A Modified Tri-Exponential Model for Multi-b-value Diffusion-Weighted Imaging: A Method to Detect the Strictly Diffusion-Limited Compartment in Brain

Quantifying H&E staining results, grading and predicting IDH mutation status of gliomas using hybrid multi-dimensional MRI

OBJECTIVE

To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas.

MATERIALS AND METHODS

Included were 71 glioma patients (mean age, 50.17 ± 13.38 years; 35 men). HM-MRI images were collected at five different echo times (80-200 ms) with seven b-values (0-3000 s/mm2). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models.

RESULTS

HM-MRI metrics FDvery-slow and FDslow demonstrated a significant correlation with the H&E staining results (p < .05). Besides, FDvery-slow showed the highest area under ROC curve (AUC = 0.854) for grading, while Dslow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics FDvery-slow and T2Dslow improved the diagnostic performance for grading (AUC = 0.876).

DISCUSSION

HM-MRI can aid in non-invasive diagnosis of gliomas.

Using Ultrahigh b -Value Diffusion-Weighted Imaging to Noninvasively Assess Renal Fibrosis in a Rabbit Model of Renal Artery Stenosis

OBJECTIVE

This study aimed to investigate the feasibility of diffusion-weighted imaging with ultrahigh b values ( ub DWI) for the evaluation of renal fibrosis (RF) induced by renal artery stenosis (RAS) in a rabbit model.

METHODS

Thirty-two rabbits underwent left RAS operation, whereas 8 rabbits received sham surgery. All rabbits underwent ub DWI ( b = 0-4500 s/mm 2 ). The standard apparent diffusion coefficient (ADC st ), molecular diffusion coefficient ( D ), perfusion fraction ( f ), perfusion-related diffusion coefficient ( D *) and ultrahigh apparent diffusion coefficient (ADC uh ) were longitudinally assessed before operation and at weeks 2, 4, and 6 after operation. The degree of interstitial fibrosis and the expression of aquaporin (AQP) 1 and AQP2 were determined through pathological examination.

RESULTS

In the stenotic kidney, the ADC st , D , f , and ADC uh values of the renal parenchyma significantly decreased compared with those at baseline (all P < 0.05), whereas the D * values significantly increased after RAS induction ( P < 0.05). The ADC st , D , D *, and f were weakly to moderately correlated with interstitial fibrosis as well as with the expression of AQP1 and AQP2. Furthermore, the ADC uh negatively correlated with interstitial fibrosis ( ρ = -0.782, P < 0.001) and positively correlated with AQP1 and AQP2 expression ( ρ = 0.794, P < 0.001, and ρ = 0.789, P < 0.001, respectively).

CONCLUSIONS

Diffusion-weighted imaging with ultrahigh b values shows the potential for noninvasive assessment of the progression of RF in rabbits with unilateral RAS. The ADC uh derived from ub DWI could reflect the expression of AQPs in RF.

Magnetic resonance imaging to assess the brain response to fasting in glioblastoma-bearing rats as a model of cancer anorexia

BACKGROUND

Global energy balance is a vital process tightly regulated by the brain that frequently becomes dysregulated during the development of cancer. Glioblastoma (GBM) is one of the most investigated malignancies, but its appetite-related disorders, like anorexia/cachexia symptoms, remain poorly understood.

METHODS

We performed manganese enhanced magnetic resonance imaging (MEMRI) and subsequent diffusion tensor imaging (DTI), in adult male GBM-bearing (n = 13) or control Wistar rats (n = 12). A generalized linear model approach was used to assess the effects of fasting in different brain regions involved in the regulation of the global energy metabolism: cortex, hippocampus, hypothalamus and thalamus. The regions were selected on the contralateral side in tumor-bearing animals, and on the left hemisphere in control rats. An additional DTI-only experiment was completed in two additional GBM (n = 5) or healthy cohorts (n = 6) to assess the effects of manganese infusion on diffusion measurements.

RESULTS

MEMRI results showed lower T₁ values in the cortex (p-value < 0.001) and thalamus (p-value < 0.05) of the fed ad libitum GBM animals, as compared to the control cohort, consistent with increased Mn²⁺ accumulation. No MEMRI-detectable differences were reported between fed or fasting rats, either in control or in the GBM group. In the MnCl₂-infused cohorts, DTI studies showed no mean diffusivity (MD) variations from the fed to the fasted state in any animal cohort. However, the DTI-only set of acquisitions yielded remarkably decreased MD values after fasting only in the healthy control rats (p-value < 0.001), and in all regions, but thalamus, of GBM compared to control animals in the fed state (p-value < 0.01). Fractional anisotropy (FA) decreased in tumor-bearing rats due to the infiltrate nature of the tumor, which was detected in both diffusion sets, with (p-value < 0.01) and without Mn²⁺ administration (p-value < 0.001).

CONCLUSIONS

Our results revealed that an altered physiological brain response to fasting occurred in hunger related regions in GBM animals, detectable with DTI, but not with MEMRI acquisitions. Furthermore, the present results showed that Mn²⁺ induces neurotoxic inflammation, which interferes with diffusion MRI to detect appetite-induced responses through MD changes.

A three-component multi-b-value diffusion-weighted imaging might be a useful biomarker for detecting microstructural features in gliomas with differences in malignancy and IDH-1 mutation status

OBJECTIVES

The purpose of the study was to explore the performance of a three-component diffusion model in evaluating the degree of malignancy and isocitrate dehydrogenase 1 (IDH-1) gene type of gliomas.

METHODS

Overall, 60 patients with gliomas were enrolled. The intermediate and perfusion-related diffusion coefficients (D_int and D_p) and fractions of strictly limited, intermediate, and perfusion-related diffusion (F_very-slow, F_int, and F_p) were obtained with a three-component diffusion model. Parameters were also obtained from a diffusion kurtosis model and mono- and biexponential models. All parameters were compared between different tumor grades and IDH-1 gene types. Diagnostic performance and logistic regression analyses were performed.

RESULTS

High-grade gliomas (HGGs) had significantly higher F_int, F_very-slow, and D_p values but significantly lower F_p and D_int values than low-grade gliomas (LGGs), and F_int and F_p differed significantly among grade II, III, and IV gliomas (p < 0.05 for all). F_int achieved the highest AUC of 0.872 in differentiating between LGGs and HGGs. Logistic regression analysis revealed that in each model, F_int, diffusion coefficient (D), apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) were associated with glioma grading. After multiple regression analysis, F_int remained the only differentiator. Additionally, F_int and F_p showed significant differences between IDH-1 mutated and IDH-1 wild-type gliomas (p = 0.007 and 0.01, respectively).

CONCLUSIONS

The three-component DWI model served as a useful biomarker for detecting microstructural features in gliomas with different grades and IDH-1 mutation statuses.

KEY POINTS

• The three-component model enables the estimation of an intermediate diffusion component. • The three-component model performed better than the other models in glioma grading and genotyping. • F_int was useful in evaluating the grade and genotype of gliomas.

Heterogeneity evaluation of multi-high b-value apparent diffusion coefficient on cerebral ischemia in MCAO rat

Purpose

To assess brain damage in a rat model of cerebral ischemia based on apparent diffusion coefficient (ADC) data obtained from multi-high b-values and evaluate the relationship between Aquaporin 4 (AQP4) expression and ADC.

Methods

Thirty eight male Sprague-Dawley rats were randomized into two groups: (1) sham controls (n = 6) and (2) cerebral ischemia (successful model, n = 19). All rats underwent diffusion-weighted imaging (DWI) with both standard b-values and multi-high b-values (2,500-4,500 s/mm²) using a 3.0-T device. Standard ADC (ADC_st) maps and multi-high b-value ADCs (ADC_mh) were calculated, respectively. Aquaporin 4 expression was quantified using Western blot. Relative values of ADC_st and ADC_mh, AQP4 expression were compared between the sham group and the ischemia group. Correlations between ADC values and AQP4 expression were evaluated.

Results

At 0.5 h after suture insertion, the value of ADC_mh on the lesion was obviously decreased, and there was no difference in lesion volume when compared with ADC_st. After reperfusion, besides similar regions where ADC_st values decreased, we also found additional large values on ADC_mh within the cortex of the ipsilateral side or surrounding the lesion. The lesion evolution of the large value on ADC_mh was quite different from other indicators. But the total ADC_mh values were still significantly associated with ADC_st. The AQP4 protein expression level was appreciably increased after middle cerebral artery occlusion (MCAO), but there was no correlation between AQP4 expression either with ADC_mh or ADC_st.

Conclusion

We found the large values on ADC_mh during the progression of cerebral infarction is varied, but there was no correlation between ADC_mh values and AQP4 expression. ADC_mh may indicate the heterogeneity of ischemia lesions, but the underlying pathological basis should be further explored.

Axonal T2 estimation using the spherical variance of the strongly diffusion-weighted MRI signal

In magnetic resonance imaging, the application of a strong diffusion weighting suppresses the signal contributions from the less diffusion-restricted constituents of the brain's white matter, thus enabling the estimation of the transverse relaxation time T₂ that arises from the more diffusion-restricted constituents such as the axons. However, the presence of cell nuclei and vacuoles can confound the estimation of the axonal T₂, as diffusion within those structures is also restricted, causing the corresponding signal to survive the strong diffusion weighting. We devise an estimator of the axonal T₂ based on the directional spherical variance of the strongly diffusion-weighted signal. The spherical variance T₂ estimates are insensitive to the presence of isotropic contributions to the signal like those provided by cell nuclei and vacuoles. We show that with a strong diffusion weighting these estimates differ from those obtained using the directional spherical mean of the signal which contains both axonal and isotropically-restricted contributions. Our findings hint at the presence of an MRI-visible isotropically-restricted contribution to the signal in the white matter ex vivo fixed tissue (monkey) at 7T, and do not allow us to discard such a possibility also for in vivo human data collected with a clinical 3T system.

Ultra-high b value DWI in distinguishing fresh gray matter ischemic lesions from white matter ones: a comparative study with routine and high b value DWI

Background

Fresh ischemic lesions (FILs) can occur in both the brain's gray matter (GM) and white matter (WM), with each location signifying a different prognosis for patients. This study aims to investigate the application of ultra-high b value diffusion-weighted imaging (DWI) in distinguishing FILs in these two areas via a comparative study with routine and high b value DWI.

Methods

Multiple b value DWI (b=0, 500, 1,000, 2,000, 4,000, 6,000, 8,000, 10,000 s/mm²) was performed on 47 patients with suspected acute ischemic stroke (AIS). Apparent diffusion coefficient (ADC) maps, including ADC₅₀₀, ADC_1,000, ADC_2,000, ADC_4,000, ADC_6,000, ADC_8,000, and ADC_10,000, were calculated, and the mean ADC value of the FILs in the GM and WM on each map was obtained by referring to the structural magnetic resonance imaging (MRI). ADC value differences of the FILs in the GM and WM were compared using Mann-Whitney U tests, and receiver operating characteristic (ROC) curves evaluated the diagnostic efficiency of each ADC value in distinguishing FILs in the two areas.

Results

In the enrolled 34 patients, 145 FILs were identified, of which 42 involved the GM, 87 the WM, and 16 both the GM and WM. A total of 161 regions were delineated, 58 in the GM and 103 in the WM. The values of FILs in the WM on ADC_2,000, ADC_4,000, ADC_6,000, ADC_8,000, and ADC_10,000 maps were significantly lower than those in the GM (P=0.007, P<0.001, P<0.001, P<0.001 and P<0.001, respectively), while no significant differences were found on ADC₅₀₀ and ADC_1,000 maps (P=0.427 and P=0.225, respectively). ROC curves demonstrated that the area under the curve (AUC) paralleled the increasing b value, ascending from ADC₅₀₀ to ADC_10,000 (0.538, 0.558, 0.629, 0.766, 0.827, 0.859, 0.872, in that order).

Conclusions

Ultra-high b value DWI is extremely sensitive to the slight diffusion difference between FILs in the GM and the WM. Its sensitivity parallels the increasing b value, indicating its clinical advantage in identifying the microstructure of FILs.

Useful MRI Findings for Minimally Invasive Surgery for Early Cervical Cancer

Simple Summary

Radical hysterectomy and lymph node dissection are extensive procedures with severe post-operative morbidities and should be avoided on patients with low risk of recurrence. Still, due to lack of good prognostic tools, radical surgery is performed on most patients with early stage cervical cancer, leading to overtreatment and unnecessary morbidities. The recent International Federation of Gynecology and Obstetrics (FIGO) staging system accepts the use of magnetic resonance imaging (MRI) in addition to physical examination. Currently, 3 Tesla (3T) MRI is available widely and, due to its high soft tissue contrast, can provide more useful information on precise estimation of tumor size and metastasis than can physical examination in patients with cervical cancer. Therefore, this imaging modality can help gynecologic oncologists to determine whether minimally invasive surgery is necessary and can be used for early detection of small recurrent cancers.

Abstract

According to the recent International Federation of Gynecology and Obstetrics (FIGO) staging system, Stage III cervical cancer indicates pelvic or paraaortic lymph node metastasis. Accordingly, the new FIGO stage accepts imaging modalities, such as MRI, as part of the FIGO 2018 updated staging. Magnetic resonance imaging (MRI) is the best imaging modality to estimate the size or volume of uterine cancer because of its excellent soft tissue contrast. As a result, MRI is being used increasingly to determine treatment options and follow-up for cervical cancer patients. Increasing availability of cancer screening and vaccination have improved early detection of cervical cancer. However, the incidence of early cervical cancers has increased compared to that of advanced cervical cancer. A few studies have investigated if MRI findings are useful in management of early cervical cancer. MRI can precisely predict tumor burden, allowing conization, trachelectomy, and simple hysterectomy to be considered as minimally invasive treatment options for early cervical cancer. This imaging modality also can be used to determine whether there is recurrent cancer following minimally invasive treatments. The purpose of this review is to highlight useful MRI features for managing women with early cervical cancer.

Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging

OBJECTIVE

To evaluate the potential of diffusional variance decomposition (DIVIDE) for grading, molecular feature classification, and microstructural characterization of gliomas.

MATERIALS AND METHODS

Participants with suspected gliomas underwent DIVIDE imaging, yielding parameter maps of fractional anisotropy (FA), mean diffusivity (MD), anisotropic mean kurtosis (MK_A), isotropic mean kurtosis (MK_I), total mean kurtosis (MK_T), MK_A/MK_T, and microscopic fractional anisotropy (μFA). Tumor type and grade, isocitrate dehydrogenase (IDH) 1/2 mutant status, and the Ki-67 labeling index (Ki-67 LI) were determined after surgery. Statistical analysis included 33 high-grade gliomas (HGG) and 17 low-grade gliomas (LGG). Tumor diffusion metrics were compared between HGG and LGG, among grades, and between wild and mutated IDH types using appropriate tests according to normality assessment results. Receiver operating characteristic and Spearman correlation analysis were also used for statistical evaluations.

RESULTS

FA, MD, MK_A, MK_I, MK_T, μFA, and MK_A/MK_T differed between HGG and LGG (FA: p = 0.047; MD: p = 0.037, others p < 0.001), and among glioma grade II, III, and IV (FA: p = 0.048; MD: p = 0.038, others p < 0.001). All diffusion metrics differed between wild-type and mutated IDH tumors (MK_I: p = 0.003; others: p < 0.001). The metrics that best discriminated between HGG and LGGs and between wild-type and mutated IDH tumors were MK_T and FA respectively (area under the curve 0.866 and 0.881). All diffusion metrics except FA showed significant correlation with Ki-67 LI, and MK_I had the highest correlation coefficient (r_s = 0.618).

CONCLUSION

DIVIDE is a promising technique for glioma characterization and diagnosis.

KEY POINTS

• DIVIDE metrics MK_I is related to cell density heterogeneity while MK_A and μFA are related to cell eccentricity. • DIVIDE metrics can effectively differentiate LGG from HGG and IDH mutation from wild-type tumor, and showed significant correlation with the Ki-67 labeling index. • MK_I was larger than MK_A which indicates predominant cell density heterogeneity in gliomas. • MK_A and MK_I increased with grade or degree of malignancy, however with a relatively larger increase in the cell eccentricity metric MK_A in relation to the cell density heterogeneity metric MK_I.

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

Beyond the diffusion standard model in fixed rat spinal cord with combined linear and planar encoding

Information about tissue on the microscopic and mesoscopic scales can be accessed by modelling diffusion MRI signals, with the aim of extracting microstructure-specific biomarkers. The standard model (SM) of diffusion, currently the most broadly adopted microstructural model, describes diffusion in white matter (WM) tissues by two Gaussian components, one of which has zero radial diffusivity, to represent diffusion in intra- and extra-axonal water, respectively. Here, we reappraise these SM assumptions by collecting comprehensive double diffusion encoded (DDE) MRI data with both linear and planar encodings, which was recently shown to substantially enhance the ability to estimate SM parameters. We find however, that the SM is unable to account for data recorded in fixed rat spinal cord at an ultrahigh field of 16.4 T, suggesting that its underlying assumptions are violated in our experimental data. We offer three model extensions to mitigate this problem: first, we generalize the SM to accommodate finite radii (axons) by releasing the constraint of zero radial diffusivity in the intra-axonal compartment. Second, we include intracompartmental kurtosis to account for non-Gaussian behaviour. Third, we introduce an additional (third) compartment. The ability of these models to account for our experimental data are compared based on parameter feasibility and Bayesian information criterion. Our analysis identifies the three-compartment description as the optimal model. The third compartment exhibits slow diffusion with a minor but non-negligible signal fraction (∼12%). We demonstrate how failure to take the presence of such a compartment into account severely misguides inferences about WM microstructure. Our findings bear significance for microstructural modelling at large and can impact the interpretation of biomarkers extracted from the standard model of diffusion.

Validation of conductivity tensor imaging using giant vesicle suspensions with different ion mobilities

Background

Electrical conductivity of a biological tissue at low frequencies can be approximately expressed as a tensor. Noting that cross-sectional imaging of a low-frequency conductivity tensor distribution inside the human body has wide clinical applications of many bioelectromagnetic phenomena, a new conductivity tensor imaging (CTI) technique has been lately developed using an MRI scanner. Since the technique is based on a few assumptions between mobility and diffusivity of ions and water molecules, experimental validations are needed before applying it to clinical studies.

Methods

We designed two conductivity phantoms each with three compartments. The compartments were filled with electrolytes and/or giant vesicle suspensions. The giant vesicles were cell-like materials with thin insulating membranes. We controlled viscosity of the electrolytes and the giant vesicle suspensions to change ion mobility and therefore conductivity values. The conductivity values of the electrolytes and giant vesicle suspensions were measured using an impedance analyzer before CTI experiments. A 9.4-T research MRI scanner was used to reconstruct conductivity tensor images of the phantoms.

Results

The CTI technique successfully reconstructed conductivity tensor images of the phantoms with a voxel size of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.5\times 0.5\times 0.5\hbox { mm}^3$$\end{document}0.5×0.5×0.5mm3. The relative \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L^2$$\end{document}L2 errors between the conductivity values measured by the impedance analyzer and those reconstructed by the MRI scanner was between 1.1 and 11.5.

Conclusions

The accuracy of the new CTI technique was estimated to be high enough for most clinical applications. Future studies of animal models and human subjects should be pursued to show the clinical efficacy of the CTI technique.

Towards unconstrained compartment modeling in white matter using diffusion-relaxation MRI with tensor-valued diffusion encoding

PURPOSE

To optimize diffusion-relaxation MRI with tensor-valued diffusion encoding for precise estimation of compartment-specific fractions, diffusivities, and T₂ values within a two-compartment model of white matter, and to explore the approach in vivo.

METHODS

Sampling protocols featuring different b-values (b), b-tensor shapes (b_Δ ), and echo times (TE) were optimized using Cramér-Rao lower bounds (CRLB). Whole-brain data were acquired in children, adults, and elderly with white matter lesions. Compartment fractions, diffusivities, and T₂ values were estimated in a model featuring two microstructural compartments represented by a "stick" and a "zeppelin."

RESULTS

Precise parameter estimates were enabled by sampling protocols featuring seven or more "shells" with unique b/b_Δ /TE-combinations. Acquisition times were approximately 15 minutes. In white matter of adults, the "stick" compartment had a fraction of approximately 0.5 and, compared with the "zeppelin" compartment, featured lower isotropic diffusivities (0.6 vs. 1.3 μm² /ms) but higher T₂ values (85 vs. 65 ms). Children featured lower "stick" fractions (0.4). White matter lesions exhibited high "zeppelin" isotropic diffusivities (1.7 μm² /ms) and T₂ values (150 ms).

CONCLUSIONS

Diffusion-relaxation MRI with tensor-valued diffusion encoding expands the set of microstructure parameters that can be precisely estimated and therefore increases their specificity to biological quantities.

The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain

The so-called “dot-compartment” is conjectured in diffusion MRI to represent small spherical spaces, such as cell bodies, in which the diffusion is restricted in all directions. Previous investigations inferred its existence from data acquired with directional diffusion encoding which does not permit a straightforward separation of signals from ‘sticks’ (axons) and signals from ‘dots’. Here we combine isotropic diffusion encoding with ultra-strong diffusion gradients (240 ​mT/m) to achieve high diffusion-weightings with high signal to noise ratio, while suppressing signal arising from anisotropic water compartments with significant mobility along at least one axis (e.g., axons). A dot-compartment, defined to have apparent diffusion coefficient equal to zero and no exchange, would result in a non-decaying signal at very high b-values (b≳7000s/mm2). With this unique experimental setup, a residual yet slowly decaying signal above the noise floor for b-values as high as 15000s/mm2 was seen clearly in the cerebellar grey matter (GM), and in several white matter (WM) regions to some extent. Upper limits of the dot-signal-fraction were estimated to be 1.8% in cerebellar GM and 0.5% in WM. By relaxing the assumption of zero diffusivity, the signal at high b-values in cerebellar GM could be represented more accurately by an isotropic water pool with a low apparent diffusivity of 0.12 μm2/ms and a substantial signal fraction of 9.7%. The T2 of this component was estimated to be around 61ms. This remaining signal at high b-values has potential to serve as a novel and simple marker for isotropically-restricted water compartments in cerebellar GM.

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The “dot-compartment” is conjectured in diffusion MRI to represent e.g. cell bodies.

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We combine isotropic encoding with ultra-strong gradients to study the dot-compartment.

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A slowly decaying signal for high b-values was seen in cerebellar GM.

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An apparent diffusivity of 0.12 and signal fraction of 9.7% were estimated.

•

The signal could serve as a novel and simple marker for spherical compartments.

In vivo measurement of cytoplasmic organelle water fraction using diffusion-weighted imaging: Application in the malignant grading and differential diagnosis of gliomas

Recently, we have proposed a theoretical modified tri-exponential model for multi-b-value diffusion-weighted imaging (DWI) to measure the cytoplasmic organelle water fraction (COWF). This study aims to investigate whether COWF maps are effective in evaluating the malignant degree of gliomas and distinguishing primary central nervous system lymphomas (PCNSL) from gliomas.We performed this retrospective study based on our prospectively collected data. All patients underwent preoperative multi-b-value DWI. Parametric maps were derived from multi-b-value DWI maps using the modified tri-exponential model. Receiver operating characteristic analyses were used to assess the diagnostic accuracy of the parameter maps. Pearson correlation coefficients were calculated to investigate the correlations between the parameters and the Ki-67 proliferation index.A total of 66 patients were enrolled, including 16 low-grade gliomas (LGG), 45 high-grade gliomas (HGG), and 5 PCNSL. The mean COWF values were significantly different among LGG (3.1 ± 1.4%), HGG (6.9 ± 2.8%), and PCNSL (14.0 ± 2.2%) (P < .001). The areas under the curves of the mean COWF value in distinguishing HGG from LGG and distinguishing PCNSL from gliomas were 0.899 and 0.980, respectively. The mean COWF value had a moderate correlation with the Ki-67 proliferation index (r = 0.647).The COWF map is useful in malignant grading of gliomas, and may be helpful in distinguishing PCNSL from gliomas.

Preclinical Molecular Imaging for Precision Medicine in Breast Cancer Mouse Models

Precision and personalized medicine is gaining importance in modern clinical medicine, as it aims to improve diagnostic precision and to reduce consequent therapeutic failures. In this regard, prior to use in human trials, animal models can help evaluate novel imaging approaches and therapeutic strategies and can help discover new biomarkers. Breast cancer is the most common malignancy in women worldwide, accounting for 25% of cases of all cancers and is responsible for approximately 500,000 deaths per year. Thus, it is important to identify accurate biomarkers for precise stratification of affected patients and for early detection of responsiveness to the selected therapeutic protocol. This review aims to summarize the latest advancements in preclinical molecular imaging in breast cancer mouse models. Positron emission tomography (PET) imaging remains one of the most common preclinical techniques used to evaluate biomarker expression in vivo, whereas magnetic resonance imaging (MRI), particularly diffusion-weighted (DW) sequences, has been demonstrated as capable of distinguishing responders from nonresponders for both conventional and innovative chemo- and immune-therapies with high sensitivity and in a noninvasive manner. The ability to customize therapies is desirable, as this will enable early detection of diseases and tailoring of treatments to individual patient profiles. Animal models remain irreplaceable in the effort to understand the molecular mechanisms and patterns of oncologic diseases.

Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR)

Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.