Synopsis

Keywords: Prostate, Prostate, Prostate Cancer, Multiparametric MRI, New MR Sequences

This educational abstract will go through the more recent MRI pulse sequences that are being developed for the early detection and better characterization of prostate cancer (PCa). By incorporating cutting-edge multiparametric MR imaging (mpMRI) approaches into the diagnostic workup, it is possible to address current challenges with serum prostate-specific antigen (PSA) level based screening, problems with overdiagnosis with random biopsy, and multifocality of the PCa. Due to high negative predictive value of mpMRI, it not only improves the diagnosis of clinically significant PCa but also aids in lowering the number of unnecessary biopsies.

Abstract

Prostate cancer (PCa) has long been associated to inter- and intra-tumoral heterogeneity ¹. Other problems with the current PCa diagnostic pathway include the potential for overdiagnosis and overtreatment of indolent tumors due to challenges associated with serum prostate-specific antigen (PSA)-based screening and standard transrectal ultrasound (TRUS)-biopsies.
Multiparametric MRI (mpMRI) has become a key diagnostic method for PCa risk stratification, detection of transition zone (TZ) tumors, detection of clinically significant PCa, and PCa staging over the past ten years ². A high-resolution T2-weighted (T2W)-MRI (transverse, coronal, and sagittal orientations), diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI, and/or magnetic resonance spectroscopic imaging (MRSI) are some of the regularly utilized prostate mpMRI sequences. The most recent Cochrane meta-analysis, PROMIS trials, MRI-FIRST trials, and other studies back up the assertion that MRI provides the best diagnostic accuracy for identifying clinically significant PCa when compared to the standard TRUS-guided biopsy ^2-5. Despite advancements, mpMRI still has inter-observer variability in the interpretation of mpMRI, high cost, inconsistent image quality, and a moderate level of specificity in the TZ PCa.
As a result of these concerns, various groups have been developing new MRI methodologies and sequences to improve PCa diagnosis. The purpose of this educational exhibit is to provide a brief overview of novel MR sequences as well as various emerging methods for the treatment and diagnosis of PCa.

Novel prostate MR sequences and descriptions:
Intravoxel Incoherent Motion (IVIM) MR Imaging for PCa
Two non-exchanging compartments, vascular (water in the capillaries or blood vessels) and non-vascular, are used in this potential diffusion modeling technique (water in and around cells). When evaluated at low b-values (such as 0-100 s/mm²), the IVIM model provides additional information in the signal equation due to perfusion which is shown to be useful in PCa ⁶.

Prostate MRI Restriction Spectrum Imaging (RSI)
The RSI technique is one of the advanced diffusion-based techniques that use a multishell diffusion acquisition with a range of b-values as part of its DW modeling process. The RSI can quantify non-Gaussian diffusion in tissue microstructures using a linear mixture model ^{7, 8}. RSI is used to isolate signals from highly restricted and isotropic water in PCa and can provide information about nuclear volume fraction, cellular size, etc.

VERDICT MRI for PCa
The Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumors (VERDICT) assigns the DWI signal to three principal components: (a) intracellular water (S1), (b) water in the extracellular extravascular space (S2), and (c) vascular water in the capillary network (S3) ⁹. This is done by fitting the diffusion models with different diffusion times and diffusion weightings. VERDICT provides quantitative information regarding cell density, size of cells, intra- and extracellular volume fractions, as well as pseudo-diffusivity associated with blood flow, which are not provided by ADC and IVIM models.

Hyperpolarized magnetic resonance imaging (HP ¹³C-MRI)
It is possible to detect [1-13C] lactate production in PCa through hyperpolarized magnetic resonance imaging (HP ¹³C-MRI) following intravenous administration of hyperpolarized [1-13C] pyruvate. The polarization is transferred from an electron to the target molecule via dynamic nuclear polarization, the predominant hyperpolarization technique, at extremely low temperatures and high magnetic fields ^{10, 11}. C-13 probes are magnetized by hyperpolarized (HP) ¹³C MRI, which also offers unique metabolic data pertinent to heart and cancer disorders.

T2 maps and Luminal water imaging (LWI)
Several T2W images taken at various echo times can be combined to create T2 maps. To address the subjective nature of T2W, quantitative T2 maps can be helpful. Sabouri et al. developed LWI through the use of a three-dimensional multi-echo spin-echo technique ¹². To measure different T2 components, the signal was fitted to an exponential multi-exponential function (using regularized non-negative least squares). By using this technique, luminal water fraction (LWF), which denotes the fractional volume of the luminal space, may be determined ¹².

Hybrid multi-dimensional MRI (HM-MRI)
The prostate tissue is made up of three distinct components: epithelium, stroma, and lumen. The differential diffusivity of these glandular divisions can provide insight into a number of prostatic disorders ¹³. Assessing changes in ADC and T2 values in response to variations in time to echo and b-values is the basis of HM-MRI. Additionally, prostatic tissue compositions can be non-invasively evaluated using HM-MRI by utilizing a mathematical model ¹⁴.

Amide proton transfer (APT) MR imaging
CEST is a molecular imaging technique that measures the proton exchange between bulk water and proton in smaller metabolites ¹⁵. APT MRI has the potential to detect PCa. MR Fingerprinting MR fingerprinting (MRF) allows for the simultaneous measurement of T1, T2, and proton density quantitative maps ¹⁶. The distinctive nature of tissue is provided by the signal evolution or "fingerprints".

There has been tremendous progress made in the field of image analysis in addition to improvements in MR sequences. In the field of radiomics and radiogenomics, and advances in artificial intelligence, including machine learning and deep learning, are increasingly being used for the reconstruction, segmentation, and characterization of PCa lesions ^{17, 18}. It is necessary that these novel methods be prospectively validated by collecting high-quality data and reproducible results prior to their incorporation into prostate mpMRI and clinical practice.

Acknowledgements

No acknowledgement found.