GENITOURINARY IMAGING P rostate cancer (PCa) is the fourth most commonly diagnosed cancer and ranked eighth among the leading causes of cancer-related deaths worldwide (1). The aggressiveness of PCa is indicated by the Gleason score (GS), which is important for guiding management and predicting clinical outcome (2). Previous studies have demonstrated that GS is closely associated with pathologic characteristics in PCa. For instance, the proliferation of epithelial cells and tumor cells and reduced stroma and lumen volumes are hallmarks of high-risk PCa (3).Multiparametric MRI, especially diffusion MRI, plays an indispensable role, given its high sensitivity for PCa detection (4,5). The diffusion MRI-derived apparent diffusion coefficient (ADC) is traditionally considered to reflect the cellularity of tumors based on empirical observations (3). However, ADC is only an overall measure of the water diffusivity that is determined by multiple microstructural features, such as the intra-and extracellular space, cell size, permeability, and intrinsic diffusivity. More specific imaging markers for characterizing specific tumor microstructure are urgently needed for precise description of the pathologic characteristics of PCa.Progress in multidimensional diffusion MRI has enhanced our capacity to probe tissue microstructure by using different diffusion weightings (q-space) and varying diffusion times (t-space) (6), opening new avenues in PCa research (7-10). Particularly, the recent development of time-dependent diffusion MRI has demonstrated unique advantages in depicting cellular microstructures by characterizing the diffusion time dependence of restricted water diffusion and relating the diffusion time dependence to specific microstructural parameters (11,12). Background: Recently developed time-dependent diffusion MRI has potential in characterizing cellular tissue microstructures; however, its value in imaging prostate cancer (PCa) remains unknown. Purpose: To investigate the feasibility of time-dependent diffusion MRI-based microstructural mapping for noninvasively characterizing cellular properties of PCa and for discriminating between clinically significant PCa and clinically insignificant disease. Materials and Methods:Men with a clinical suspicion of PCa were enrolled prospectively between October 2019 and August 2020. Time-dependent diffusion MRI data were acquired with pulsed and oscillating gradient diffusion MRI sequences at an equivalent diffusion time of 7.5-30 msec on a 3.0-T scanner. Time-dependent diffusion MRI-based microstructural parameters, including cell diameter, intracellular volume fraction, cellularity, and diffusivities, were estimated with a two-compartment model. These were compared for different International Society of Urological Pathology grade groups (GGs), and their performance in discriminating clinically significant PCa (GG .1) from clinically insignificant disease (benign and GG 1) was determined with a linear discriminant analysis. The fitted microstructural parameters were validate...
Diffusion-time- ( td) dependent diffusion MRI (dMRI) extends our ability to characterize brain microstructure by measuring dMRI signals at varying td. The use of oscillating gradient (OG) is essential for accessing short td but is technically challenging on clinical MRI systems. This study aims to investigate the clinical feasibility and value of td-dependent dMRI in neonatal hypoxic-ischemic encephalopathy (HIE). Eighteen HIE neonates and six normal term-born neonates were scanned on a 3 T scanner, with OG-dMRI at an oscillating frequency of 33 Hz (equivalent td ≈ 7.5 ms) and pulsed gradient (PG)-dMRI at a td of 82.8 ms and b-value of 700 s/mm2. The td-dependence, as quantified by the difference in apparent diffusivity coefficients between OG- and PG-dMRI (ΔADC), was observed in the normal neonatal brains, and the ΔADC was higher in the subcortical white matter than the deep grey matter. In HIE neonates with severe and moderate injury, ΔADC significantly increased in the basal ganglia (BG) compared to the controls (23.7% and 10.6%, respectively). In contrast, the conventional PG-ADC showed a 12.6% reduction only in the severe HIE group. White matter edema regions also demonstrated increased ΔADC, where PG-ADC did not show apparent changes. Our result demonstrated that td-dependent dMRI provided high sensitivity in detecting moderate-to-severe HIE.
Background Gliomas are the most common type of central nervous system tumors in children, and the combination of histological and molecular classification is essential for prognosis and treatment. Here, we proposed a newly developed microstructural mapping technique based on diffusion-time-dependent diffusion MRI (td-dMRI) theory to quantify tumor cell properties, and tested these microstructural markers in identifying histological grade and molecular alteration of H3K27. Methods This prospective study included 69 pediatric glioma patients aged 6.14±3.25 years old, who underwent td-dMRI with pulsed and oscillating gradient diffusion sequences on a 3T scanner. dMRI data acquired at varying tds were fitted into a two-compartment microstructural model to obtain intracellular fraction (fin), cell diameter, cellularity, etc. Apparent diffusivity coefficient (ADC) and T1 and T2 relaxation times were also obtained. H&E stained histology was used to validate the estimated microstructural properties. Results For histological classification of low- and high-grade pediatric gliomas, the cellularity index achieved the highest area under the receiver-operating-curve (AUC) of 0.911 among all markers, while ADC, T1, and T2 showed AUCs of 0.906, 0.885, and 0.886. For molecular classification of H3K27-altered glioma in 39 midline glioma patients, cell diameter showed the highest discriminant power with an AUC of 0.918, and the combination of cell diameter and extracellular diffusivity further improved AUC to 0.929. The td-dMRI estimated fin correlated well with the histological groundtruth with r=0.7. Conclusions The td-dMRI-based microstructural properties outperformed routine MRI measurements in diagnosing pediatric gliomas, and the different microstructural features showed complimentary strength in histological and molecular classifications.
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