Journal of Magnetic Resonance Imaging最新文献

Background: Artificial intelligence (AI) assistance may enhance radiologists' performance in detecting clinically significant prostate cancer (csPCa) on MRI. Further validation is needed for radiologists with different experiences.

Purpose: To assess the performance of experienced and less-experienced radiologists in detecting csPCa, with and without AI assistance.

Study type: Retrospective.

Population: Nine hundred patients who underwent prostate MRI and biopsy (median age 67 years; 356 with csPCa and 544 with non-csPCa).

Field strength/sequence: 3-T and 1.5-T, diffusion-weighted imaging using a single-shot gradient echo-planar sequence, turbo spin echo T2-weighted image.

Assessment: CsPCa regions based on biopsy results served as the reference standard. Ten less-experienced (<500 prostate MRIs) and six experienced (>1000 prostate MRIs) radiologists reviewed each case twice using Prostate Imaging Reporting and Data System v2.1, with and without AI, separated by 4-week intervals. Cases were equally distributed among less-experienced radiologists, and 90 cases were randomly assigned to each experienced radiologist. Reading time and diagnostic confidence were assessed.

Statistical tests: Area under the curve (AUC), sensitivity, specificity, reading time, and diagnostic confidence were compared using the DeLong test, Chi-squared test, Fisher exact test, or Wilcoxon rank-sum test between the two sessions. A P-value <0.05 was considered significant. Adjusting threshold using Bonferroni correction was performed for multiple comparisons.

Results: For less-experienced radiologists, AI assistance significantly improved lesion-level sensitivity (0.78 vs. 0.88), sextant-level AUC (0.84 vs. 0.93), and patient-level AUC (0.84 vs. 0.89). For experienced radiologists, AI assistance only improved sextant-level AUC (0.82 vs. 0.91). AI assistance significantly reduced median reading time (250 s [interquartile range, IQR: 157, 402] vs. 130 s [IQR: 88, 209]) and increased diagnostic confidence (5 [IQR: 4, 5] vs. 5 [IQR: 4, 5]) irrespective of experience and enhanced consistency among experienced radiologists (Fleiss κ: 0.53 vs. 0.61).

Data conclusion: AI-assisted reading improves the performance of detecting csPCa on MRI, particularly for less-experienced radiologists.

Evidence level: 3 TECHNICAL EFFICACY: Stage 2.

THE CLINICAL APPLICATION OF VARIOUS IMAGING METHODS IN HS DETECTION. A 39-YEAR-OLD FEMALE WITH EPILEPSY SEIZURE FOR MORE THAN 10 YEARS. V-EEG SHOWED MANY EPILEPTIC WAVES IN THE RIGHT PREFRONTAL AND ANTERIOR MIDDLE TEMPORAL REGIONS. (A) MRI T1WI SHOWED MARKED ATROPHY OF THE RIGHT HIPPOCAMPUS. (B, C) MRI T2WI AND FLAIR SHOWED THE RIGHT HIPPOCAMPUS HYPERINTENSITY. (D) INTERICTAL PET SCAN SHOWED HYPOMETABOLIC AREAS IN THE RIGHT TEMPORAL HIPPOCAMPUS. (E) PET/MRI SHOWED THE LESION IN THE RIGHT HIPPOCAMPUS. (F) UAI BY SHANGHAI UNITED IMAGING INTELLIGENT MEDICAL TECHNOLOGY CO. ALSO SHOWED MARKED ATROPHY OF THE RIGHT HIPPOCAMPUS. THE PATIENT UNDERWENT RIGHT ATL. THE PATHOLOGY CONFIRMED HS. BY YAO ET AL. (2309-2331)

Background: The risk of biochemical recurrence (BCR) in prostate cancer (PCa) is typically assessed using D'Amico score. However, iron and fat content in PCa are closely related to tumor cell proliferation and the risk of BCR may be estimated using multiparametric MRI (mpMRI).

Purpose: To noninvasively estimate fat and iron content in PCa and to evaluate their utility in enhancing D'Amico scores for predicting BCR in PCa patients.

Study type: Prospective.

Subjects: Forty-eight male patients in the BCR group (age 71.31 ± 5.74 years) and 27 male patients in the non-BCR group (age 70.3 ± 6.04 years).

Field strength/sequence: 3.0 T, Turbo-spin echo T2-weighted imaging, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) imaging, Gradient echo Q-Dixon sequence.

Assessment: The mean fat fraction (FF) and T2* values of lesions were extracted from the FF map and the T2* map. Additionally, prostate volume, mean apparent diffusion coefficient (ADC) value, periprostatic fat thickness (PPFT), subcutaneous fat thickness (SFT), blood lipid content, pre- and post-operative prostate-specific antigen (PSA) values were collected.

Statistical tests: Stepwise-COX regression analysis was employed to identify the significant predictors of BCR, which led to the construction of an improvement-adjusted (IA) model. Then the IA model as well as the D'Amico score were evaluated using C-index and time-dependent AUC, decision-curve analysis, and Kaplan-Meier curve. P < 0.05 was statistically significant.

Results: Significant differences were observed in PSA, D'Amico score, ISUP grade, T2*, FF, and ADC values of the lesions in the BCR group compared with the non-BCR group. Mean T2*, FF, and ADC values of the lesions were screened to construct the IA model incorporated into the D'Amico score (IA Model: C-index = 0.749; AUC = 0.812; D'Amico score: C-index = 0.672; AUC = 0.723).

Data conclusion: This study demonstrated that mpMRI can quantitatively estimate fat and iron within PCa lesions. By integrating ADC, FF, and T2* values into the D'Amico score, the preoperative-risk assessment for BCR can be improved.

Evidence level: 2 TECHNICAL EFFICACY: Stage 2.

Background: Multiparametric MRI may cause overdiagnosis of clinically significant prostate cancer (csPCa) with the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1).

Objectives: To investigate the diagnostic performance of stiffness as a standalone and complementary marker to PI-RADS v2.1 for diagnosing csPCa.

Study type: Prospective.

Subjects: One hundred forty-seven participants with pathologically confirmed prostate lesions (≥1 cm), including 71 with csPCa.

Field strength/sequence: T1-weighted fast spin-echo, T2-weighted fast spin-echo, single-shot echo-planar diffusion-weighted imaging, fast 3D gradient-echo T1-weighted dynamic contrast-enhanced imaging, and 3D single-shot spin-echo based echo-planar MR elastography at 3.0 T.

Assessment: The PI-RADS v2.1 score was assessed by three radiologists independently. Lesion shear stiffness (SS) values at 60 Hz and 90 Hz were measured. A modified PI-RADS integrating stiffness with PI-RADS v2.1 was developed. Diagnostic performance for csPCa was compared between stiffness, PI-RADS v2.1 and the modified PI-RADS.

Statistical test: Spearman's correlation, Fleiss κ and intraclass correlation, Pearson correlation, one-way analysis of variance, area under the receiver operating characteristic curve (AUC), and the Delong test. Significance level was P < 0.05.

Results: In the peripheral zone, csPCa (N = 35) had significantly higher SS than non-csPCa at 60 Hz (3.22 ± 0.66 kPa vs. 2.56 ± 0.56 kPa) and at 90 Hz (5.64 ± 1.30 kPa vs. 4.48 ± 0.84 kPa). PI-RADS v2.1 showed 100% sensitivity, 58% specificity, and 0.79 AUC for detecting csPCa. SS achieved 97% sensitivity, 52% specificity, and 0.80 AUC at 60 Hz, while SS had 63% sensitivity, 87% specificity, and 0.78 AUC at 90 Hz. The modified PI-RADS, combing SS at 60 Hz with PI-RADS v2.1, resulted in a significantly increased AUC (0.86) compared to that of PI-RADS v2.1, with a sensitivity of 97% and specificity of 75%.

Data conclusion: Stiffness can help identifying csPCa in the peripheral zone. Combining stiffness with the PI-RADS v2.1 improved the diagnostic accuracy and specificity for csPCa.

Evidence level: 1 TECHNICAL EFFICACY: Stage 2.

Background: Diffusion tensor imaging (DTI) is susceptible to partial volume effects from free water, which can be corrected by using bi-tensor free water imaging (FWI). This approach may improve the evaluation of microstructural changes associated with Wilson's disease (WD).

Purpose: To investigate microstructural changes in white matter of WD using DTI and FWI.

Study type: Prospective.

Subjects: Nineteen neurological WD (7 female, 31.68 ± 7.89 years), 10 hepatic WD (3 female, 29.67 ± 13.37 years), and 25 healthy controls (13 female, 29.5 ± 7.7 years).

Field strength/sequence: 3-T, spin-echo echo-planar imaging diffusion-weighted imaging, T1-weighted, T2-weighted, fluid-attenuated inversion recovery.

Assessment: Various diffusion metrics, including mean diffusivity (MD), radial diffusivity (RD), fractional anisotropy (FA), axial diffusivity (AD), free water, and free water-corrected metrics (MD_T, RD_T, FA_T, and AD_T) were estimated and compared across entire white matter skeleton among neurological WD, hepatic WD, and controls. Voxel-wise tract-based spatial statistics and region of interest (ROI) analysis based on white matter atlas were performed. Additionally, partial correlation analysis was conducted to assess the relationship between FWI indices in ROIs and clinical indicators.

Statistical tests: One-way analysis of variance, family-wise error correction for multiple comparisons, and Bonferroni correction for post hoc comparisons. A P-value <0.05, corrected for multiple comparisons, was considered statistically significant.

Results: Our study found significantly lower FA and higher MD, AD, and RD across most of white matter skeleton in neurological WD. Decreased FA_T and increased MD_T, AD_T, and RD_T were observed only in limited white matter areas compared to DTI indices. Additionally, a significant relationship was found between Unified WD Rating Scale neurological subscale of neurological WD and free water (r = 0.613) in middle cerebellar peduncle, AD_T (r = -0.555) in superior cerebellar peduncle, RD_T (r = 0.655), and FA_T (r = -0.660) in posterior limb in internal capsule.

Data conclusion: FWI may allow a more precise evaluation of microstructural changes in WD than conventional DTI, with FWI metrics potentially correlating with clinical severity scores of WD patients.

Level of evidence: 2 TECHNICAL EFFICACY: Stage 2.

Extremely severe nausea was experienced by four subjects positioned prone on a 7T scanner table with their arm extended overhead for a wrist examination and their head positioned approximately 10-20 cm above the magnet's central axis. Movement through the large static and spatial field gradients of current 7T MRI scanner magnets typically causes mild vestibular activation which is well tolerated by most individuals. However, when positioned off-axis, the head moves through regions of even larger and more rapidly changing magnetic fields which in the current study were sufficient to induce the extremely severe nausea. Ensuring the head remains on-axis mitigates this effect.

Background: Integrating vessels encapsulating tumor clusters (VETC) and microvascular invasion (MVI) (VM hereafter) is potentially useful in risk stratification of hepatocellular carcinoma (HCC). However, noninvasive assessment methods for VM are lacking.

Purpose: To investigate the diagnostic performance of tomoelastography in assessing the VM status in HCC.

Study type: Retrospective.

Population: One hundred sixty-eight patients with surgically confirmed HCC consisting of 115 training and 53 validation cohorts, divided into negative-VM and positive-VM groups with mild or severe-VMs. Of them, 127 patients completed the follow-up (median: 26.1 months).

Field strength/sequence: 3D multifrequency tomoelastography with a single-shot spin-echo echo-planar imaging sequence, and liver MRI including T1-weighted in-phase and opposed-phase gradient echo (GRE), T2-weighted turbo spin echo, diffusion-weighted imaging and dynamic contrast-enhanced T1-weighted GRE sequences at 3.0 T.

Assessment: Shear wave speed (c) and phase angle of the shear modulus (φ) were calculated on tomoelastograms. Imaging features were visually analyzed and clinical features were collected. Conventional models used clinical and imaging features while nomograms combined tomoelastography, clinical and imaging features.

Statistical tests: Univariable and multivariable logistic regression analyses, nomogram, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log-rank test. P < 0.05 was considered statistically significant.

Results: Tumor-to-liver parenchyma ratio of c (cr) and tumor c were independent risk factors for positive-VM and severe-VM, respectively. In validation cohort, the nomograms including cr and tumor c performed significantly better than the conventional models for diagnosing positive-VM (0.84 [95% CI: 0.72-0.93] vs. 0.77 [95% CI: 0.64-0.88]) and severe-VM (0.86 [95% CI: 0.68-0.96] vs. 0.75 [95% CI: 0.55-0.89]). Patients with estimated positive-VM (9.3 months)/severe-VM (9.2 months) based on nomograms had shorter median recurrence-free survival than those with estimated negative-VM (>20.0 months)/mild-VM (18.0 months) in validation cohort.

Data conclusion: Tomoelastography based-nomograms showed good performance for noninvasively assessing VM status in patients with HCC.

Evidence level: 3 TECHNICAL EFFICACY: Stage 2.