 [12], and 2000 [14] s/mm2 ) were employed. DKI scans utilized the following settings: TR/TE, 3800 to 4600/87.4 ms;
slice thickness, 3 mm; space, 0.5 mm; FOV, 28 × 28 cm; matrix, 128 × 128; with 15
gradient directions and three b -values (0, 1000, 2000 s/mm2 ), and the selection of b -values was based on the study results of Chou et al.[14 ] Details are shown in [Table 1 ].
Table 1
Acquisition parameters of the multiparametic MRI protocol
Sequence
TR/TE (ms)
Slice/Gap (mm)
FOV (cm2 )
Matrix
b (s/mm2 )
Direction
T2 WI
3200–3500/90–100
3/0.5
24 × 24
256 × 224
–
–
T1 WI
750–780/9–10
3/0.5
24 × 24
256 × 224
–
–
DWI
3500/70–75
3/0.5
28 × 28
128 × 128
0,2000
–
DCE-MRI
3.8/1.4
3/0.5
32 × 28
256 × 192
–
–
DKI
3800–4600/87.4
3/0.5
28 × 28
128 × 128
0, 1000, 2000
15
IVIM
3500–4400/87.5
3.6/0.5
28 × 28
128 × 128
0, 25, 50, 75, 100, 150, 200, 400, 800, 1200, 2000
Abbreviations: DCE-MRI, dynamic contrast-enhanced MRI; DKI, diffusion kurtosis imaging;
DWI, diffusion-weighted imaging; FOV, field of view; IVIM, intravoxel incoherent motion;
MRI, magnetic resonance imaging; T1 WI, T1 -weighted imaging; T2 WI, T2 -weighted imaging; TE, echo time; TR, repetition time.
Imaging and Quantitative Data Analysis
The evaluation of all prostate mpMRI adhered to the Prostate Imaging–Reporting and
Data System (PI-RADS) version 2 and subsequently version 2.1; PI-RADS version 2 was
employed as the evaluative framework prior to the advent of version 2.1.[15 ]
[16 ] Each image underwent independent appraisal by two veteran radiologists (J.B., 20
years, and D.M.L., 15 years of experience in urogenital imaging), with no prior exposure
to the patients' clinical narratives, laboratory diagnostics, or ancillary imaging
findings, including ultrasonography. Should discrepancies emerge in diagnostic conclusions,
the radiologists would jointly reassess the lesion to establish a consensus.
Quantitative data acquisition was executed using the GE AW 4.6 workstation. Three
regions of interest (ROIs) were drawn at the maximum three slices of each target lesions.
Each ROI was manually outlined around the whole lesion on the maximum three slices
by two radiologists (J.B., 20 years, and K.P.Z., 5 years of experience in urogenital
imaging) in consensus on the related parameter maps of IVIM (standard ADC map, diffusion
coefficient map, pseudo-diffusion coefficient map, and perfusion fraction map), stretched
exponential model (distribute diffusion coefficient [DDC] map and heterogeneity index
map), and DKI (fractional anisotropy [FA] map, mean diffusion [MD] map, and mean kurtosis
[MK] map) using T2 WI or ADC map as references (if the lesion was located in the peripheral zone, ADC
map was referred and if the lesion was located in the transitional zone, T2 WI was referenced). Average values were computed and employed for subsequent data
analysis. Detailed visualization is provided in [Fig. 2 ].
Fig. 2 Quantitative data acquisition using the GE AW 4.6 workstation (GE Healthcare, United
States), illustrated by the example of acquiring diffusion coefficient (D) values.
Step 1: Assessment of target lesions was performed using the Prostate Imaging–Reporting
and Data System (PI-RADS), version 2.1 criteria. Step 2: A region of interest (ROI)
was delineated on each of the three maximum slices of the target lesion; each ROI
was manually outlined around the whole lesion. Then, the diffusion (D) values for
each ROI were subsequently recorded individually. Step 3: Finally, calculation of
the mean D value across the target lesions was performed.
Pathology Acquisition and Analysis
All biopsies were conducted with the BK transperineal MRI/TRUS fusion biopsy system
(BK Medical, Denmark). Initially, three biopsy cores were taken from each targeted
lesion. Subsequently, patients received 18 to 24 systematic biopsies following the
Ginsburg protocol, which involves using a spring-loaded biopsy gun equipped with an
18-gauge needle. In this phase, two cores were sampled from each of the 12 sectors,
beginning with the anterior sectors. The procedures were performed by either of two
experienced urologists (K.G., 11 years, and Y.J.X., 13 years of experience in urogenital
pathology diagnosis).
Pathological assessment of each specimen was independently conducted by two pathologists,
who were not privy to the MRI outcomes. Discrepancies between the evaluations were
reconciled through consensus. The evaluative framework for the pathological analysis
adhered to the International Society of Urological Pathology (ISUP) consensus guidelines
established in 2014.[17 ] Each sample's pathological classification and Gleason grading were determined distinctly.
Statistical Analysis
The normality and homogeneity of variances were assessed using the Kolmogorov–Smirnov
test and Levene's test, respectively. To test whether the related parameters between
FIRG and un-FIRG were statistically significant, an independent sample t -test was used. Binary logistic regression was conducted to investigate the relationship
between DKI, IVIM, and stretched exponential model parameters and the likelihood of
un-FIRG in the intermediate-risk group. Following the binary logistic regression,
receiver operating characteristic (ROC) curve analysis was performed on the statistically
significant combination parameters to evaluate their diagnostic effectiveness in identifying
un-FIRG within the intermediate-risk group. A p -value of < 0.05 was deemed to indicate statistical significance. All statistical
analyses were executed using the SPSS software (IBM, Version 25).
Results
A total of 118 intermediate-risk group PCa patients were included, among which 51
were FIRG ([Fig. 3 ]), and 67 were un-FIRG ([Fig. 4 ]). The average age of patients in the FIRG and un-FIRG was 78.53 ± 7.87 (57–92) years
and 78.12 ± 7.00 (59–90) years, respectively (p > 0.05). The average total-PSA (T-PSA) of the un-FIRG (16.17 ± 2.39 ng/mL) was significantly
higher than that of the FIRG (13.50 ± 2.10 ng/mL) (p < 0.001). Conversely, the average free-PSA (F-PSA)/T-PSA ratio of the un-FIRG (0.14 ± 0.02)
was significantly lower than that of the FIRG (0.17 ± 0.02) (p < 0.001). In our study, the average F-PSA of the FIRG (2.33 ± 0.27 ng/mL) was higher
than that of the un-FIRG (2.28 ± 0.28 ng/mL), but has no statistical significance
(p = 0.323). Both the FIRG group PCa lesions (47.06%) and the un-FIRG group PCa lesions
(49.25%) were predominantly PI-RADS category 5, with no statistical significance among
PI-RADS categories of the two groups (p = 0.092, detail showed in [Table 2 ]). Pathologically, in the FIRG, 19.61% was grade group 1, and 80.39% was grade group
2, whereas in the un-FIRG, 5.97% was grade group 1, 19.40% was grade group 2, and
74.63% was grade group 3. In terms of clinical T staging, the percentage of T2c stage
patients in the FIRG (43.13%) was lower than that in the un-FIRG (55.22%). The detailed
clinical characteristics are shown in [Table 3 ].
Table 2
PI-RADS classification of PCa lesions according to PI-RADS V 2 and PI-RADS V 2.1
Category
According to PI-RADS V 2 (n = 21)
According to PI-RADS V 2.1 (n = 97)
p
FIRG (n = 8)
Un-FIRG (n = 13)
p
FIRG (n = 43)
Un-FIRG (n = 54)
p
PI-RADS 2
0.98
3
(2.5%)
2
(1.7%)
0.91
0.92
PI-RADS 3
1
(0.8%)
2
(1.7%)
8
(6.8%)
10
(8.5%)
PI-RADS 4
3
(2.5%)
5
(4.2%)
12
(10.2%)
15
(12.7%)
PI-RADS 5
4
(3.4%)
6
(5.1%)
20
(16.7%)
27
(22.9%)
Abbreviations: FIRG, favorable intermediate-risk group; PCa, prostate carcinoma; PI-RADS,
Prostate Imaging–Reporting and Data System; Un-FIRG, unfavorable intermediate-risk
group.
Table 3
Clinical characteristics of the patients
Variables
FIRG (n = 51)
Un-FIRG (n = 67)
p
Age (y)
78.53 ± 7.87 (57–92)
78.12 ± 7.00 (59–90)
0.766
T-PSA (ng/mL)
13.50 ± 2.10
16.17 ± 2.39
< 0.001
F-PSA (ng/mL)
2.33 ± 0.27
2.28 ± 0.28
0.323
F-PSA/T-PSA
0.17 ± 0.02
0.14 ± 0.02
< 0.001
Grade group
1 (n = 12)
2 (n = 39)
1 (n = 4)
2 (n = 19)
3 (n = 44)
Clinical T staging
cT2a (n = 5)
cT2b (n = 24)
cT2c (n = 22)
cT2b (n = 30)
cT2c (n = 37)
Abbreviations: FIRG, favorable intermediate-risk group; F-PSA, free-PSA; PSA, prostate-specific
antigen; T-PSA, total-PSA; Un-FIRG, unfavorable intermediate-risk group.
Fig. 3 A 76-year-old favorable intermediate-risk group (FIRG) prostate carcinoma (PCa) patient,
prostate-specific antigen (PSA) of 10.32 ng/mL, Gleason 3 + 3, and cT2a. (A –C ) The lesion with Prostate Imaging–Reporting and Data System (PI-RADS) 4 seen in the
left middle peripheral zone. (D ) Hematoxylin and eosin (HE) staining, ×100. (E ) Fractional anisotropy (FA) of 0.325. (F ) Mean diffusion (MD) of 0.897 × 10−3 mm2 /s. (G ) Mean kurtosis (MK) of 0.564. (H ) Standard apparent diffusion coefficient (ADC) of 0.627 × 10−3 mm2 /s. (I ) Diffusion coefficient (D) of 0.553 × 10−3 mm2 /s. (J ) Distribute diffusion coefficient (DDC) of 0.701 × 10−3 mm2 /s.
Fig. 4 A 79-year-old unfavorable intermediate-risk group (un-FIRG) prostate carcinoma (PCa)
patient, prostate-specific antigen (PSA) of 16.32 ng/mL, Gleason 4 + 3, and cT2b.
(A –C ) The lesion with Prostate Imaging–Reporting and Data System (PI-RADS) 5 seen in the
left middle peripheral zone. (D ) Hematoxylin and eosin (HE) staining, ×100. (E ) Fractional anisotropy (FA) of 0.424. (F ) Mean diffusion (MD) of 0.815 × 10−3 mm2 /s. (G ) Mean kurtosis (MK) of 0.652. (H ) Standard apparent diffusion coefficient (ADC) of 0.507 × 10−3 mm2 /s. (I ) Diffusion coefficient (D) of 0.451 × 10−3 mm2 /s. (J ) Distribute diffusion coefficient (DDC) of 0.539 × 10−3 mm2 /s.
Results of Independent Samples t -Test
Results of independent samples t -test showed that the parameters FA, MK, and MD of DKI, the parameters standard ADC
and diffusion coefficient (D) of IVIM, and the parameter DDC of stretched exponential
model have significant difference between the FIRG and un-FIRG ([Table 4 ]). FA value and MK value of the un-FIRG (0.413 ± 0.066, 0.637 ± 0.066) were significantly
higher than those of the FIRG (0.317 ± 0.067, 0.555 ± 0.079). On the contrary, the
MD value, standard ADC value, D value, and DDC value of the un-FIRG were significantly
lower than those of the FIRG. Compared with the FIRG, the MD value, standard ADC value,
D value, and DDC value of the un-FIRG were decreased by 0.067 × 10−3 mm2 /s, 0.101 × 10−3 mm2 /s, 0.109 × 10−3 mm2 /s, and 0.143 × 10−3 mm2 /s, respectively.
Table 4
Results of independent samples t -test
Variables
FIRG
Un-FIRG
t
p -Value
FA
0.317 ± 0.067
0.413 ± 0.066
−7.807
< 0.001
MK
0.555 ± 0.079
0.637 ± 0.066
−6.153
< 0.001
MD (×10−3 mm2 /s)
0.888 ± 0.133
0.821 ± 0.148
2.536
0.013
Standard ADC (×10−3 mm2 /s)
0.621 ± 0.122
0.520 ± 0.120
4.495
< 0.001
D (×10−3 mm2 /s)
0.555 ± 0.100
0.446 ± 0.957
6.016
< 0.001
DDC (×10−3 mm2 /s)
0.688 ± 0.111
0.545 ± 0.084
7.944
< 0.001
Abbreviations: D, pure diffusion coefficient; DDC, distributed diffusion coefficient;
FA, fractional anisotropy; FIRG, favorable intermediate-risk group; MD, mean diffusion;
MK, mean kurtosis; standard ADC, standard apparent diffusion coefficient; un-FIRG,
unfavorable intermediate-risk group.
Results of Binary Logistic Regression Analysis
Binary logistic regression analysis was used to analyze the relevant parameters of
IVIM, DKI, and stretched exponential models and construct a diagnostic model. The
results showed that the diagnostic model was statistically significant, chi-square = 90.969,
p < 0.001, which could effectively distinguish the 86.40% of un-FIRG in the intermediate-risk
group. Among the nine independent variables included in the model, the FA value (p < 0.001), MK value (p = 0.014), MD value (p = 0.031), D value (p = 0.041), and DDC value (p = 0.001) were statistically significant. Odds ratios (95% confidence interval [CI])
were 6.856 (2.679–17.545), 3.579 (1.292–10.013), 3.018 (1.107–8.227), 0.411 (0.175–0.962),
and 0.177 (0.062–0.510), respectively ([Table 5 ], [Fig. 5 ]). The results of ROC analysis showed that the area under the curve was 0.9429, 95%
CI: 0.9095–0.9805. Sensitivity, specificity, positive predictive value, and negative
predictive value were 88.06, 84.31, 88.06, and 84.31%, respectively.
Fig. 5 The binary logistic regression analysis results revealed that, in the intermediate-risk
group, the prevalence of unfavorable intermediate-risk group (un-FIRG) increased with
rising fractional anisotropy (FA) and mean kurtosis (MK) values but decreased as mean
diffusion (MD), diffusion coefficient (D), and distribute diffusion coefficient (DDC)
values increased. The diagnostic efficacy of these parameters in differentiating un-FIRG
from the intermediate-risk group was 0.9429.
Table 5
Prediction for un-FIRG PCa patients from intermediate-risk group
Variables
p -Value
Odds ratio (95% CI)
FA
< 0.001
6.856(2.679~17.545)
MK
0.014
3.579(1.292~10.013)
MD
0.031
3.018(1.107∼8.227)
D
0.041
0.411(0.175∼0.962)
DDC
0.001
0.177(0.062∼0.510)
Abbreviations: CI, confidence interval; D, pure diffusion coefficient; DDC, distributed
diffusion coefficient; FA, fractional anisotropy; MD, mean diffusion; MK, mean kurtosis;
PCa, prostate carcinoma; un-FIRG, unfavorable intermediate-risk group.
Discussion
In this study, a significant difference was found in the parameters of IVIM, stretching
exponential model, and DKI between the FIRG and un-FIRG. MD value, standard ADC value,
D value, and DDC value of the FIRG were significantly lower than those of the un-FIRG.
Results of our study are consistent with previous studies.[13 ]
[18 ]
[19 ] Parameters MD value of DKI, standard ADC value and D value of IVIM, and DDC value
of stretching exponential model reflect the diffusion restriction of water molecules
in tissues. Some studies have suggested that the cause of decrease of MD value, standard
ADC value, D value, and DDC value with increase of PCa Gleason pattern may be related
to histological differences, including vascular (i.e., capillaries), fibromuscular
stroma, epithelium, and glandular lumen.[13 ]
[18 ]
[19 ]
[20 ] PCa with higher Gleason pattern has higher cellularity and smaller extracellular
space, which leads to increased diffusion restriction of water molecules in the tissue.
According to the assessment criterion of PCa pathological tissue recommended by the
ISUP, the growth pattern of PCa tissue was divided into five grades by the Gleason
system, with the increase of Gleason pattern, the morphology of histiocytes become
more irregular, have higher cellularity, and the destruction of glandular structure
become more complete.[21 ] In addition, our results showed that the pathology of FIRG was mainly grade group
2 (Gleason 3 + 4), accounting for approximately 76.47%, in contrast, the pathology
of un-FIRG was mainly grade group 3 (Gleason 4 + 3), accounting for approximately
65.67%. Therefore, the reasonable explanation may be that compared with the FIRG,
the un-FIRG have higher Gleason pattern and the tissue contains increased volumes
of low-restriction diffusivity epithelial cells and decreased high-restriction diffusivity
stroma and lumen space. Those factors resulted in the un-FIRG having lower D value,
standard ADC value, D value, and DDC value than those of the FIRG. In fact, the diffusion
motion of water molecules in tissues is non-Gaussian, which is also reflected in our
research results, where the FA value and MK value of FIRG were significantly higher
than that of the un-FIRG. Our results are consistent with the results of previous
studies.[22 ]
[23 ]
[24 ] Results of Chatterjee et al[24 ] showed that the nuclear fraction, cytoplasmic fraction, and cellular fraction increase
with the increase of the Gleason pattern, while the stromal fraction and luminal fraction
have opposite correlation with the Gleason pattern. Those pathological differences
increased diffusion restriction of water molecules in tissues, while also causing
the diffusion restriction of water molecules deviating from normal distribution more
significant. As a result, those factors may be responsible for the significantly higher
FA value and MK value of the un-FIRG than the FIRG.
In our study, pseudo-diffusion coefficient (D*) value and perfusion fraction (f ) value have no statistical significance between the FIRG and un-FIRG. This is in
keeping with previous study.[20 ] It is known that the D* and f values mainly reflect the perfusion of the tissue.[25 ] Specifically, separation of perfusion from diffusion requires high signal-to-noise
ratios, and there are some technical challenges to overcome, such as artifacts from
other bulk flow phenomena. Vascular and tubular flow may be difficult to disentangle
in some tissues. Active transport resulting from glandular secretion (such as breast
ducts, salivary glands, and pancreas) may also be difficult to separate from microcapillary
perfusion. What is more, IVIM imaging has a differential sensitivity to vessel sizes,
according to the range of b -values that are used.25 Those factors may be responsible for the failure of D* value and f value to differentiate the FIRG and un-FIRG. Some studies have shown that the parameter
heterogeneity index (α ) value of the stretched exponential model has the ability to diagnose PCa.[26 ] However, results of our study showed that the α value has no statistical significance between the FIRG and un-FIRG. The plausible
explanation may be that compared with benign prostate tissue, PCa has relatively complex
microscopic structure (including higher tumor density, smaller glandular lumen, and
extracellular space, etc.), these factors result in α value to have the ability to differentiate PCa and benign prostatic hyperplasia.
However, in PCa tissue, the Gleason pattern, as an important indicator to evaluate
the aggressiveness of PCa, is based on the microstructure of PCa tissue (including
the relative proportion of luminal, epithelial, and stromal components), not just
tumor heterogeneity. Those microstructure differences between the FIRG and un-FIRG
may not be sufficient to be reflected by α value.
Binary logistic regression analysis was used to analyze the relevant parameters of
IVIM, DKI, and stretched exponential model to construct a diagnostic model, and the
results showed that the diagnostic efficiency was 0.9429. This result may be related
to the un-FIRG to have higher Gleason pattern than the FIRG. In our study, the pathology
of the FIRG were Gleason 3 + 3 and 3 + 4, of which Gleason 3 + 4 accounted for 80.39%,
while the pathology of the un-FIRG were Gleason 3 + 3, Gleason 3 + 4, and Gleason
4 + 3, and Gleason 4 + 3 accounted for 74.63%. Previous studies have observed that
compared with Gleason pattern 3 + 4 PCa, Gleason pattern 4 + 3 PCa has significantly
lower standard ADC value, D value, DDC value, and MD value, and have significantly
higher MK value and FA value,[27 ]
[28 ] and the results were confirmed in our study. Tamada et al[28 ] used DWI and DKI to differentiate Gleason 3 + 4 and Gleason 4 + 3 PCa, and the results
showed that the diagnostic efficacy was 0.721. In contrast, the results of our study
showed higher diagnostic efficacy, which may be related to the patients of Gleason
3 + 4 PCa (n = 113) being significantly more than Gleason 4 + 3 PCa (n = 48).
There are some limitations in our study. First, our study was a single-center study.
Second, in our study, the ROIs were manually drawn at the slice with the maximum slice
of PCa lesions, as well as the nearest levels above and below it. What is more, the
ROIs were manually outlined around the whole PCa lesions. The choice of different
ROIs might also lead to differences in study results due to tumor size and heterogeneity.
Finally, our study only analyzed the mean values of the relevant parameters, while
some research results showed that it may be a feasible method to use histogram analysis.[29 ]
Conclusion
In conclusion, the risk stratification modeling combining IVIM, stretched exponential
model, and DKI exhibited great potential for noninvasive risk stratification of intermediate-risk
group PCa, revealing significant discriminatory power.
