Journal of Magnetic Resonance Imaging最新文献

Set during the Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), the "Clinical Focus Meeting" (CFM) aims to bridge the gap between innovative magnetic resonance imaging (MRI) scientific research and daily patient care. This initiative is dedicated to maximizing the impact of MRI technology on healthcare outcomes for patients. At the 2023 Annual Meeting, clinicians and scientists from across the globe were invited to discuss neuroinflammation from various angles (entitled "Imaging the Fire in the Brain"). Topics ranged from fundamental mechanisms and biomarkers of neuroinflammation to the role of different contrast mechanisms, including both proton and non-proton techniques, in brain tumors, autoimmune disorders, and pediatric neuroinflammatory diseases. Discussions also delved into how systemic inflammation can trigger neuroinflammation and the role of the gut-brain axis in causing brain inflammation. Neuroinflammation arises from various external and internal factors and serves as a vital mechanism to mitigate tissue damage and provide neuroprotection. Nonetheless, excessive neuroinflammatory responses can lead to significant tissue injury and subsequent neurological impairments. Prolonged neuroinflammation can result in cellular apoptosis and neurodegeneration, posing severe consequences. MRI can be used to visualize these consequences, by detecting blood-brain barrier damage, characterizing brain lesions, quantifying edema, and identifying specific metabolites. It also facilitates monitoring of chronic changes in both the brain and spinal cord over time, potentially leading to better patient outcomes. This paper represents a summary of the 2023 CFM, and is intended to guide the enthusiastic MR user to several key and novel sequences that MRI offers to image pathophysiologic processes underlying acute and chronic neuroinflammation. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 3.

Background: The von Hippel-Lindau (VHL) mutation is an important alteration in clear cell renal cell carcinoma (ccRCC); however, its imaging phenotype remains unclear.

Purpose: To investigate whether MRI features can reflect the VHL mutation status.

Study type: Retrospective.

Field strength/sequence: 3 T/fast spin echo T2-weighted, spin-echo echo planar diffusion-weighted, gradient recalled echo T1-weighted, gradient recalled echo chemical-shift T1-weighted, and contrast-enhanced gradient recalled echo T1-weighted sequences.

Population: One hundred five patients with ccRCC who underwent preoperative contrast-enhanced MRI and subsequent genomic sequencing: 59 consecutive patients from our institution (38 [64.41%] with VHL mutations) formed a training cohort, and 46 from The Cancer Genome Atlas (TCGA) database (24 [52.17%] with VHL mutations) formed an independent test cohort.

Assessment: Two radiologists, with 23 and 33 years of experience respectively, jointly evaluated the semantic MRI features of the primary lesion in ccRCCs to propose potential features related to VHL mutations in both cohorts. Three additional readers, with 5, 7, and 10 years of experience respectively, independently reviewed all lesions to assess the interobserver agreement of MRI features. A VHL mutational likelihood score (VHL-MULIS) system was constructed using the training cohort and validated using the independent test cohort.

Statistical tests: Fisher's test or chi-square test, t-test or Mann-Whitney U test, logistic regression, Cohen's kappa (κ), area under the receiver operating characteristic curve (AUC). A two-sided P value <0.05 was considered statistically significant.

Results: In both the local and public cohorts, T2-weighted signal intensity and presence of microscopic fat from primary lesions were significantly associated with VHL mutation status. The VHL-MULIS incorporated maximum diameter, T2-weighted signal intensity, and presence of microscopic fat in the training cohort and demonstrated promising diagnostic ability (AUC, 0.82; sensitivity, 0.79; specificity, 0.82) and substantial interobserver agreement (κ, 0.787) in the test cohort.

Data conclusion: The VHL mutation exhibited a distinct MRI phenotype. Integrating multiple semantic MRI features has potential to reflect the mutation status in patients with ccRCC.

Evidence level: 3 TECHNICAL EFFICACY: Stage 2.

Background: Quantitative parametric mapping is an increasingly important tool for noninvasive assessment of chronic liver disease. Conventional parametric mapping techniques require multiple breath-held acquisitions and provide limited anatomic coverage.

Purpose: To investigate a multi-inversion spin and gradient echo (MI-SAGE) technique for simultaneous estimation of T₁, T₂, and T₂* of the liver.

Study type: Prospective.

Subjects: Sixteen research participants, both adult and pediatric (age 17.5 ± 4.6 years, eight male), with and without known liver disease (seven asymptomatic healthy controls, two fibrotic liver disease, five steatotic liver disease, and two fibrotic and steatotic liver disease).

Field strength/sequence: 1.5 T, single breath-hold and respiratory triggered MI-SAGE, breath-hold modified Look-Locker inversion recovery (MOLLI, T₁ mapping), breath-hold gradient and spin echo (GRASE, T₂ mapping), and multiple gradient echo (mGRE, T₂* mapping) sequences.

Assessment: Agreement between hepatic T₁, T₂, and T₂* estimated using MI-SAGE and conventional parametric mapping sequences was evaluated. Repeatability and reproducibility of MI-SAGE were evaluated using a same-session acquisition and second-session acquisition.

Statistical tests: Bland-Altman analysis with bias assessment and limits of agreement (LOA) and intraclass correlation coefficients (ICC).

Results: Hepatic T₁, T₂, and T₂* estimates obtained using the MI-SAGE technique had mean biases of 72 (LOA: -22 to 166) msec, -3 (LOA: -10 to 5) msec, and 2 (LOA: -5 to 8) msec (single breath-hold) and 36 (LOA: -43 to 120) msec, -3 (LOA: -17 to 11) msec, and 4 (LOA: -3 to 11) msec (respiratory triggered), respectively, in comparison to conventional acquisitions using MOLLI, GRASE, and mGRE. All MI-SAGE estimates had strong repeatability and reproducibility (ICC > 0.72).

Data conclusion: Hepatic T₁, T₂, and T₂* estimates obtained using an MI-SAGE technique were comparable to conventional methods, although there was a 12%/6% for breath-hold/respiratory triggered underestimation of T₁ values compared to MOLLI. Both respiratory triggered and breath-hold MI-SAGE parameter maps demonstrated strong repeatability and reproducibility.

Level of evidence: 1 TECHNICAL EFFICACY: Stage 2.

Background: Pancreatic cancer has a poor prognosis. Targeting Kirsten Rat Sarcoma (KRAS) mutation and its related pathways may enhance immunotherapy efficacy. While in vivo monitoring of therapeutic response and immune cell migration remains challenging, Fluorine-19 MRI (¹⁹F MRI) may allow noninvasive longitudinal imaging of immune cells.

Purpose: Evaluating the potential of ¹⁹F MRI for monitoring changes in the tumor immune microenvironment, in response to combined SHP2/MEK inhibition.

Study type: Pre-clinical animal study.

Animal model: Murine genetically engineered pancreatic cancer model (N = 20, both sexes).

Field strength/sequence: 9.4-T, two-dimensional multi-slice Rapid Acquisition with Relaxation Enhancement sequence. Intravenous injection of ¹⁹F-perfluorocarbon (PFC) nanoparticles.

Assessment: Upon tumor detection by conventional ¹H MRI screening, ¹⁹F MRI was performed in mice 24 hours after PFC nanoparticle administration. Animals were randomly assigned to four treatment groups: allosteric Src-homology-2-containing protein tyrosine phosphatase 2 (SHP2) inhibitor SHP099, the mitogen-activated extracellular signal-regulated kinase 1/2 (MEK1/2) inhibitor Trametinib, the combination of both, or a vehicle control (4 to 6 mice each group), administered every other day per oral gavage. ¹H and ¹⁹F MRI was repeated 7 days and 14 days later. Pancreatic immune cell infiltrates were analyzed by flow cytometry and multiplex immunohistofluorescence (mIHF) upon sacrifice.

Statistical tests: Independent t-tests and one-way analysis of variance.

Results: ¹⁹F MRI revealed continuous decrease of PFC-signals in tumors from vehicle controls (100%, 80%, and 74% on days 0, 7, and 14, respectively), contrasting with stable or increasing signals under KRAS-pathway-directed treatment. MEK inhibition showed 100%, 152%, and 84% and dual SHP2/MEK1/2 inhibition demonstrated signals of 100%, 134%, and 100% on days 0, 7, 14, respectively. mIHF analyses indicated CD11b⁺ macrophages/monocytes as primary contributors to the observed ¹⁹F MRI signal differences.

Data conclusion: ¹⁹F MRI might provide non-invasive longitudinal estimates for abundance and spatial distribution of CD11b⁺ macrophages/monocytes in pancreatic cancer.

Evidence level: 1 TECHNICAL EFFICACY: Stage 2.

Background: While changes in brain metabolites after injury have been reported, relationships between metabolite changes and head impacts are less characterized.

Purpose: To investigate alterations in neurochemistry in high school athletes as a function of head impacts, concussion, and the use of a jugular vein compression (JVC) collar.

Study type: Prospective controlled trial.

Subjects: A total of 284 male American football players, divided into JVC collar and noncollar groups; 215 included in final analysis (age = 15.9 ± 1.0 years; 114 in collar group).

Field strength/sequence: 3 Tesla/T₁-weighted gradient echo, ¹H point resolved spectroscopy, acquired between August and November 2018.

Assessment: Head impacts were quantified using accelerometers. Concussion was diagnosed by medical professionals for each team. Pre- to postseason differences in total N-acetylaspartate (tNAA), total choline (tCho), myo-inositol (myoI), and glutamate + glutamine (Glx), in primary motor cortex (M1) and anterior cingulate cortex (ACC), relative to total creatine (tCr), were determined.

Statistical tests: Group-wise comparisons were performed using Wilcoxon signed-rank, Friedman's, and Mann-Whitney U tests. Relationships between ∆metabolite/tCr and mean g-force were analyzed using linear regressions accounting for concussion and JVC collar. Significance was set at P ≤ 0.05.

Results: In participants without concussion, a significant decrease in tCho/tCr (0.233 ± 1.40 × 10^-3 to 0.227 ± 1.47 × 10^-7) and increase in Glx/tCr (1.60 ± 8.75 × 10^-3 to 1.63 ± 1.08 × 10^-2) in ACC were observed pre- to postseason. The relationship between ∆tCho/tCr in M1 and ACC and mean g-force from >80 g to >140 g differed significantly between participants with and without concussion (M1 β ranged from 3.9 × 10^-3 to 2.1 × 10^-3; ACC β ranged from 2.7 × 10^-3 to 2.1 × 10^-3). Posthoc analyses revealed increased tCho/tCr in M1 was positively associated with mean g-force >100 g (β = 3.6 × 10^-3) and >110 g (β = 2.9 × 10^-3) in participants with concussion. Significant associations between $∆\text{myoI}/\mathrm{tCr}$ in ACC and mean g-force >110 g (β = -1.1 × 10^-3) and >120 g (β = -1.1 × 10^-3) were observed in the collar group only.

Data conclusion: Diagnosed concussion and the use of a JVC collar result in distinct neurochemical trends after repeated head impacts.

Level of evidence: 2 TECHNICAL EFFICACY: Stage 3.

Presenting quantitative data using non-standardized color maps potentially results in unrecognized misinterpretation of data. Clinically meaningful color maps should intuitively and inclusively represent data without misleading interpretation. Uniformity of the color gradient for color maps is critically important. Maximal color and lightness contrast, readability for color vision-impaired individuals, and recognizability of the color scheme are highly desirable features. This article describes the use of color maps in five key quantitative MRI techniques: relaxometry, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI, MR elastography (MRE), and water-fat MRI. Current display practice of color maps is reviewed and shortcomings against desirable features are highlighted. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Background: Gliomas are highly invasive brain neoplasms. MRI is the most important tool to diagnose and monitor glioma but has shortcomings. In particular, the assessment of tumor cell invasion is insufficient. This is a clinical dilemma, as recurrence can arise from MRI-occult glioma cell invasion.

Hypothesis: Tumor cell invasion, tumor growth and radiotherapy alter the brain parenchymal microstructure and thus are assessable by diffusion tensor imaging (DTI) and MR elastography (MRE).

Study type: Experimental, animal model.

Animal model: Twenty-three male NMRI nude mice orthotopically implanted with S24 patient-derived glioma cells (experimental mice) and 9 NMRI nude mice stereotactically injected with 1 μL PBS (sham-injected mice).

Field strength/sequence: 2D and 3D T2-weighted rapid acquisition with refocused echoes (RARE), 2D echo planar imaging (EPI) DTI, 2D multi-slice multi-echo (MSME) T2 relaxometry, 3D MSME MRE at 900 Hz acquired at 9.4 T (675 mT/m gradient strength).

Assessment: Longitudinal 4-weekly imaging was performed for up to 4 months. Tumor volume was assessed in experimental mice (n = 10 treatment-control, n = 13 radiotherapy). The radiotherapy subgroup and 5 sham-injected mice underwent irradiation (3 × 6 Gy) 9 weeks post-implantation/sham injection. MRI-/MRE-parameters were assessed in the corpus callosum and tumor core/injection tract. Imaging data were correlated to light sheet microscopy (LSM) and histology.

Statistical tests: Paired and unpaired t-tests, a P-value ≤0.05 was considered significant.

Results: From week 4 to 8, a significant callosal stiffening (4.44 ± 0.22 vs. 5.31 ± 0.29 kPa) was detected correlating with LSM-proven tumor cell invasion. This was occult to all other imaging metrics. Histologically proven tissue destruction in the tumor core led to an increased T2 relaxation time (41.65 ± 0.34 vs. 44.83 ± 0.66 msec) and ADC (610.2 ± 12.27 vs. 711.2 ± 13.42 × 10^-6 mm²/s) and a softening (5.51 ± 0.30 vs. 4.24 ± 0.29 kPa) from week 8 to 12. Radiotherapy slowed tumor progression.

Data conclusion: MRE is promising for the assessment of key glioma characteristics.

Evidence level: NA TECHNICAL EFFICACY: Stage 2.

Background: Pathological axillary lymph node (pALN) burden is an important factor for treatment decision-making in clinical T1-T2 (cT1-T2) stage breast cancer. Preoperative assessment of the pALN burden and prognosis aids in the individualized selection of therapeutic approaches.

Purpose: To develop and validate a machine learning (ML) model based on clinicopathological and MRI characteristics for assessing pALN burden and survival in patients with cT1-T2 stage breast cancer.

Study type: Retrospective.

Population: A total of 506 females (range: 24-83 years) with cT1-T2 stage breast cancer from two institutions, forming the training (N = 340), internal validation (N = 85), and external validation cohorts (N = 81), respectively.

Field strength/sequence: This study used 1.5-T, axial fat-suppressed T2-weighted turbo spin-echo sequence and axial three-dimensional dynamic contrast-enhanced fat-suppressed T1-weighted gradient echo sequence.

Assessment: Four ML methods (eXtreme Gradient Boosting [XGBoost], Support Vector Machine, k-Nearest Neighbor, Classification and Regression Tree) were employed to develop models based on clinicopathological and MRI characteristics. The performance of these models was evaluated by their discriminative ability. The best-performing model was further analyzed to establish interpretability and used to calculate the pALN score. The relationships between the pALN score and disease-free survival (DFS) were examined.

Statistical tests: Chi-squared test, Fisher's exact test, univariable logistic regression, area under the curve (AUC), Delong test, net reclassification improvement, integrated discrimination improvement, Hosmer-Lemeshow test, log-rank, Cox regression analyses, and intraclass correlation coefficient were performed. A P-value <0.05 was considered statistically significant.

Results: The XGB II model, developed based on the XGBoost algorithm, outperformed the other models with AUCs of 0.805, 0.803, and 0.818 in the three cohorts. The Shapley additive explanation plot indicated that the top variable in the XGB II model was the Node Reporting and Data System score. In multivariable Cox regression analysis, the pALN score was significantly associated with DFS (hazard ratio: 4.013, 95% confidence interval: 1.059-15.207).

Data conclusion: The XGB II model may allow to evaluate pALN burden and could provide prognostic information in cT1-T2 stage breast cancer patients.

Level of evidence: 3 TECHNICAL EFFICACY: Stage 2.