Types Of Magnetic Resonance Imaging Research Articles

Multi-Slice Generation sMRI and fMRI for Autism Spectrum Disorder Diagnosis Using 3D-CNN and Vision Transformers.

Researchers have explored various potential indicators of ASD, including changes in brain structure and activity, genetics, and immune system abnormalities, but no definitive indicator has been found yet. Therefore, this study aims to investigate ASD indicators using two types of magnetic resonance images (MRI), structural (sMRI) and functional (fMRI), and to address the issue of limited data availability. Transfer learning is a valuable technique when working with limited data, as it utilizes knowledge gained from a pre-trained model in a domain with abundant data. This study proposed the use of four vision transformers namely ConvNeXT, MobileNet, Swin, and ViT using sMRI modalities. The study also investigated the use of a 3D-CNN model with sMRI and fMRI modalities. Our experiments involved different methods of generating data and extracting slices from raw 3D sMRI and 4D fMRI scans along the axial, coronal, and sagittal brain planes. To evaluate our methods, we utilized a standard neuroimaging dataset called NYU from the ABIDE repository to classify ASD subjects from typical control subjects. The performance of our models was evaluated against several baselines including studies that implemented VGG and ResNet transfer learning models. Our experimental results validate the effectiveness of the proposed multi-slice generation with the 3D-CNN and transfer learning methods as they achieved state-of-the-art results. In particular, results from 50-middle slices from the fMRI and 3D-CNN showed a profound promise in ASD classifiability as it obtained a maximum accuracy of 0.8710 and F1-score of 0.8261 when using the mean of 4D images across the axial, coronal, and sagittal. Additionally, the use of the whole slices in fMRI except the beginnings and the ends of brain views helped to reduce irrelevant information and showed good performance of 0.8387 accuracy and 0.7727 F1-score. Lastly, the transfer learning with the ConvNeXt model achieved results higher than other transformers when using 50-middle slices sMRI along the axial, coronal, and sagittal planes.

MiRNA-520a-3p combined with folic acid conjugated Fe2O3@PDA multifunctional nanoagents for MR imagine and antitumor gene-photothermal therapy

Osteosarcoma (OS) is a primary malignant bone tumor that occurs mainly in adolescents. Researchers are devoting to develop combination therapy methods in a multifunctional nanoplatform for the treatment of osteosarcoma. The results of previous research have shown that up-regulation of miR-520a-3p could induce anticancer effects in osteosarcoma. In order to improve the effect of gene therapy (GT), we attempted to carry miR-520a-3p in a multifunctional vector for comprehensive therapy. Fe2O3 is a type of magnetic resonance imaging (MRI) contrast that is widely used as a drug delivery agent. When coated with polydopamine (PDA), it can also be used as a photothermal therapy (PTT) agent (Fe2O3@ PDA). To deliver nanoagents targeted to a tumor site, folic acid (FA) conjugated with Fe2O3 @PDA was manufactured as FA-Fe2O3@PDA. FA was chosen as the target molecule to enhance utilization and reduce toxicity of nanoparticles. However, the therapeutic efficacy of FA-Fe2O3-PDA combined with miR-520a-3p has not yet been studied. In this study, we synthesized FA-Fe2O3@PDA-miRNA and investigated the potential of combining PDA regulated PTT and miR-520a-3p regulated GT to kill osteosarcoma cells. The results indicated that down-regulation of interleukin 6 receptor (IL6R) by miR-520a-3p and the photothermal ability of PDA could induce satisfactory anticancer effects in osteosarcoma, and the curative ratio was better than that used alone PTT or GT. Moreover, as a kind of T 2 magnetic contrast, miRNA-Fe2O3@PDA-FA can be used for MRI. These findings indicated that miRNA-Fe2O3@PDA-FA is an effective anti-tumor nanovector for PTT combined with GT.

Detection of Aspartylglucosaminuria Patients from Magnetic Resonance Images by a Machine-Learning-Based Approach.

Magnetic resonance (MR) imaging data can be used to develop computer-assisted diagnostic tools for neurodegenerative diseases such as aspartylglucosaminuria (AGU) and other lysosomal storage disorders. MR images contain features that are suitable for the classification and differentiation of affected individuals from healthy persons. Here, comparisons were made between MRI features extracted from different types of magnetic resonance images. Random forest classifiers were trained to classify AGU patients (n = 22) and healthy controls (n = 24) using volumetric features extracted from T1-weighted MR images, the zone variance of gray level size zone matrix (GLSZM) calculated from magnitude susceptibility-weighted MR images, and the caudate-thalamus intensity ratio computed from T2-weighted MR images. The leave-one-out cross-validation and area under the receiver operating characteristic curve were used to compare different models. The left-right-averaged, normalized volumes of the 25 nuclei of the thalamus and the zone variance of the thalamus demonstrated equal and excellent performance as classifier features for binary organization between AGU patients and healthy controls. Our findings show that texture-based features of susceptibility-weighted images and thalamic volumes can differentiate AGU patients from healthy controls with a very low error rate.

Performance Evaluation of MRI Based on Newly Developed MRiLab Simulation Using Inversion Recovery Pulse Sequence with Various Inversion Times

Among the representative types of magnetic resonance imaging (MRI) sequences, inversion recovery (IR) can improve the ability to detect brain lesions. The purpose of this study was to confirm the MRI characteristics according to TI changes in IR sequences. In this study, the MRiLab simulation program a newly developed, well-validated program was used to quantitatively analyze image characteristics with respect to the TI value in the IR sequence. Brain tissue phantom and standard phantom images were acquired by changing the TI at 100-ms intervals from 100 to 2,500 ms. In brain tissue phantom images, signal intensity (SI) values showed the lowest signals at TI values of 400, 700, and 2,500 ms in the white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), respectively. The contrast evaluated in CSF-WM was superior at 400 ms and the lowest at 1,800 ms. In addition, the contrast evaluated in WM-GM was superior at 400 ms and the lowest at 1,500 ms. In the case of the brain standard phantom, the SI and contrast showed the same tendency as brain tissue phantom. In conclusion, an appropriate TI value was derived for obtaining images with excellent SI and contrast between brain tissues using a newly developed simulation program.

Comparison of automatic liver volumetry performance using different types of magnetic resonance images

Measurements of liver volume from MR images can be valuable for both clinical and research applications. Automated methods using convolutional neural networks have been used successfully for this using a variety of different MR image types as input. In this work, we sought to determine which types of magnetic resonance images give the best performance when used to train convolutional neural networks for liver segmentation and volumetry.Abdominal MRI scans were performed at 3 Tesla on 42 adolescents with obesity. Scans included Dixon imaging (giving water, fat, and T2* images) and low-resolution T2-weighted scout images. Multiple convolutional neural network models using a 3D U-Net architecture were trained with different input images. Whole-liver manual segmentations were used for reference. Segmentation performance was measured using the Dice similarity coefficient (DSC) and 95% Hausdorff distance. Liver volume accuracy was evaluated using bias, precision, intraclass correlation coefficient, normalized root mean square error (NRMSE), and Bland-Altman analyses.The models trained using both water and fat images performed best, giving DSC = 0.94 and NRMSE = 4.2%. Models trained without the water image as input all performed worse, including in participants with elevated liver fat. Models using the T2-weighted scout images underperformed the Dixon-based models, but provided acceptable performance (DSC ≥ 0.92, NMRSE ≤6.6%) for use in longitudinal pediatric obesity interventions. The model using Dixon water and fat images as input gave the best performance, with results comparable to inter-reader variability and state-of-the-art methods.

Instrumental validation of free water, peak-width of skeletonized mean diffusivity, and white matter hyperintensities: MarkVCID neuroimaging kits.

IntroductionTo describe the protocol and findings of the instrumental validation of three imaging‐based biomarker kits selected by the MarkVCID consortium: free water (FW) and peak width of skeletonized mean diffusivity (PSMD), both derived from diffusion tensor imaging (DTI), and white matter hyperintensity (WMH) volume derived from fluid attenuation inversion recovery and T1‐weighted imaging.MethodsThe instrumental validation of imaging‐based biomarker kits included inter‐rater reliability among participating sites, test–retest repeatability, and inter‐scanner reproducibility across three types of magnetic resonance imaging (MRI) scanners using intra‐class correlation coefficients (ICC).ResultsThe three biomarkers demonstrated excellent inter‐rater reliability (ICC >0.94, P‐values < .001), very high agreement between test and retest sessions (ICC >0.98, P‐values < .001), and were extremely consistent across the three scanners (ICC >0.98, P‐values < .001).DiscussionThe three biomarker kits demonstrated very high inter‐rater reliability, test–retest repeatability, and inter‐scanner reproducibility, offering robust biomarkers suitable for future multi‐site observational studies and clinical trials in the context of vascular cognitive impairment and dementia (VCID).

Magnetic Resonance Arthrography of the Wrist: The Impact on Operative Management of Wrist Injuries.

Background There is no consensus on the utility of arthrography in the evaluation of wrist injuries. This study evaluates ordering trends of different types of magnetic resonance imaging (MRI) of the wrist and compares rates of surgery following these imaging modalities. Methods A national claims-based database was used to identify patients who underwent MRI within 90 days of a first-instance diagnosis of wrist injury from 2010 to 2018. The utilization of MRI without intravenous (IV) contrast, MRI with IV contrast, and MRI with arthrogram was investigated. The instances of operative procedures of the wrist within 1 year of MRI study were recorded. Patient demographics, comorbidities, type of operative procedure, and ordering physician specialty were obtained. Logistic regression analysis was used to evaluate the utilization of MRI and subsequent 1-year operative intervention rates as well as association of patient-related factors. Results Magnetic resonance arthrography use was associated with higher rates of subsequent operative treatment. Surgeons were more likely to order an arthrogram at the time of MRI. Younger patients were more likely to undergo MRI-based advanced imaging. Conclusion Surgeons may perceive MRA of the wrist to play an important role in operative decision-making following wrist injury. Level of Evidence This is a Level III, retrospective cohort study.

Neuroimaging methods for assessing the brain in diabetes mellitus (literature review)

Diabetes mellitus (DM) is associated with changes in the structure of the brain and deterioration of cognitive functions from mild to moderate according to neuropsychological testing. With the growing DM epidemic and the increasing number of people living to old age, cognitive dysfunctions associated with DM can have serious consequences for the future of public and practical health. Chronic hyperglycemia, severe episodes of hypoglycemia, and microvascular complications are important risk factors common for type 1 and type 2 diabetes. DM is also associated with structural and functional changes in the brain, which can be diagnosed by various types of magnetic resonance imaging (MRI) of the brain. In this review, we investigate studies conducted over the past two decades to improve the understanding of how DM effects the brain function and structure. We also describe the changes characteristic of type 1 and type 2 diabetes during standard MRI, functional MRI and proton magnetic-resonance spectroscopy (proton MRS) as well as their features.

Evaluation of MRI images’ pixels intensity in three different MRI sequences

Numerous types of Magnetic Resonance Imaging (MRI) sequence have been utilized for in vivo tibiofemoral contact area study has led inconsistency and disproportionate results. Thus, this study aim is to assess intensity differences of MRI images in three different MRI sequences. Ten healthy subjects with average age of 25.5 ± 4.76 consisted three males and seven females have no history of knee injuries participated in this study. Subjects were scanned through an Achieva 3.0T TX coupled with a SENSE spine coil 15. The selected MRI sequences were Turbo Spin Echo (TSE), Fast Field Echo (FFE) and Steady State Free Precision (SSFP). The results showed that the SSFP sequence consistently uncovered extra contact areas than the FFE and TSE sequences. Overall intensity value indicated that the TSE sequence intersected the highest intensity differences at important knee tissue components: trabecular-cortical and cartilage-synovial. The study demonstrated that the TSE sequence yields an accurate contact length detection which promoted a consistent tibiofemoral contact area for quantification. Also, this study suggested that the TSE sequence is a proper MRI sequence selection for in vivo tibofemoral contact area study.

Efficient Regularized Field Map Estimation in 3D MRI.

Magnetic field inhomogeneity estimation is important in some types of magnetic resonance imaging (MRI), including field-corrected reconstruction for fast MRI with long readout times, and chemical shift based water-fat imaging. Regularized field map estimation methods that account for phase wrapping and noise involve nonconvex cost functions that require iterative algorithms. Most existing minimization techniques were computationally or memory intensive for 3D datasets, and are designed for single-coil MRI. This paper considers 3D MRI with optional consideration of coil sensitivity, and addresses the multi-echo field map estimation and water-fat imaging problem. Our efficient algorithm uses a preconditioned nonlinear conjugate gradient method based on an incomplete Cholesky factorization of the Hessian of the cost function, along with a monotonic line search. Numerical experiments show the computational advantage of the proposed algorithm over state-of-the-art methods with similar memory requirements.

Diffusion tensor imaging denoising based on Riemann nonlocal similarity

Diffusion tensor imaging (DTI) is a non-invasive magnetic resonance imaging technique and a special type of magnetic resonance imaging, which has been widely used to study the diffusion process in the brain. The signal-to-noise ratio of DTI data is relatively low, the shape and direction of the noisy tensor data are destroyed. This limits the development of DTI in clinical applications. In order to remove the Rician noise and preserve the diffusion tensor geometry of DTI, we propose a DTI denoising algorithm based on Riemann nonlocal similarity. Firstly, DTI tensor is mapped to the Riemannian manifold to preserve the structural properties of the tensor. The Riemann similarity measure is used to search for non-local similar blocks to form similar patch groups. Then the Gaussian mixture model is used to learn the prior distribution of patch groups. Finally, the noisy patch group is denoised by Bayesian inference and the denoised patch group is reconstructed to obtain the final denoised image. The denoising experiments of real and simulated DTI data are carried out to verify the feasibility and effectiveness of the proposed algorithm. The experimental results show that our algorithm not only effectively removes the Rician noise in the DTI image, but also preserves the nonlinear structure of the DTI image. Comparing to the existing denoising algorithms, our algorithm has better improvement of the principal diffusion direction, lower absolute error of fractional anisotropy and higher peak signal-to-noise ratio.

Diffusion Tensor Imaging Denoising Based on Riemannian Geometric Framework and Sparse Bayesian Learning

Diffusion tensor imaging (DTI) is a special type of magnetic resonance imaging (MRI), which is the only noninvasive method that can effectively observe and trace the white matter fiber bundles of the brain. In the imaging process, the signal-to-noise ratio (SNR) of the MR image is low due to the influence of Rician noise. And it leads to processing difficultly by existing algorithms, which limits the development of DTI in clinical applications. In order to remove the Rician noise and preserve the diffusion tensor geometry of DTI, we propose a DTI denoising algorithm based on Riemannian geometric framework and sparse Bayesian learning. Firstly, DTI tensor is mapped to the Riemannian manifold to preserve the structural properties of the tensor. And then, sparse Bayesian learning method is used to reconstruct the noise-free DTI. The experimental results for synthetic or real DTI data show that the proposed algorithm effectively removes the Rician noise in the DTI as well as preserves the nonlinear structure of the DTI. Comparing with the existing denoising algorithm, the proposed algorithm has better denoising performance.

A novel MRI- and CT-based scoring system to differentiate malignant from osteoporotic vertebral fractures in Chinese patients

BackgroundVarious types of magnetic resonance imaging (MRI) and computed tomography (CT) findings are used to differentiate malignant vertebral fractures (MVFs) from osteoporotic vertebral fractures (OVFs). The distinguishing ability of any single finding is limited. This study developed a novel scoring system that integrates multiple MRI and CT signs for improved accuracy of differential diagnosis between MVFs and OVFs.MethodsA total of 150 MVFs and 150 OVFs in thoracolumbar vertebrae were analyzed. MRI and CT images were obtained within 2 months of the probable time of fracture. The sensitivity and specificity of 15 MRI and CT image findings were evaluated. A stepwise discriminant analysis using these signs as variables was used to create a scoring system to differentiate MVFs from OVFs.ResultsAll 15 image findings had strong specificity and moderate sensitivity. Seven MRI and three CT image findings were selected and assigned integral values in the final scoring system. A total score of 4 or greater points indicated MVF, whereas a total score of 3 or fewer points indicated OVF. The classification accuracy was 98.3% in the test set.ConclusionsThis novel scoring system using MRI and CT radiologic findings to differentiate MVFs from OVFs in Chinese patients was efficient with high accuracy and good applicability.

A Preliminary Report Requiring Continuation of Research to Confirm Fallopian Tube Adenocarcinoma: A Non-Experimental, Non-Randomized, Cross-Sectional Study.

BackgroundTransvaginal ultrasound has fair characteristics, and pathology is an invasive technique for fallopian tube tumor diagnosis. Magnetic resonance images have better intra- and inter-observer reliabilities for detection of primary fallopian tube malignant tumor(s) than the other diagnostic modalities. The purpose of this study was to investigate parameters of different types of magnetic resonance images for women with fallopian tube adenocarcinoma and to compare these parameters with the FIGO grading system to improve the accuracy of diagnosis and prognosis.Material/MethodsA total of 121 women who had clinically-proven fallopian tube adenocarcinoma were included in this cross-sectional study. A 3.0 T magnetic resonance images system was used for spin-lattice relaxation-weighted (T1WI), spin-spin relaxation-weighted (T2WI), diffusion-weighted, (DWI), and apparent diffusion coefficient (ADC) images. ANOVA following Tukey post hoc tests and Spearman rank correlation were performed at 99% confidence level.ResultsAxial T1WI, axial T2WI, and axial DWI, were provided low, intermediate, and high fluid signal intensity, respectively, for a tumor. Sagittal T1WI showed contrast uptake by the mass with necrosis. Sagittal T2WI showed a solid mass with well-defined walls. Sagittal DWI showed restriction to diffusion. ADC values were significantly higher for FIGO grade 1 women than for FIGO grade 3 women (p<0.0001, q=16.591). The Spearman correlation coefficient was 0.1012 between mean ADC and FIGO grading.ConclusionsWe recommend that magnetic resonance images be included in the FIGO guideline for grading of malignancies in the female genital tract.

Linked 4-Way Multimodal Brain Differences in Schizophrenia in a Large Chinese Han Population

Multimodal fusion has been regarded as a promising tool to discover covarying patterns of multiple imaging types impaired in brain diseases, such as schizophrenia (SZ). In this article, we aim to investigate the covarying abnormalities underlying SZ in a large Chinese Han population (307 SZs, 298 healthy controls [HCs]). Four types of magnetic resonance imaging (MRI) features, including regional homogeneity (ReHo) from resting-state functional MRI, gray matter volume (GM) from structural MRI, fractional anisotropy (FA) from diffusion MRI, and functional network connectivity (FNC) resulted from group independent component analysis, were jointly analyzed by a data-driven multivariate fusion method. Results suggest that a widely distributed network disruption appears in SZ patients, with synchronous changes in both functional and structural regions, especially the basal ganglia network, salience network (SAN), and the frontoparietal network. Such a multimodal coalteration was also replicated in another independent Chinese sample (40 SZs, 66 HCs). Our results on auditory verbal hallucination (AVH) also provide evidence for the hypothesis that prefrontal hypoactivation and temporal hyperactivation in SZ may lead to failure of executive control and inhibition, which is relevant to AVH. In addition, impaired working memory performance was found associated with GM reduction and FA decrease in SZ in prefrontal and superior temporal area, in both discovery and replication datasets. In summary, by leveraging multiple imaging and clinical information into one framework to observe brain in multiple views, we can integrate multiple inferences about SZ from large-scale population and offer unique perspectives regarding the missing links between the brain function and structure that may not be achieved by separate unimodal analyses.

The VALiDATe29 MRI Based Multi-Channel Atlas of the Squirrel Monkey Brain.

We describe the development of the first digital atlas of the normal squirrel monkey brain and present the resulting product, VALiDATe29. The VALiDATe29 atlas is based on multiple types of magnetic resonance imaging (MRI) contrast acquired on 29 squirrel monkeys, and is created using unbiased, nonlinear registration techniques, resulting in a population-averaged stereotaxic coordinate system. The atlas consists of multiple anatomical templates (proton density, T1, and T2* weighted), diffusion MRI templates (fractional anisotropy and mean diffusivity), and ex vivo templates (fractional anisotropy and a structural MRI). In addition, the templates are combined with histologically defined cortical labels, and diffusion tractography defined white matter labels. The combination of intensity templates and image segmentations make this atlas suitable for the fundamental atlas applications of spatial normalization and label propagation. Together, this atlas facilitates 3D anatomical localization and region of interest delineation, and enables comparisons of experimental data across different subjects or across different experimental conditions. This article describes the atlas creation and its contents, and demonstrates the use of the VALiDATe29 atlas in typical applications. The atlas is freely available to the scientific community.

Accuracy of magnetic resonance studies in the detection of chondral and labral lesions in femoroacetabular impingement: systematic review and meta-analysis

BackgroundSeveral types of Magnetic resonance imaging (MRI) are commonly used in imaging of femoroacetabular impingement (FAI), however till now there are no clear protocols and recommendations for each type. The aim of this meta-analysis is to detect the accuracy of conventional magnetic resonance imaging (cMRI), direct magnetic resonance arthrography (dMRA) and indirect magnetic resonance arthrography (iMRA) in the diagnosis of chondral and labral lesions in femoroacetabular impingement (FAI).MethodsA literature search was finalized on the 17th of May 2016 to collect all studies identifying the accuracy of cMRI, dMRA and iMRA in diagnosing chondral and labral lesions associated with FAI using surgical results (arthroscopic or open) as a reference test. Pooled sensitivity and specificity with 95% confidence intervals using a random-effects meta-analysis for MRI, dMRA and iMRA were calculated also area under receiver operating characteristic (ROC) curve (AUC) was retrieved whenever possible where AUC is equivocal to diagnostic accuracy.ResultsThe search yielded 192 publications which were reviewed according inclusion and exclusion criteria then 21 studies fulfilled the eligibility criteria for the qualitative analysis with a total number of 828 cases, lastly 12 studies were included in the quantitative meta-analysis.Meta-analysis showed that as regard labral lesions the pooled sensitivity, specificity and AUC for cMRI were 0.864, 0.833 and 0.88 and for dMRA were 0.91, 0.58 and 0.92. While in chondral lesions the pooled sensitivity, specificity and AUC for cMRI were 0.76, 0.72 and 0.75 and for dMRA were 0.75, 0.79 and 0.83, while for iMRA were sensitivity of 0.722 and specificity of 0.917.ConclusionsThe present meta-analysis showed that the diagnostic test accuracy was superior for dMRA when compared with cMRI for detection of labral and chondral lesions.The diagnostic test accuracy was superior for labral lesions when compared with chondral lesions in both cMRI and dMRA. Promising results are obtained concerning iMRA but further studies still needed to fully assess its diagnostic accuracy.

Medical media analytics via ranking and big learning: A multi-modality image-based disease severity prediction study

Medical media analytics receives vast popularity nowadays because of its effectiveness in improving the performance of diverse health-care applications. In this study, the essential disease severity prediction problem in medical media analytics is investigated and a computer-aided diagnosis (CAD) strategy based on ranking and learning techniques is presented to tackle the disease severity prediction task. To be specific, two types of magnetic resonance images (MRI), including T1-weighted images as anatomic MRI and arterial spin labeling (ASL) images as functional MRI, are incorporated as multi-modality images to provide image-based information for dementia disease severity prediction in this study. There are two main steps composed of the whole CAD strategy. First, the problem of partial volume effects (PVE) mainly caused by signal cross-contamination due to pixel heterogeneity and limited spatial resolution of ASL is focused. Conventional regression-based PVE correction methods are discussed and their inherent problems of blurring and brain details loss in correction results, which prevents the actual brain atrophy being revealed, are studied. A pixel-based PVE correction method, which only counts on single pixel information and formulates the PVE correction problem as a constrained optimization problem solved via the split-Bregman algorithm, is presented to solve the problem. Second, ranking and learning techniques are incorporated based on multi-modality images after performing PVE correction for dementia disease severity prediction. Technically, a conventional discrete position-based ranking evaluation measure is approximated and its surrogated continuous form is optimized via gradient ascend for ranking functions learning. A large database composed of multi-modality images acquired from 320 real patients is utilized for experimental evaluation. Extensive experiments and comprehensive statistical analysis are carried out to demonstrate the superiority of the introduced CAD strategy with comparison to several existing ones. Promising results are reported from the statistical perspective.

In-vitro and in-vivo diagnostic techniques for prostate cancer: a review.

This paper overviews one of the most important, interesting, and challenging problems in oncology, early diagnosis of prostate cancer. Developing effective diagnostic techniques for prostate cancer is of great clinical importance and can improve the effectiveness of treatment and increase the patient's chance of survival. The main focus of this study is to overview the different in-vitro and in-vivo technologies for diagnosing prostate cancer. This review discusses the current clinically used in-vitro cancer diagnostic tools, such as biomarker tests and needle biopsies and including their applications, advantages, and limitations. Moreover, the current in-vitro research tools that focus on the role of nanotechnology in prostate cancer diagnosis have been detailed. In addition to the in-vitro techniques, the current study discusses in detail developed in-vivo non-invasive state-of-the-art Computer-Aided Diagnosis (CAD) systems for prostate cancer based on analyzing Transrectal Ultrasound (TRUS) and different types of magnetic resonance imaging (MRI), e.g., T2-MRI, Diffusion Weighted Imaging (DWI), Dynamic Contrast Enhanced (DCE)-MRI, and multi-parametric MRI, focusing on their implementation, experimental procedures, and reported outcomes. Furthermore, the paper addresses the limitations of the current prostate cancer diagnostic techniques, outlines the challenges that these techniques face, and introduces the recent trends to solve these challenges, which include biomarkers used in in-vitro lab-on-a-chip nanotechnology-based methods.

Assessment of the Early Predictive Power of Quantitative Magnetic Resonance Imaging Parameters During Neoadjuvant Chemotherapy for Uterine Cervical Cancer

The purpose of this study was to quantitatively evaluate 3 types of magnetic resonance imaging (MRI) parameters in parallel for the early prediction of neoadjuvant chemotherapy (NACT) effectiveness in cervical cancer-tumor volume parameters, diffusion parameters, and perfusion parameters. We prospectively evaluated 13 patients with International Federation of Gynecology and Obstetrics stage IB to IIB cervical squamous cell carcinoma who underwent 3 serial MRI studies, that is, pretreatment, post-first course NACT, and post-second course NACT followed by radical hysterectomy. We obtained tumor volume parameters, diffusion parameters, and dynamic contrast material-enhanced perfusion parameters quantitatively from pretreatment MRI and post-first course MRI. The correlation of these parameters and the eventual tumor volume regression rate (TVRR) obtained from pretreatment MRI and post-second course MRI before surgery were investigated, statistically based on the Pearson correlation coefficient. Thirteen patients had a total of 39 scans. Early TVRR (r = 0.844; P < 0.001), the fractional volume of the tissue extracellular extravascular space (Ve, r = 0.648; P < 0.05), and the change of Ve during the first course of NACT (r = -0.638; P < 0.05) correlated with eventual TVRR. Early TVRR, Ve, and the change of Ve could be useful predictors for the treatment effectiveness of NACT. These parameters could help to modify strategy in the early stage of NACT and to choose individualized treatment to avoid the delay of radical treatment, even when NACT is ineffective.