Introduction: This article focused on probing the correlation of magnetic resonance imaging (MRI) and computed tomography (CT) manifestations with biological factor expression and lymph node metastasis (LNM) in aggressive prostate cancer (PCa). Methods: A total of 136 PCa patients underwent surgical treatment and received CT and MRI examinations before surgery, whereby the apparent diffusion coefficient (ADC) values of quantitative MRI (qMRI) parameters were obtained. Patients were categorized into the non-aggressive PCa group and the aggressive PCa group according to the postoperative pathological results and Gleason scores. The expression of biological factors (prostate-specific antigen [PSA], proliferating cell nuclear antigen [PCNA], p27, and Ki-67) in both groups was tested. CT and MRI manifestations of aggressive PCa patients were analyzed. The qMRI parameters, biological factors levels, and LNM were compared in two groups; the relationships between CT and MRI manifestations, qMRI parameters, and positive expression of biological factors and LNM were probed in two groups. Results: In the aggressive PCa group, MRI and CT presented different degrees of abnormal prostate changes. In the aggressive PCa group, PSA and p27 expression and ADC values were lower, and PCNA and Ki-67, and LNM rates were higher. Patients’ LNM rate was higher than that of ≤2 cm when the tumor diameter was >2 cm. ADC values were positively correlated with PSA and p27 positive expression and negatively correlated with PCNA, Ki-67, and LNM in the aggressive PCa group. Conclusion: MRI and CT manifestations of aggressive PCa had certain characteristics; MRI manifestations and qMRI possessed a correlation with biological factors and LNM; ADC could be employed to assess the aggressiveness of PCa.

1.
Mai
CW
,
Chin
KY
,
Foong
LC
,
Pang
KL
,
Yu
B
,
Shu
Y
, et al
.
Modeling prostate cancer: what does it take to build an ideal tumor model
.
Cancer Lett
.
2022
;
543
:
215794
.
2.
Adamaki
M
,
Zoumpourlis
V
.
Prostate cancer biomarkers: from diagnosis to prognosis and precision-guided therapeutics
.
Pharmacol Ther
.
2021
;
228
:
107932
.
3.
Chinniah
S
,
Stish
B
,
Costello
BA
,
Pagliaro
L
,
Childs
D
,
Quevedo
F
, et al
.
Radiation therapy in oligometastatic prostate cancer
.
Int J Radiat Oncol Biol Phys
.
2022
;
114
(
4
):
684
92
.
4.
Thomsen
MK
,
Busk
M
.
Pre-clinical models to study human prostate cancer
.
Cancers
.
2023
;
15
(
17
):
4212
.
5.
Xin
S
,
Liu
X
,
Li
Z
,
Sun
X
,
Wang
R
,
Zhang
Z
, et al
.
ScRNA-seq revealed an immunosuppression state and tumor microenvironment heterogeneity related to lymph node metastasis in prostate cancer
.
Exp Hematol Oncol
.
2023
;
12
(
1
):
49
.
6.
Kang
Z
,
Luo
Y
,
Xiao
E
,
Li
Q
,
Wang
L
.
CD151 and prostate cancer progression: a review of current literature
.
Asia Pac J Clin Oncol
.
2023
;
19
(
4
):
434
8
.
7.
Uhr
A
,
Glick
L
,
Gomella
LG
.
An overview of biomarkers in the diagnosis and management of prostate cancer
.
Can J Urol
.
2020
;
27
(
S3
):
24
7
.
8.
Pianou
NK
,
Stavrou
PZ
,
Vlontzou
E
,
Rondogianni
P
,
Exarhos
DN
,
Datseris
IE
.
More advantages in detecting bone and soft tissue metastases from prostate cancer using (18)F-PSMA PET/CT
.
Hell J Nucl Med
.
2019
;
22
(
1
):
6
9
.
9.
Mendes
B
,
Domingues
I
,
Silva
A
,
Santos
J
.
Prostate cancer aggressiveness prediction using CT images
.
Life
.
2021
;
11
(
11
):
1164
.
10.
Fernandes
MC
,
Yildirim
O
,
Woo
S
,
Vargas
HA
,
Hricak
H
.
The role of MRI in prostate cancer: current and future directions
.
Magma
.
2022
;
35
(
4
):
503
21
.
11.
Murgic
J
,
Gregov
M
,
Mrčela
I
,
Budanec
M
,
Krengli
M
,
Fröbe
A
, et al
.
Mri-guided radiotherapy for prostate cancer: a new paradigm
.
Acta Clin Croat
.
2022
;
61
(
Suppl 3
):
65
70
.
12.
Awiwi
MO
,
Gjoni
M
,
Vikram
R
,
Altinmakas
E
,
Dogan
H
,
Bathala
TK
, et al
.
MRI and PSMA PET/CT of biochemical recurrence of prostate cancer
.
Radiographics
.
2023
;
43
(
12
):
e230112
.
13.
Fu
X
,
Jiang
B
,
Fu
J
,
Jia
J
.
MRI diagnosis and pathological examination of axillary lymph node metastasis in breast cancer patients
.
Contrast Media Mol Imaging
.
2022
;
2022
:
4519982
.
14.
Abbasy
L
,
Mohammadzadeh
A
,
Hasanzadeh
M
,
Ehsani
M
,
Mokhtarzadeh
A
.
Biosensing of prostate specific antigen (PSA) in human plasma samples using biomacromolecule encapsulation into KCC-1-npr-NH(2): a new platform for prostate cancer detection
.
Int J Biol Macromol
.
2020
;
154
:
584
95
.
15.
Liong
ML
,
Lim
CR
,
Yang
H
,
Chao
S
,
Bong
CW
,
Leong
WS
, et al
.
Blood-based biomarkers of aggressive prostate cancer
.
PLoS One
.
2012
;
7
(
9
):
e45802
.
16.
Tohi
Y
,
Kato
T
,
Sugimoto
M
.
Aggressive prostate cancer in patients treated with active surveillance
.
Cancers
.
2023
;
15
(
17
):
4270
.
17.
Vetrone
L
,
Fortunati
E
,
Castellucci
P
,
Fanti
S
.
Future imaging of prostate cancer: do we need more than PSMA PET/CT
.
Semin Nucl Med
.
2024
;
54
(
1
):
150
62
.
18.
Hagens
MJ
,
Luining
WI
,
Jager
A
,
Donswijk
ML
,
Cheung
Z
,
Wondergem
M
, et al
.
The diagnostic value of PSMA PET/CT in men with newly diagnosed unfavorable intermediate-risk prostate cancer
.
J Nucl Med
.
2023
;
64
(
8
):
1238
43
.
19.
Borrelli
P
,
Larsson
M
,
Ulén
J
,
Enqvist
O
,
Trägårdh
E
,
Poulsen
MH
, et al
.
Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival
.
Clin Physiol Funct Imaging
.
2021
;
41
(
1
):
62
7
.
20.
Yang
G
,
Yang
F
,
Zhang
F
,
Wang
X
,
Tan
Y
,
Qiao
Y
, et al
.
Radiomics profiling identifies the value of CT features for the preoperative evaluation of lymph node metastasis in papillary thyroid carcinoma
.
Diagnostics
.
2022
;
12
(
5
):
1119
.
21.
Jung
Y
,
Cackowski
FC
,
Yumoto
K
,
Decker
AM
,
Wang
Y
,
Hotchkin
M
, et al
.
Abscisic acid regulates dormancy of prostate cancer disseminated tumor cells in the bone marrow
.
Neoplasia
.
2021
;
23
(
1
):
102
11
.
22.
Saito
S
,
Koyama
Y
,
Ueda
J
,
Hashido
T
.
Relationship between apparent diffusion coefficient distribution and cancer grade in prostate cancer and benign prostatic hyperplasia
.
Diagnostics
.
2022
;
12
(
2
):
525
.
23.
Noto
B
,
Eveslage
M
,
Auf der Springe
K
,
Exler
A
,
Faldum
A
,
Heindel
W
, et al
.
Robustness of apparent diffusion coefficient-based lymph node classification for diagnosis of prostate cancer metastasis
.
Eur Radiol
.
2024
;
34
(
7
):
4504
15
.
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