Diffusion-weighted MRI is increasingly applied in the body. It has been recognized for some time, on the basis of scientific experiments and studies in the brain, that the calculation of apparent ...diffusion coefficient by simple monoexponential relationship between MRI signal and b value does not fully account for tissue behavior. However, appreciation of this fact in body diffusion MRI is relatively new, because technologic advancements have only recently enabled high-quality body diffusion-weighted images to be acquired using multiple b values. There is now increasing interest in the radiologic community to apply more sophisticated analytic approaches, such as those based on the principles of intravoxel incoherent motion, which allows quantitative parameters that reflect tissue microcapillary perfusion and tissue diffusivity to be derived.
In this review, we discuss the principles of intravoxel incoherent motion as applied to body diffusion-weighted MRI. The evidence for the technique in measuring tissue perfusion is presented and the emerging clinical utility surveyed. The requisites and challenges of quantitative evaluation beyond simple monoexponential relationships are highlighted.
Medical image analysis remains a challenging application area for artificial intelligence. When applying machine learning, obtaining ground-truth labels for supervised learning is more difficult than ...in many more common applications of machine learning. This is especially so for datasets with abnormalities, as tissue types and the shapes of the organs in these datasets differ widely. However, organ detection in such an abnormal dataset may have many promising potential real-world applications, such as automatic diagnosis, automated radiotherapy planning, and medical image retrieval, where new multimodal medical images provide more information about the imaged tissues for diagnosis. Here, we test the application of deep learning methods to organ identification in magnetic resonance medical images, with visual and temporal hierarchical features learned to categorize object classes from an unlabeled multimodal DCE-MRI dataset so that only a weakly supervised training is required for a classifier. A probabilistic patch-based method was employed for multiple organ detection, with the features learned from the deep learning model. This shows the potential of the deep learning model for application to medical images, despite the difficulty of obtaining libraries of correctly labeled training datasets and despite the intrinsic abnormalities present in patient datasets.
Quantitative whole-body diffusion-weighted MRI (WB-DWI) is now possible using semi-automatic segmentation techniques. The method enables whole-body estimates of global Apparent Diffusion Coefficient ...(gADC) and total Diffusion Volume (tDV), both of which have demonstrated considerable utility for assessing treatment response in patients with bone metastases from primary prostate and breast cancers. Here we investigate the agreement (inter-observer repeatability) between two radiologists in their definition of Volumes Of Interest (VOIs) and subsequent assessment of tDV and gADC on an exploratory patient cohort of nine. Furthermore, each radiologist was asked to repeat his or her measurements on the same patient data sets one month later to identify the intra-observer repeatability of the technique. Using a Markov Chain Monte Carlo (MCMC) estimation method provided full posterior probabilities of repeatability measures along with maximum a-posteriori values and 95% confidence intervals. Our estimates of the inter-observer Intraclass Correlation Coefficient (ICCinter) for log-tDV and median gADC were 1.00 (0.97-1.00) and 0.99 (0.89-0.99) respectively, indicating excellent observer agreement for these metrics. Mean gADC values were found to have ICCinter = 0.97 (0.81-0.99) indicating a slight sensitivity to outliers in the derived distributions of gADC. Of the higher order gADC statistics, skewness was demonstrated to have good inter-user agreement with ICCinter = 0.99 (0.86-1.00), whereas gADC variance and kurtosis performed relatively poorly: 0.89 (0.39-0.97) and 0.96 (0.69-0.99) respectively. Estimates of intra-observer repeatability (ICCintra) demonstrated similar results: 0.99 (0.95-1.00) for log-tDV, 0.98 (0.89-0.99) and 0.97 (0.83-0.99) for median and mean gADC respectively, 0.64 (0.25-0.88) for gADC variance, 0.85 (0.57-0.95) for gADC skewness and 0.85 (0.57-0.95) for gADC kurtosis. Further investigation of two anomalous patient cases revealed that a very small proportion of voxels with outlying gADC values lead to instability in higher order gADC statistics. We therefore conclude that estimates of median/mean gADC and tumour volume demonstrate excellent inter- and intra-observer repeatability whilst higher order statistics of gADC should be used with caution when ascribing significance to clinical changes.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We describe our semi-automatic segmentation of whole-body diffusion-weighted MRI (WBDWI) using a Markov random field (MRF) model to derive tumor total diffusion volume (tDV) and associated global ...apparent diffusion coefficient (gADC); and demonstrate the feasibility of using these indices for assessing tumor burden and response to treatment in patients with bone metastases. WBDWI was performed on eleven patients diagnosed with bone metastases from breast and prostate cancers before and after anti-cancer therapies. Semi-automatic segmentation incorporating a MRF model was performed in all patients below the C4 vertebra by an experienced radiologist with over eight years of clinical experience in body DWI. Changes in tDV and gADC distributions were compared with overall response determined by all imaging, tumor markers and clinical findings at serial follow up. The segmentation technique was possible in all patients although erroneous volumes of interest were generated in one patient because of poor fat suppression in the pelvis, requiring manual correction. Responding patients showed a larger increase in gADC (median change = +0.18, range = -0.07 to +0.78 × 10(-3) mm2/s) after treatment compared to non-responding patients (median change = -0.02, range = -0.10 to +0.05 × 10(-3) mm2/s, p = 0.05, Mann-Whitney test), whereas non-responding patients showed a significantly larger increase in tDV (median change = +26%, range = +3 to +284%) compared to responding patients (median change = -50%, range = -85 to +27%, p = 0.02, Mann-Whitney test). Semi-automatic segmentation of WBDWI is feasible for metastatic bone disease in this pilot cohort of 11 patients, and could be used to quantify tumor total diffusion volume and median global ADC for assessing response to treatment.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Multiple sclerosis (MS) is a complex trait in which allelic variation in the MHC class II region exerts the single strongest effect on genetic risk. Epidemiological data in MS provide strong evidence ...that environmental factors act at a population level to influence the unusual geographical distribution of this disease. Growing evidence implicates sunlight or vitamin D as a key environmental factor in aetiology. We hypothesised that this environmental candidate might interact with inherited factors and sought responsive regulatory elements in the MHC class II region. Sequence analysis localised a single MHC vitamin D response element (VDRE) to the promoter region of HLA-DRB1. Sequencing of this promoter in greater than 1,000 chromosomes from HLA-DRB1 homozygotes showed absolute conservation of this putative VDRE on HLA-DRB1*15 haplotypes. In contrast, there was striking variation among non-MS-associated haplotypes. Electrophoretic mobility shift assays showed specific recruitment of vitamin D receptor to the VDRE in the HLA-DRB1*15 promoter, confirmed by chromatin immunoprecipitation experiments using lymphoblastoid cells homozygous for HLA-DRB1*15. Transient transfection using a luciferase reporter assay showed a functional role for this VDRE. B cells transiently transfected with the HLA-DRB1*15 gene promoter showed increased expression on stimulation with 1,25-dihydroxyvitamin D3 (P = 0.002) that was lost both on deletion of the VDRE or with the homologous "VDRE" sequence found in non-MS-associated HLA-DRB1 haplotypes. Flow cytometric analysis showed a specific increase in the cell surface expression of HLA-DRB1 upon addition of vitamin D only in HLA-DRB1*15 bearing lymphoblastoid cells. This study further implicates vitamin D as a strong environmental candidate in MS by demonstrating direct functional interaction with the major locus determining genetic susceptibility. These findings support a connection between the main epidemiological and genetic features of this disease with major practical implications for studies of disease mechanism and prevention.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Initially thought to play a restricted role in calcium homeostasis, the pleiotropic actions of vitamin D in biology and their clinical significance are only now becoming apparent. However, the mode ...of action of vitamin D, through its cognate nuclear vitamin D receptor (VDR), and its contribution to diverse disorders, remain poorly understood. We determined VDR binding throughout the human genome using chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq). After calcitriol stimulation, we identified 2776 genomic positions occupied by the VDR and 229 genes with significant changes in expression in response to vitamin D. VDR binding sites were significantly enriched near autoimmune and cancer associated genes identified from genome-wide association (GWA) studies. Notable genes with VDR binding included IRF8, associated with MS, and PTPN2 associated with Crohn's disease and T1D. Furthermore, a number of single nucleotide polymorphism associations from GWA were located directly within VDR binding intervals, for example, rs13385731 associated with SLE and rs947474 associated with T1D. We also observed significant enrichment of VDR intervals within regions of positive selection among individuals of Asian and European descent. ChIP-seq determination of transcription factor binding, in combination with GWA data, provides a powerful approach to further understanding the molecular bases of complex diseases.
Background
Interpretation of diffusion in conjunction with T2‐weighted MRI is essential for assessing prostate cancer; however, the combination of apparent diffusion coefficient (ADC) with ...quantitative T2 mapping remains unexplored.
Purpose
To document the T2 components and ADC of untreated and irradiated nonmalignant prostate tissue as a measure of their glandular luminal and cellular compartments and to compare values with those of tumor.
Study Type
Prospective.
Population
Twenty‐four men with prostate cancer (14 untreated; 10 with biochemical recurrence following radiation therapy).
Field Strength/Sequences
Endorectal 3 T MRI including a 32‐echo gradient echo and spin echo (GRASE) and an 8 b‐value diffusion‐weighted sequence.
Assessment
Regions of interest were drawn on ADC maps and T2‐weighted images around focal lesions in areas of biopsy‐positive prostate cancer and in nonmalignant areas of untreated and irradiated peripheral zone (PZ), and untreated transitional zone (TZ). Multiecho T2 data were fitted with mono‐/biexponential decay and nonnegative least squares functions. The luminal water fraction (LWF) was derived.
Statistical Tests
The preference between mono‐ and biexponential decay was assessed using the Bayesian information criterion. Differences in fitted parameters between tissue types were compared (paired t‐test within groups, Kruskal–Wallis and Wilcoxon rank‐sum test between groups) and correlations between ADC and T2 components assessed (Spearman rank correlation test).
Results
LWF in tumor (0.09) was significantly lower than in PZ or TZ (0.27 and 0.18, P < 0.01, respectively), but tumor values were comparable to nonmalignant irradiated prostate (0.08). The short T2 relaxation rate was lower in tumor than in nonmalignant untreated or irradiated tissue (significant compared with TZ, P = 0.01). There was a strong correlation between LWF and ADC in normal untreated tissue (r = 0.88, P < 0.001). This relationship was absent in nonmalignant irradiated prostrate (r = –0.35, P = 0.42) and in tumor (r = –0.04, P = 0.88).
Data Conclusion
T2 components in conjunction with ADC can be used to characterize untreated and irradiated nonmalignant prostate and tumor. LWF is most useful at discriminating tumor in the untreated prostate.
Level of Evidence: 2
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2019;50:619–627.
To build machine learning predictive models for surgical risk assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa) before radical prostatectomy; and to compare the use ...of decision curve analysis (DCA) and receiver operating characteristic (ROC) metrics for selecting input feature combinations in models.
This retrospective observational study included two independent data sets: 139 participants from a single institution (training), and 55 from 15 other institutions (external validation), both treated with Robotic Assisted Radical Prostatectomy (RARP). Five ML models, based on different combinations of clinical, semantic (interpreted by a radiologist) and radiomics features computed from T2W-MRI images, were built to predict extracapsular extension in the prostatectomy specimen (pECE+). DCA plots were used to rank the models' net benefit when assigning patients to prostatectomy with non-nerve-sparing surgery (NNSS) or nerve-sparing surgery (NSS), depending on the predicted ECE status. DCA model rankings were compared with those drived from ROC area under the curve (AUC).
In the training data, the model using clinical, semantic, and radiomics features gave the highest net benefit values across relevant threshold probabilities, and similar decision curve was observed in the external validation data. The model ranking using the AUC was different in the discovery group and favoured the model using clinical + semantic features only.
The combined model based on clinical, semantic and radiomic features may be used to predict pECE + in patients with PCa and results in a positive net benefit when used to choose between prostatectomy with NNS or NNSS.
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