Dynamic Contrast-enhanced Research Articles

Translation of dynamic contrast-enhanced imaging onto a magnetic resonance-guided linear accelerator in patients with head and neck cancer.

Magnetic resonance imaging - linear accelerator (MRI-linac) systems permit imaging of tumours to guide treatment. Dynamic contrast enhanced (DCE)-MRI allows investigation of tumour perfusion. We assessed the feasibility of performing DCE-MRI on a 1.5T MRI-linac in patients with head and neck cancer (HNC) and measured biomarker repeatability and sensitivity to radiotherapy effects. Patients were imaged on a 1.5T MRI-linac or a 1.5T diagnostic MR system twice before treatment. DCE-MRI parameters including Ktrans were calculated, with the optimum pharmacokinetic model identified using corrected Akaike information criterion. Repeatability was assessed by within-subject coefficient of variation (wCV). Treatment effects were assessed as change measured at week 2 of radiotherapy. 14 patients were recruited (6 scanned on diagnostic MR and 8 on MRI-linac), with a total of 24 lesions. Baseline Ktrans estimates were comparable on both MR systems; 0.13 [95%CI: 0.10 to 0.16] min-1 (diagnostic MR) and 0.15 [0.12 to 0.18] min-1 (MRI-linac). wCV values were 22.6% [95% CI: 16.2 to 37.3%] (diagnostic MR) and 11.7% [8.4 to 19.3%] (MRI-linac). Combined cohort increase in Ktrans was significant (p<0.01). Similar results were seen for other DCE-MRI parameters. DCE-MRI is feasible on a 1.5T MRI-linac system in patients with HNC. Parameter estimates, repeatability, and sensitivity to treatment were similar to those measured on a conventional diagnostic MR system. These data support performing DCE-MRI in studies on the MRI-linac to assess treatment response and adaptive guidance based on tumour perfusion.

Value of MRI-Based Ovarian-Adnexal Reporting and Data System for the Diagnosis of Adnexal Masses.

Objective To assess the value of the MRI-based ovarian-adnexal reporting and data system (O-RADS MRI) for the diagnosis of adnexal masses. Methods A total of 407 patients who underwent dynamic contrast enhancement (DCE)-MRI and pathological examination (gold standard) at the Department of Radiology,Central Hospital of Wuhan between May 2017 and December 2022 were enrolled in this study.Two radiologists performed the O-RADS MRI scoring of adnexal masses according to MRI features and calculated the malignancy rate of adnexal masses by O-RADS MRI score,enhancement type,and mass type.Moreover,receiver operating characteristic curves were established to further evaluate the diagnostic values of O-RADS MRI score,enhancement type,and mass type for adnexal masses. Results A total of 502 adnexal masses were identified in the 407 patients enrolled in this study,including 364 benign masses and 138 malignant masses (including junctional masses).Radiologist 1 reported the malignancy rates of 0,0,5.4%,80.0%,and 89.7% and radiologist 2 reported the malignancy rates of 0,0,5.8%,86.2%,and 83.0% for the adnexal masses with the O-RADS MRI scores of 1-5,respectively.With O-RADS MRI ≥4 indicating malignant masses,the sensitivity,specificity,accuracy,positive predictive value,negative predictive value,false negative rate,and false positive rate were 94.2%,93.6%,93.8%,84.9%,97.7%,2.3%,and 15.1% for radiologist 1 and 93.4%,93.6%,93.6%,85.4%,97.4%,3.6%,and 14.6% for radiologist 2,respectively.The malignancy rates of the adnexal masses presenting no enhancement,cystic wall enhancement,type Ⅰ curve,type Ⅱ curve,and type Ⅲ curve were 0,1.3%,5.7%,81.2%,and 89.0% as reported by radiologist 1 and 0,1.2%,11.3%,87.6%,and 80.0% as reported by radiologist 2,respectively.The malignancy rates of the adnexal masses that were cystic lesions,cystic segregated lesions,solid lesions,cystic solid lesions,and cystic solid segregated lesions were 0,7.1%,38.7%,79.1%,and 89.8% as reported by radiologist 1 and 0,8.1%,37.8%,72.4%,and 89.6% as reported by radiologist 2,respectively.With type Ⅱ and type Ⅲ curves as the criteria for malignancy,the sensitivity of radiologists 1 and 2 was lower for cystic segregated lesions,both at 50.0%.For the masses containing solid components,radiologists 1 and 2 demonstrated low specificity,which was 57.7% and 56.5%,respectively.False-positive masses contained solid components and were mostly fibroadenomas or adnexal leiomyomas,while false-negative masses were mostly junctional cystadenomas with no or few solid components. Conclusions The O-RADS MRI risk stratification has a high diagnostic value for adnexal masses.Further evaluation and refinement are needed to reduce the false-positive rate.

Sex differences and determinants of coronary microvascular function in asymptomatic adults with type 2 diabetes.

Coronary microvascular dysfunction (CMD) is a significant complication in type 2 diabetes (T2D) and may be more common in women. We aimed to evaluate the sex differences and sex-specific clinical determinants of CMD in adults with T2D without prevalent cardiovascular disease. Single center pooled analysis of four prospective studies comparing asymptomatic people with T2D and controls. All subjects underwent comprehensive cardiovascular phenotyping with myocardial perfusion reserve (MPR) quantified with perfusion MRI. Participants with silent coronary disease were excluded. Multivariable linear regression was performed to identify determinants of MPR with an interaction term for sex. Four-hundred and seventy-nine T2D (age 57 ± 11y, 42% women) were compared with 116 controls (age 53 ± 11y, 41% women). Men with T2D, but not women, demonstrated worse systolic function and higher extra-cellular volume fraction than controls. MPR was significantly lower in T2D than controls (women, 2.6 ± 0.9 vs. 3.3 ± 1.0, p <0.001; men, 3.1 ± 0.9 vs. 3.5 ± 1.0, p = 0.004), and lower in women than men with T2D (p <0.001). More women than men with T2D had MPR <2.5 (46% vs. 26%, p <0.001). There was a significant interaction between sex and body mass index (BMI) for MPR (p-interaction <0.001). Following adjustment for clinical risk factors, inverse association with MPR were body mass index (BMI) in women (β = -0.17, p = 0.045) and systolic blood pressure in men (β = -0.14, p = 0.049). Among asymptomatic adults with T2D, women had a greater prevalence of CMD than men. Risk factors modestly but significantly associated with coronary microvascular dysfunction in asymptomatic people with T2D were BMI among women and systolic blood pressure among men.

DCE-Qnet: deep network quantification of dynamic contrast enhanced (DCE) MRI.

Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption. A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B1 inhomogeneities to estimate perfusion (Ktrans, vp, ve), tissue T1 relaxation, proton density and bolus arrival time (BAT). The accuracy was tested in a digital phantom in comparison to a conventional nonlinear least-squares fitting (NLSQ). In vivo testing was conducted in ten healthy subjects. Regions of interest in the cervix and uterine myometrium were used to calculate the inter-subject variability. The clinical utility was demonstrated on a cervical cancer patient. Test-retest experiments were used to assess reproducibility of the parameter maps in the tumor. The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5 and 51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1 and 47%. The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T1 scan or BAT processing, leading to a reduction of 10 min per scan and more accurate quantification.

Preoperatively predicting human epidermal growth factor receptor 2-low expression in breast cancer using neural network model based on multiparameter magnetic resonance imaging.

Preoperative prediction of human epidermal growth factor receptor 2 (HER2)-low expression using magnetic resonance imaging (MRI) can enhance the selection of clinical treatment strategies and enhance patient outcomes. Herein, we investigated the value of a neural network model constructed with multiparametric MRI in diagnosing HER2-low breast cancer. This retrospective study involved two different centers. A total of 895 breast cancer patients (903 lesions) were enrolled from the Second Hospital of Shandong University (known as "Center 1") between January 2015 to December 2022. They were allocated to the training set (626 cases/632 lesions) and the internal validation set (269 cases/271 lesions). The external validation set included 100 patients (100 lesions) from the Qilu Hospital of Shandong University (referred to as "Center 2") between June 2021 to December 2022. All patients were subgrouped into HER2-low and HER2-0 expression groups. We used t-tests, Wilcoxon rank sum tests, and Chi-squared tests or Fisher's exact test to compare the dynamic contrast-enhanced MRI features (morphological/hemodynamic features), and the apparent diffusion coefficient (ADC) values. A neural network model was constructed using the Neuralnet package in R, with the architecture specified as c(5,2) for the hidden layers. Bootstrapping was used for internal validation. The diagnostic performance in the training set was analyzed using receiver operating characteristic (ROC) curves. The clinical effectiveness of the model was validated using a decision curve analysis (DCA). HER2-low breast cancer lesions had irregular morphology, high early enhancement rate, and low ADC value compared to HER2-0 expressed lesions. The differences were significant (P<0.05). We then constructed a neural network model using these significant variables. ROC analysis showed that the area under the ROC curve of the model for diagnosing HER2-low breast cancer in the training, internal validation, and external validation sets was 0.757 [95% confidence interval (CI): 0.712-0.802], 0.728 (95% CI: 0.658-0.798), and 0.791 (95% CI: 0.693-0.890), respectively. The DCA demonstrated that the net benefit of the model was significantly greater than zero at a predicted probability of 0.764. The neural network model based on MRI features is an effective tool in predicting HER2-low breast cancer, which may facilitate clinical treatment decision-making.

Comparison of normalized cerebral blood flow between different post-processing methods of dynamic susceptibility contrast perfusion-weighted imaging and arterial spin labeling in gliomas with different grading.

Two post-processing methods of dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI), arterial input function (AIF) and gamma-variate fitting (GVF), can both derive cerebral blood flow (CBF). Moreover, AIF can provide T2* and T1 leakage indicators. This study aimed to compare the consistency of normalized CBF between different post-processing methods of DSC-PWI and arterial spin labeling (ASL) in gliomas, and take the quantitative metrics percentage of signal recovery (PSR) as a reference to verify the value of T2* and T1 leakage indicators in characterizing leakage effect and evaluating the grading of gliomas. From 1 January 2020 to 15 December 2023, 56 consecutive inpatients were retrospectively enrolled, comprising 24 patients with low-grade glioma (LGG) and 32 patients with high-grade glioma (HGG). The normalized CBF was derived from AIF, GVF and ASL. The T2* and T1 leakage indicators of AIF were graded by 4-point scale. The consistency and difference of normalized CBF between DSC-PWI and ASL were tested by linear correlation/regression analysis, Bland-Altman plots, and Student's t-test. The correlation between the difference of point for both leakage indicators and PSR was tested by Spearman correlation analysis, then inter-group difference of PSR was compared by t-test. The intra-group and inter-group differences of point for T2* and T1 leakage indicators were compared by χ2/Fisher's exact test. The normalized CBF derived from AIF and GVF were correlated with ASL in both groups (all r>0.7, all P<0.001), and linear regressions were not significantly different in each group (all P>0.05). The difference of normalized CBF between ASL and AIF in the HGG group was larger than that in the LGG group (P=0.017); however, the difference of normalized CBF between ASL and GVF was not significant (P=0.085). The strong correlation between the difference of point for both leakage indicators and PSR was verified (r=-0.739, P<0.0001), and the HGG group was shown to have higher PSR compared with the LGG group (t=2.043, P=0.04). The comparison of intra-group and inter-group differences in T2* leakage and T1 indicators showed that the HGG group was more prone to T1 leakage than the LGG group (P<0.05), and weight of T1 leakage was greater than that of T2* leakage (χ2=11.28, P=0.004). The normalized CBF derived from DSC-PWI has good consistency with ASL in gliomas, regardless of post-processing methods. GVF can provide less bias of normalized CBF between HGG and LGG groups. However, T2* and T1 leakage indicators of AIF can be utilized as a surrogate of PSR to characterize both leakage effects and evaluate glioma grading.

PI-QUAL version 2 image quality categorisation and inter-reader agreement compared to version 1.

Prostate imaging quality (PI-QUAL) was developed to standardise the evaluation of prostate MRI quality and has recently been updated to version 2. This study aims to assess inter-reader agreement for PI-QUAL v1 and v2 scores and investigates changes in MRI quality score categories. The study retrospectively analysed 350 multiparametric MRI (mpMRI) scans. Two expert uroradiologists independently assessed mpMRI quality using PI-QUAL v1 and v2 guidelines. Biparametric MRI (bpMRI) categorisation based on PI-QUAL v2 included only T2WI and diffusion-weighted imaging (DWI) results. Inter-reader agreement was determined using percentage agreement and kappa, and categorisation comparisons were made using the chi-square test. Substantial inter-reader agreement was observed for the overall PI-QUAL v1 score (κ = 0.64) and moderate agreement for v2 mpMRI (κ = 0.54) and v2 bpMRI scores (κ = 0.57). Inter-reader agreements on individual sequences were similar between v1 and v2 (kappa for individual sequences: T2WI, 0.46 and 0.49; DWI, 0.66 and 0.70; DCE, 0.71 and 0.61). Quality levels shifted from predominantly "optimal" in v1 (65%) down to "acceptable" using v2 (55%); p < 0.001. The addition of DCE increased the proportion of cases with at least "adequate" quality at mpMRI (64%) compared to bpMRI (30%); p < 0.001. This study shows consistent inter-reader agreement between PI-QUAL v1 and v2, encompassing overall and individual sequence categorisation. A notable shift from "optimal" to "acceptable" quality was demonstrated when moving from v1 to v2, with DCE tending improving quality from "inadequate" (bpMRI) to "acceptable" (mpMRI). Question What are the agreement levels of image quality of prostate MRI by using PI-QUAL v1 and v2? Findings Inter-reader agreement based on PI-QUAL v1 and v2 is comparable. Dynamic contrast enhancement (DCE) enables an overall shift from inadequate quality (at bpMRI) to acceptable quality (mpMRI). Clinical relevance The inter-reader agreement on PI-QUAL v1 and v2 is equivalent. PI-QUAL v2 assesses prostate bpMRI as well as mpMRI quality. Transitioning from inadequate to acceptable between v2-bpMRI and v2-mpMRI highlights the role of DCE as an "image quality safety net."

Comparison of Arterial Spin-Labeling and DSC Perfusion MR Imaging in Pediatric Brain Tumors: A Systematic Review and Meta-Analysis.

Brain tumors are a leading cause of mortality in children. Accurate tumor grading is essential to plan treatment and for prognostication. Perfusion imaging has been shown to correlate well with tumor grade in adults, however there are fewer studies in pediatric patients. Moreover, there is no consensus regarding which MR perfusion technique demonstrates the highest accuracy in the latter population. We sought to compare the diagnostic test accuracy of DSC and arterial spin-labeling (ASL), in their ability to differentiate between low- and high-grade pediatric brain tumors at first presentation. Articles were retrieved from online electronic databases: MEDLINE (Ovid), Web of Science Core Collection, and Scopus. Studies in pediatric patients with a treatment-naïve diagnosed brain tumor and imaging including either ASL or DSC or both, together with a histologic diagnosis were included. Studies involving adult patient or mixed age populations, studies with incomplete data, and those that used dynamic contrast-enhanced perfusion were excluded. The sensitivities and specificities obtained from each study were used to calculate the true-positive, true-negative, false-positive, and false-negative count. A case was defined as a histologically proved high-grade tumor. The random-effect model was used to merge statistics. Significance level was set at P < .05. Forest plots showing pairs of sensitivity and specificity, with their 95% CIs, were constructed for each study. The bivariate model was applied to account for between-study variability. The summary receiver operating characteristics (SROC) plots were constructed from the obtained data sets. The area under the curve for the SROC of all studies was estimated to determine the overall diagnostic test accuracy of perfusion MRI, followed by a separate comparison of the SROC of ASL versus DSC studies. There was a small and heterogeneous sample size. The diagnostic accuracy of ASL was found to be comparable and not inferior to DSC, thus its use in the diagnostic assessment of pediatric patients should continue to be supported.

Intravoxel incoherent motion imaging in stroke infarct core and penumbra is related to long-term clinical outcome

Intravoxel incoherent motion (IVIM) imaging, a contrast agent-free magnetic resonance imaging technique, enables the evaluation of microvascular perfusion abnormalities in acute stroke. Prior research reported reduced IVIM values within the infarct core in acute stroke. However, findings concerning IVIM characteristics in the penumbra have been mixed and the relationship between IVIM and clinical outcomes remains unknown. We employed a longitudinal multimodal imaging approach for ischemic stroke patients (n analyzed=24; pre-/post-treatment and 90-day post-stroke assessments) including IVIM, diffusion-weighted, and contrast-enhanced perfusion-weighted imaging. We evaluated IVIM in relevant stroke areas after endovascular treatment. Reduced post-treatment IVIM perfusion fraction in infarct core and recanalized penumbra was associated with poorer functional recovery at 90-days post-stroke (NIH Stroke Scale [NIHSS]; r=-0.64 and r=-0.69). Including IVIM perfusion fraction increased the explained variance of NIHSS from 42% up to 83% compared to well-known prognostic factors core volume and patient age. Additionally, IVIM perfusion fraction was reduced in the core and recanalized penumbra compared to contralateral healthy tissue, suggesting impaired microvascular reperfusion after endovascular treatment. In conclusion, IVIM characteristics of the infarct core and recanalized penumbra are strong prognostic factors for long-term outcome in stroke patients and IVIM shows promise for characterizing microvascular perfusion in relevant stroke areas.

MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer

ObjectiveLymphovascular invasion (LVI) is critical for the effective treatment and prognosis of breast cancer (BC). This study aimed to investigate the value of eight machine learning models based on MRI radiomic features for the preoperative prediction of LVI status in BC.MethodsA total of 454 patients with BC with known LVI status who underwent breast MRI were enrolled and randomly assigned to the training and validation sets at a ratio of 7:3. Radiomic features were extracted from T2WI and dynamic contrast-enhanced (DCE) of MRI sequences, the optimal feature filter and LASSO algorithm were used to obtain the optimal features, and eight machine learning algorithms, including LASSO, logistic regression, random forest, k-nearest neighbor (KNN), support vector machine, gradient boosting decision tree, extreme gradient boosting, and light gradient boosting machine, were used to construct models for predicating LVI status in BC. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the performance of the models.ResultsEighteen radiomic features were retained to construct the radiomic signature. Among the eight machine learning algorithms, the KNN model demonstrated superior performance to the other models in assessing the LVI status of patients with BC, with an accuracy of 0.696 and 0.642 in training and validation sets, respectively.ConclusionThe eight machine learning models based on MRI radiomics serve as reliable indicators for identifying LVI status, and the KNN model demonstrated superior performance.This model offers substantial clinical utility, facilitating timely intervention in invasive BC and ultimately aiming to enhance patient survival rates.

Locus coeruleus MRI contrast, cerebral perfusion, and plasma Alzheimer’s disease biomarkers in older adults

The locus coeruleus (LC) is among the first brain structures impacted by Alzheimer’s disease (AD), and noradrenergic denervation may contribute to early neurovascular dysfunction in AD. Mechanistic links between the LC and cerebral perfusion have been demonstrated in rodents, but there have been no similar studies in aging humans. Community-dwelling older adults with no history of stroke or dementia (N=66) underwent structural (T1-MPRAGE; T1-FSE) and perfusion (resting pCASL) MRI. Plasma AD biomarkers levels were evaluated for Aβ42/40 ratio (n=56) and pTau181 (n=60). Higher rostral LC structural MRI contrast was associated with lower perfusion in entorhinal and limbic regions but higher perfusion in lateral and medial orbitofrontal cortices. Relationships between LC structure and regional cerebral perfusion were attenuated in older adults with higher plasma pTau levels and lower plasma Aβ42/40 ratios. Previously unstudied links between LC structure and cerebral perfusion are detectible in older adults using MRI and are attenuated in those showing greater AD pathophysiologic change, suggesting an uncoupling of LC-cerebral perfusion relationships in older adults with aggregating AD-related pathophysiology.

Advancing Cancer Care: A Critical Review of MRI Imaging in Pretherapeutic Malignancy Assessment

Magnetic Resonance Imaging (MRI) has become an indispensable tool in the pretherapeutic assessment of malignancies, offering unparalleled soft tissue contrast, advanced functional imaging capabilities, and the ability to provide non-invasive, radiation-free evaluations. This critical review explores the role of MRI in accurately diagnosing, staging, and guiding treatment strategies for various cancers, including breast, prostate, liver, and brain malignancies. Advanced techniques such as diffusion-weighted imaging (DWI), dynamic contrast-enhanced MRI (DCE-MRI), and the integration of artificial intelligence are discussed for their potential to enhance diagnostic precision and treatment planning. Despite its significant advantages, including superior detection of occult lesions, MRI faces challenges such as high costs, accessibility, and occasional diagnostic pitfalls. This review emphasizes MRI's transformative impact on cancer management, discusses its limitations, and highlights ongoing innovations that aim to improve its clinical utility and accessibility. The findings underscore MRI’s pivotal role in enhancing personalized medicine and optimizing therapeutic outcomes for cancer patients.

Dynamic contrast-enhanced and diffusion-weighted MR imaging for predicting tumor growth of sporadic vestibular schwannomas: a prospective study.

Advanced MR imaging, such as diffusion-weighted (DWI) and dynamic contrast-enhanced (DCE) imaging, may provide valuable non-invasive information on intrinsic tumor biology. This study aims to evaluate apparent diffusion coefficient (ADC) and DCE-MRI-derived microvascular parameter values (Ktrans, ve, and vp) as potential imaging predictors for future sporadic vestibular schwannoma (VS) growth. In this prospective cohort study, patients with newly diagnosed unilateral sporadic VS and an initial wait-and-scan strategy were enrolled between January 2021 and January 2023. Patients underwent a single timepoint comprehensive MRI protocol, including DWI and DCE-MRI sequences. The estimated values of ADC, Ktrans, ve, and vp were calculated using established pipelines on a voxelwise basis within the delineated tumor region of interest. Associations of the estimated parameter values with volumetric growth were evaluated in uni- and multivariable logistic regression and survival analyses. Of the 110 analyzed patients, 70 (64%) exhibited growth during follow-up. A significant correlation was primarily observed between the DCE-MRI-derived parameters and VS growth. The combination of mean Ktrans (P<0.001) and ve (P<0.001) tumor values provided an internally validated model with an AUC of 0.85 for growth, yielding a sensitivity of 89% and specificity of 73% at the optimized cutoff value. Only the mean ADC values were found to be significantly higher in shrinking tumors (P=0.04). The strongly significant correlation observed between VS growth and Ktrans and ve tumor values indicates great potential of the non-invasive DCE-MRI for individualized VS management in clinical practice. External validation is needed to further substantiate these findings.

Hyperperfusion and blood-brain barrier disruption beyond the diffusion-restricted infarct one day after successful mechanical thrombectomy.

Patterns of the cerebral microcirculatory response with changes in the blood brain barrier and perfusion in patients with stroke and a large vessel occlusion are still unclear. We combined dynamic contrast enhancement (DCE) permeability and DSC perfusion MRI to detect such patterns beyond the borders of the diffusion-restricted infarct core after successful recanalization. Combined DCE permeability and DSC perfusion MRI were performed prospectively in patients within 24h after successful mechanical recanalization of acute middle cerebral artery occlusion. Perfusion alterations were visually assessed on CBF and CBV maps, blood-brain-barrier disruptions (BBBD) on ktrans-maps and quantitatively evaluated with an ipsi-to contralateral ratio. Additionally, logistic regression analysis was performed for favorable early clinical outcome (NIHSS ≤2 at discharge). N=38 patients were included in the study. Subtle hyperperfusion beyond the DWI-lesion was present in 13/38 patients (34%) on CBF-maps and elevated CBV in 15/38 patients (39%). In these patients, the ratios between ipsi-and contralateral white matter CBF (p=0.01) and CBV (p<0.01) were elevated compared to patients with normal readings. Subtle, but visually and quantitatively elevated ktrans-values outside the DWI-lesion were observed in 7/38 patients (18%). None of these perfusion alterations were related to clinical outcome. Combined DCE-permeability and DSC-perfusion imaging is feasible in patients 24 hours after successful thrombectomy and reveals subtle hyperperfusion and BBBD occuring frequently beyond the diffusion restricted infarct core. DCE = Dynamic Contrast Enhancement, BBBD = blood brain barrier disruption, MT = Mechanical thrombectomy.

A signal model for fat-suppressed T1-mapping and dynamic contrast-enhanced MRI with interrupted spoiled gradient-echo readout.

The conventional gradient-echo steady-state signal model is the basis of various spoiled gradient-echo (SPGR) based quantitative MRI models, including variable flip angle (VFA) MRI and dynamic contrast-enhanced MRI (DCE). However, including preparation pulses, such as fat suppression or saturation bands, disrupts the steady-state and leads to a bias in T1 and DCE parameter estimates. This work introduces a signal model that improves the accuracy of VFA T1-mapping and DCE for interrupted spoiled gradient-echo (I-SPGR) acquisitions. The proposed model was applied to a VFA T1-mapping I-SPGR sequence in the Gold Standard T1-phantom (3T), in the brain of four healthy volunteers (3 T), and to an abdominal DCE examination (1.5T). T1-values obtained with the proposed and conventional model were compared to reference T1-values. Bland-Altman analysis (phantom) and analysis of variance (in vivo) were used to test whether bias from both methods was significantly different (p= 0.05). The proposed model outperformed the conventional model by decreasing the bias in the phantom with respect to the phantom reference values (mean bias -2 vs. -35% at 3T) and in vivo with respect to the conventional SPGR (-6 vs. -37% bias in T1, p< 0.01). The proposed signal model estimated approximately 48% (depending on baseline T1) higher contrast concentrations in vivo, which resulted in decreased DCE pharmacokinetic parameter estimates of up to 35%. The proposed signal model improves the accuracy of quantitative parameter estimation from disrupted steady-state I-SPGR sequences. It therefore provides a flexible method for applying fat suppression, saturation bands, and other preparation pulses in VFA T1-mapping and DCE.

Dynamic contrast‑enhanced MRI combined with intravoxel incoherent motion in quantitative evaluation for preoperative risk stratification of resectable rectal adenocarcinoma.

Rectal adenocarcinoma is a common malignant tumor of the digestive tract. However, it is difficult to obtain tumor microstructural information using conventional magnetic resonance imaging (MRI). Dynamic contrast-enhanced (DCE)-MRI and intravoxel incoherent motion (IVIM) have been used in tumor research. The present study aimed to explore the application of DCE-MRI and IVIM in the risk stratification of resectable rectal adenocarcinoma. To achieve this, 75 patients with rectal adenocarcinoma confirmed via postoperative pathological examination who underwent high-resolution MRI, IVIM and DCE-diffusion-weighted imaging before surgery were retrospectively enrolled and divided into very low-risk (19 cases), low-risk (29 cases) and medium-risk (27 cases) groups. The quantitative parameters of DCE-MRI and IVIM were obtained using the GenIQ and Medical Imaging Interaction Toolkit software, respectively. One-way analysis of variance or the Kruskal-Wallis H test were used to analyze the differences in the measured parameters between the different risk groups. Receiver operating characteristic curves were used to analyze the diagnostic efficacy of each parameter. The differences in rate constant (Kep), perfusion fraction (f) and false diffusion coefficient (D*) between the different risk stratification groups were statistically significant (P<0.05). When the very low- and low-risk groups were compared, f + D* had the highest diagnostic efficiency [area under the curve (AUC)=0.719], with a sensitivity and specificity of 100 and 51.70%, respectively. When the low- and medium-risk groups were compared, Kep had the highest diagnostic efficiency (AUC=0.602), with a sensitivity and specificity of 72.41 and 55.56%, respectively. When the very low-risk and medium-risk groups were compared, Kep + f + D* had the highest diagnostic efficiency (AUC=0.887), with a sensitivity and specificity of 100 and 70.4%, respectively. Thus, DCE-MRI and IVIM can aid the prognostic risk stratification of resectable rectal adenocarcinoma, and Kep, f and D* are potential quantitative imaging parameters for the risk stratification of rectal adenocarcinoma.

Tumor Morphology for Prediction of Poor Responses Early in Neoadjuvant Chemotherapy for Breast Cancer: A Multicenter Retrospective Study.

This multicenter and retrospective study investigated the additive value of tumor morphologic features derived from the functional tumor volume (FTV) tumor mask at pre-treatment (T0) and the early treatment time point (T1) in the prediction of pathologic outcomes for breast cancer patients undergoing neoadjuvant chemotherapy. A total of 910 patients enrolled in the multicenter I-SPY 2 trial were included. FTV and tumor morphologic features were calculated from the dynamic contrast-enhanced (DCE) MRI. A poor response was defined as a residual cancer burden (RCB) class III (RCB-III) at surgical excision. The area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive performance. The analysis was performed in the full cohort and in individual sub-cohorts stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status. In the full cohort, the AUCs for the use of the FTV ratio and clinicopathologic data were 0.64 ± 0.03 (mean ± SD [standard deviation]). With morphologic features, the AUC increased significantly to 0.76 ± 0.04 (p < 0.001). The ratio of the surface area to volume ratio between T0 and T1 was found to be the most contributing feature. All top contributing features were from T1. An improvement was also observed in the HR+/HER2- and triple-negative sub-cohorts. The AUC increased significantly from 0.56 ± 0.05 to 0.70 ± 0.06 (p < 0.001) and from 0.65 ± 0.06 to 0.73 ± 0.06 (p < 0.001), respectively, when adding morphologic features. Tumor morphologic features can improve the prediction of RCB-III compared to using FTV only at the early treatment time point.

Pharmacological manipulation of neurotransmitter activity induces disparate effects on cerebral blood flow and resting-state fluctuations

Abstract Functional MRI methods can assess aspects of drug-induced brain response. Resting blood oxygenation level dependent (BOLD) fMRI and arterial spin labeling (ASL) perfusion MRI indirectly measure brain function through the coupling of activity to cerebral blood flow (CBF) and oxygenation but their relative sensitivity has not been directly compared. We assessed changes in resting measures of BOLD and ASL MRI in response to two neurotransmitter modulators: citalopram, a selective serotonin reuptake inhibitor, and alprazolam, a positive allosteric modulator of GABA type A receptor. Thirty healthy subjects were imaged in a placebo-controlled study, with N = 20 subjects receiving each treatment as part of an incomplete block design. Time-averaged CBF images from ASL and measures of resting-state fluctuations of BOLD and ASL images were assessed for significant effects. Following acute citalopram administration, analysis of the ASL data showed a reduction in time-averaged regional CBF in regions associated with high levels of 5-HT1A receptor density. In contrast, following alprazolam administration, BOLD amplitude of low-frequency fluctuations showed a highly significant and cortically widespread increase, consistent with the distribution of GABA-A receptors. Only a marginal decrease in ASL CBF was detected after alprazolam intake. BOLD and ASL are each sensitive to drugs targeting neurotransmitter systems, but appear to reflect different aspects of neural metabolism and the balance between excitatory and inhibitory activity. Accordingly, their combination may best capture the effects of neurotransmitter modulations, and thus be advantageous for pharmacological MRI studies.

Natural Evolution of Incomplete Reperfusion in Patients following Endovascular Therapy after Ischemic Stroke.

A third of endovascularly treated stroke patients experience incomplete reperfusion (expanded Thrombolysis in Cerebral Infarction, eTICI<3) and the natural evolution of this incomplete reperfusion remains unknown. We systematically reviewed literature and performed a meta-analysis on the natural evolution of incomplete reperfusion after endovascular therapy. A systematic review of MEDLINE, Embase and PubMed up until March 1, 2024 using a predefined strategy. Only full-text English written articles reporting rates of either favorable (i.e., delayed reperfusion or no new infarct) or unfavorable progression (i.e., persistent perfusion deficit or new infarct) of incompletely reperfused tissue were included. Primary outcome was the rate of delayed reperfusion and its association with functional independence (modified Rankin Scale, mRS 0-2) at 90 days post-intervention. Pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated using a random-effects model. Six studies involving 950 patients (50.7% female; median age 71, IQR 60-79) were included. Four studies assessed the evolution of incomplete reperfusion on MRI perfusion imaging, while two studies used DWI and NCCT imaging, where new infarct was used to denote unfavorable progression. Five studies defined incomplete reperfusion as eTICI2b50 or 2c. Delayed reperfusion occurred in 41% (IQR 33%-51%) of cases 24h post-intervention. Achieving delayed reperfusion was associated with higher likelihood of functional independence at 90 days (OR 2.5, 95%CI 1.9-3.4). Nearly half of eTICI<3 patients achieve delayed reperfusion, leading to favorable clinical outcome. This subgroup may derive limited or potentially harmful effects from pursuing additional reperfusion strategies (e.g., intra-arterial lytics or secondary thrombectomy). Accurately predicting the evolution of incomplete reperfusion could optimize patient selection for adjunctive reperfusion strategies at the end of an intervention.

Radiomic features of dynamic contrast-enhanced MRI can predict Ki-67 status in head and neck squamous cell carcinoma

PurposeThis study aimed to investigate the potential of radiomic features derived from dynamic contrast-enhanced MRI (DCE-MRI) in predicting Ki-67 and p16 status in head and neck squamous cell carcinoma (HNSCC). Materials and methodsA cohort of 124 HNSCC patients who underwent pre-surgery DCE-MRI were included and divided into training and test set (7:3), further subgroup analysis was performed for 104 cases with oral squamous cell carcinoma (OSCC). Radiomics features were extracted from DCE images. The least absolute shrinkage and selection operator (LASSO) was used for radiomics features selection, and receiver operating characteristics analysis for predictive performance assessment. The nomogram's performance was evaluated using decision curve analysis (DCA). ResultsTen DCE-MRI features were identified to build the predictive model of HNSCC, demonstrating excellent predictive value for Ki-67 status in both the training set (AUC of 0.943) and test set (AUC of 0.801). The nomograms based on the predictive model showed good fit in the calibration curves (p > 0.05), and DCA indicated its high clinical usefulness. In subgroup analysis of OSCC, fourteen features were selected to build the predictive model for Ki-67 status with an AUC of 0.960 in training set and 0.817 in test set. No features could be included to establish a model to predict p16 status. ConclusionThe radiomics model utilizing DCE-MRI features could effectively predict Ki-67 status in HNSCC patients, offering potential for noninvasive preoperative prediction of Ki-67 status.