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At this stage, in order to further improve the standardization of magnetic resonance imaging (MRI) results for bladder cancer (BC), the Bladder Imaging Reporting and Data System (VI-RADS) has been introduced clinically, which focuses on identifying muscle infiltration Presence and extent
.
Since VI-RADS was proposed in 2018, many studies have validated its clinical utility and utility
.
However, dynamic contrast-enhanced imaging (DCEI) exists because dynamic contrast enhancement imaging (DCEI) is an integral part of traditional VI-RADS Potential limitations
.
Intravenous gadolinium-based contrast agents can rarely cause allergies, fibrosis of the nephrogenic system, renal failure, and gadolinium deposition
in brain tissue.
Therefore, there is a clinical need to establish an alternative reporting system
that does not use DCEI.
In addition, if DCEI can be reasonably omitted from VI-RADS, MRI examination time and medical costs will be greatly reduced
.
With the widespread clinical application of noise-reducing deep learning reconstruction (dDLR), high-gradient (HG) MRI scanners can produce thinner images at the same bandwidth without increasing scan time
.
The additional use of dDLR can retrospectively improve the signal-to-noise ratio (SNR)
of high-resolution MRI.
Recently, a study published in the journal European Radiology has developed a DCEI-free VI-RADS (NCE-VI-RADS) approach and evaluated the added value of dDLR to NCE-VI-RADS, providing support and reference
for further reducing the time of MRI examination and the medical cost of patients.
The study recruited 163 participants from January 2019 to December 2020, and each patient underwent a high-gradient 3T bladder MRI scan
。 A total of 108 participants
with pathologically confirmed bladder cancer through urethral resection were analyzed.
Tumors were independently assessed based on VI-RADS (scores 1-5) by two readers, two experienced radiologists (Reader 1) and one advanced radiology resident (Reader 2).
。 Traditional VI-RADS evaluation includes all three imaging types (T2-weighted imaging [T2WI], diffusion-weighted imaging [DWI], and dynamic contrast-enhanced imaging [DCEI]).
In addition, NCE-VI-RADS which includes only the type of non-scan image (T2WI and DWI), and "NCE-VI-RADS with dDLR"
including T2WI processed with dDLR and DWI, were evaluated.
All systems were evaluated
using ROC curve analysis and simple and/or weighted κ statistics.
Muscle aggression
was detected in 23/108 participants (21%).
The area under the curve (AUC) values for diagnosing muscle invasion are as follows: traditional VI-RADS, 0.
94 and 0.
91; NCE-VI-RADS, 0.
93 and 0.
91; and "NCE-VI-RADS and dDLR", 0.
96 and 0.
93, for readers 1 and 2
, respectively.
Simple κ statistics show that the two readers agree to a large extent for NCE-VI-RADS, and almost exactly the
same as traditional VI-RADS and "NCE-VI-RADS and dDLR".
The two readers performed ROC curve analysis
for the categories of muscle invasion by (a) T2WI, (b) DWI, (c) DCEI, and (d) traditional VI-RADS scores.
AUC, area under the curve; DCEI, dynamic contrast-enhanced imaging; DWI, diffusion-weighted imaging; ROC, receiver work characteristics; T2WI, T2 weighted imaging; VI-RADS, Bladder Imaging Reporting and Data System
In this prospective study using a 3T HG MRI scanner, the predictive accuracy of NCE-VI-RADS for muscle invasion in bladder cancer was comparable to that of conventional VI-RADS.
The additional use of dDLR further improves the diagnostic accuracy
of NCE-VI-RADS.
Original source:
Masanaka Watanabe,Satoru Taguchi,Haruhiko Machida,et al.
Clinical validity of non-contrast-enhanced VI-RADS: prospective study using 3-T MRI with high-gradient magnetic field.
DOI:10.
1007/s00330-022-08813-4