Journal of Magnetic Resonance Imaging最新文献

Background: The human epidermal growth factor receptor 2 (HER2) has recently emerged as hotspot in targeted therapy for urothelial bladder cancer (UBC). The HER2 status is mainly identified by immunohistochemistry (IHC), preoperative and noninvasive methods for determining HER2 status in UBC remain in searching.

Purposes: To investigate whether radiomics features extracted from MRI using machine learning algorithms can noninvasively evaluate the HER2 status in UBC.

Study type: Retrospective.

Population: One hundred ninety-five patients (age: 68.7 ± 10.5 years) with 14.3% females from January 2019 to May 2023 were divided into training (N = 156) and validation (N = 39) cohorts, and 43 patients (age: 67.1 ± 13.1 years) with 13.9% females from June 2023 to January 2024 constituted the test cohort (N = 43).

Field strength/sequence: 3 T, T2-weighted imaging (turbo spin-echo), diffusion-weighted imaging (breathing-free spin echo).

Assessment: The HER2 status were assessed by IHC. Radiomics features were extracted from MRI images. Pearson correlation coefficient and the least absolute shrinkage and selection operator (LASSO) were applied for feature selection, and six machine learning models were established with optimal features to identify the HER2 status in UBC.

Statistical tests: Mann-Whitney U-test, chi-square test, LASSO algorithm, receiver operating characteristic analysis, and DeLong test.

Results: Three thousand forty-five radiomics features were extracted from each lesion, and 22 features were retained for analysis. The Support Vector Machine model demonstrated the best performance, with an AUC of 0.929 (95% CI: 0.888-0.970) and accuracy of 0.859 in the training cohort, AUC of 0.886 (95% CI: 0.780-0.993) and accuracy of 0.846 in the validation cohort, and AUC of 0.712 (95% CI: 0.535-0.889) and accuracy of 0.744 in the test cohort.

Data conclusion: MRI-based radiomics features combining machine learning algorithm provide a promising approach to assess HER2 status in UBC noninvasively and preoperatively.

Evidence level: 2 TECHNICAL EFFICACY: Stage 3.

Background: Portal vein thrombosis (PVT) is thought to arise from stagnant blood flow, yet conclusive evidence is lacking. Relative residence time (RRT) assessed using 4D Flow MRI may offer insight into portal flow stagnation.

Purpose: To explore the relationship between RRT values and the presence of PVT in cirrhotic participants.

Study type: Prospective.

Population: Forty-eight participants with liver cirrhosis (27 males, median age 67 years [IQR: 57-73]) and 20 healthy control participants (12 males, median age 45 years [IQR: 40-54]).

Field strength/sequence: 3 T/4D Flow MRI.

Assessment: Laboratory (liver and kidney function test results and platelet count) and clinical data (presence of tumors and other imaging findings), and portal hemodynamics derived from 4D Flow MRI (spatiotemporally averaged RRT [RRT-mean], flow velocity, and flow rate) were analyzed.

Statistical tests: We used multivariable logistic regression, adjusted by selected covariates through the Lasso method, to explore whether RRT-mean is an independent risk factor for PVT. The area under the ROC curve (AUC) was also calculated to assess the model's discriminative ability. P < 0.05 indicated statistical significance.

Results: The liver cirrhosis group consisted of 16 participants with PVT and 32 without PVT. Higher RRT-mean values (odds ratio [OR] 11.4 [95% CI: 2.19, 118]) and lower platelet count (OR 0.98 per 1000 μL [95% CI: 0.96, 0.99]) were independent risk factors for PVT. The incorporation of RRT-mean (AUC, 0.77) alongside platelet count (AUC, 0.75) resulted in an AUC of 0.84. When including healthy control participants, RRT-mean had an adjusted OR of 12.4 and the AUC of the combined model (RRT-mean and platelet count) was 0.90.

Data conclusion: Prolonged RRT values and low platelet count were significantly associated with the presence of PVT in cirrhotic participants. RRT values derived from 4D Flow MRI may have potential clinical relevance in the management of PVT.

Evidence level: 2 TECHNICAL EFFICACY: Stage 2.

Background: Neonates with immature auditory function (eg, weak/absent middle ear muscle reflex) could conceivably be vulnerable to noise-induced hearing loss; however, it is unclear if neonates show evidence of hearing loss following MRI acoustic noise exposure.

Purpose: To explore the auditory effects of MRI acoustic noise in neonates.

Study type: Prospective.

Subjects: Two independent cohorts of neonates (N = 19 and N = 18; mean gestational-age, 38.75 ± 2.18 and 39.01 ± 1.83 weeks).

Field strength/sequence: T1-weighted three-dimensional gradient-echo sequence, T2-weighted fast spin-echo sequence, single-shot echo-planar imaging-based diffusion-tensor imaging, single-shot echo-planar imaging-based diffusion-kurtosis imaging and T2-weighted fluid-attenuated inversion recovery sequence at 3.0 T.

Assessment: All neonates wore ear protection during scan protocols lasted ~40 minutes. Equivalent sound pressure levels (SPLs) were measured for both cohorts. In cohort1, left- and right-ear auditory brainstem response (ABR) was measured before (baseline) and after (follow-up) MRI, included assessment of ABR threshold, wave I, III and V latencies and interpeak interval to determine the functional status of auditory nerve and brainstem. In cohort2, baseline and follow-up left- and right-ear distortion product otoacoustic emission (DPOAE) amplitudes were assessed at 1.2 to 7.0 kHz to determine cochlear function.

Statistical test: Wilcoxon signed-rank or paired t-tests with Bonferroni's correction were used to compare the differences between baseline and follow-up ABR and DPOAE measures.

Results: Equivalent SPLs ranged from 103.5 to 113.6 dBA. No significant differences between baseline and follow-up were detected in left- or right-ear ABR measures (P > 0.999, Bonferroni corrected) in cohort1, or in DPOAE levels at 1.2 to 7.0 kHz in cohort2 (all P > 0.999 Bonferroni corrected except for left-ear levels at 3.5 and 7.0 kHz with corrected P = 0.138 and P = 0.533).

Data conclusion: A single 40-minute 3-T MRI with equivalent SPLs of 103.5-113.6 dBA did not result in significant transient disruption of auditory function, as measured by ABR and DPOAE, in neonates with adequate hearing protection.

Evidence level: 2.

Technical efficacy: Stage 5.

Background: MR spectroscopy (MRS) is a noninvasive tool for evaluating biochemical alterations, such as glutamate (Glu)/gamma-aminobutyric acid (GABA) imbalance and depletion of antioxidative glutathione (GSH) after traumatic brain injury (TBI). Thalamus, a critical and vulnerable region post-TBI, is challenging for MRS acquisitions, necessitating optimization to simultaneously measure GABA/Glu and GSH.

Purpose: To assess the feasibility and optimize acquisition and processing approaches for simultaneously measuring GABA, Glx (Glu + glutamine (Gln)), and GSH in the thalamus, employing Hadamard encoding and reconstruction of MEscher-GArwood (MEGA)-edited spectroscopy (HERMES).

Study type: Prospective.

Subjects: 28 control subjects (age: 35.9 ± 15.1 years), and 17 mild TBI (mTBI) patients (age: 32.4 ± 11.3 years).

Field strength/sequence: 3T/T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE), HERMES.

Assessment: We evaluated the impact of acquisition with spatial saturation bands and post-processing with spectral alignment on HERMES performance in the thalamus among controls. Within-subject variability was examined in five controls through repeated scans within a week. The HERMES spectra in the posterior cingulate cortex (PCC) of controls were used as a reference for assessing HERMES performance in a reliable target. Furthermore, we compared metabolite levels and fitting quality in the thalamus between mTBI patients and controls.

Statistical tests: Unpaired t-tests and within-subject coefficient-of-variation (CV). A P-value <0.05 was deemed significant.

Results: HERMES spectra, acquired with saturation bands and processed with spectral alignment, yielded reliable metabolite measurements in the thalamus. The mean within-subject CV for GABA, Glx, and GSH levels were 18%, 10%, and 16% in the thalamus (7%, 9%, and 16% in the PCC). GABA (3.20 ± 0.60 vs 2.51 ± 0.55, P < 0.01) and Glx (8.69 ± 1.23 vs 7.72 ± 1.19, P = 0.03) levels in the thalamus were significantly higher in mTBI patients than in controls, with GSH (1.27 ± 0.35 vs 1.22 ± 0.28, P = 0.65) levels showing no significant difference.

Data conclusion: Simultaneous measuring GABA/Glx and GSH using HERMES is feasible in the thalamus, providing valuable insight into TBI.

Level of evidence: 2 TECHNICAL EFFICACY: Stage 2.

Background: Early and accurate identification of lymphatic node metastasis (LNM) and lymphatic vascular space invasion (LVSI) for endometrial cancer (EC) patients is important for treatment design, but difficult on multi-parametric MRI (mpMRI) images.

Purpose: To develop a deep learning (DL) model to simultaneously identify of LNM and LVSI of EC from mpMRI images.

Study type: Retrospective.

Population: Six hundred twenty-one patients with histologically proven EC from two institutions, including 111 LNM-positive and 168 LVSI-positive, divided into training, internal, and external test cohorts of 398, 169, and 54 patients, respectively.

Field strength/sequence: T2-weighted imaging (T2WI), contrast-enhanced T1WI (CE-T1WI), and diffusion-weighted imaging (DWI) were scanned with turbo spin-echo, gradient-echo, and two-dimensional echo-planar sequences, using either a 1.5 T or 3 T system.

Assessment: EC lesions were manually delineated on T2WI by two radiologists and used to train an nnU-Net model for automatic segmentation. A multi-task DL model was developed to simultaneously identify LNM and LVSI positive status using the segmented EC lesion regions and T2WI, CE-T1WI, and DWI images as inputs. The performance of the model for LNM-positive diagnosis was compared with those of three radiologists in the external test cohort.

Statistical tests: Dice similarity coefficient (DSC) was used to evaluate segmentation results. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of LNM and LVSI status identification. P value <0.05 was considered significant.

Results: EC lesion segmentation model achieved mean DSC values of 0.700 ± 0.25 and 0.693 ± 0.21 in the internal and external test cohorts, respectively. For LNM positive/LVSI positive identification, the proposed model achieved AUC values of 0.895/0.848, 0.806/0.795, and 0.804/0.728 in the training, internal, and external test cohorts, respectively, and better than those of three radiologists (AUC = 0.770/0.648/0.674).

Data conclusion: The proposed model has potential to help clinicians to identify LNM and LVSI status of EC patients and improve treatment planning.

Evidence level: 3 TECHNICAL EFFICACY: Stage 2.

Background: Pretreatment identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is important when selecting treatment strategies.

Purpose: To improve models for predicting MVI and recurrence-free survival (RFS) by developing nomograms containing three-dimensional (3D) MR elastography (MRE).

Study type: Prospective.

Population: 188 patients with HCC, divided into a training cohort (n = 150) and a validation cohort (n = 38). In the training cohort, 106/150 patients completed a 2-year follow-up.

Field strength/sequence: 1.5T 3D multifrequency MRE with a single-shot spin-echo echo planar imaging sequence, and 3.0T multiparametric MRI (mp-MRI), consisting of diffusion-weighted echo planar imaging, T2-weighted fast spin echo, in-phase out-of-phase T1-weighted fast spoiled gradient-recalled dual-echo and dynamic contrast-enhanced gradient echo sequences.

Assessment: Multivariable analysis was used to identify the independent predictors for MVI and RFS. Nomograms were constructed for visualization. Models for predicting MVI and RFS were built using mp-MRI parameters and a combination of mp-MRI and 3D MRE predictors.

Statistical tests: Student's t-test, Mann-Whitney U test, chi-squared or Fisher's exact tests, multivariable analysis, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log rank tests. P < 0.05 was considered significant.

Results: Tumor c and liver c were independent predictors of MVI and RFS, respectively. Adding tumor c significantly improved the diagnostic performance of mp-MRI (AUC increased from 0.70 to 0.87) for MVI detection. Of the 106 patients in the training cohort who completed the 2-year follow up, 34 experienced recurrence. RFS was shorter for patients with MVI-positive histology than MVI-negative histology (27.1 months vs. >40 months). The MVI predicted by the 3D MRE model yielded similar results (26.9 months vs. >40 months). The MVI and RFS nomograms of the histologic-MVI and model-predicted MVI-positive showed good predictive performance.

Data conclusion: Biomechanical properties of 3D MRE were biomarkers for MVI and RFS. MVI and RFS nomograms were established.

Level of evidence: 2 TECHNICAL EFFICACY: Stage 2.