Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine最新文献

Purpose: To re-evaluate images recovered from JCOG0911, a randomized phase 2 trial for newly diagnosed glioblastoma (nGBM) conducted by the Japan Clinical Oncology Group (JCOG) Brain Tumor Study Group.

Methods: The correlation between tumor volumes and survival was evaluated, followed by progression-free survival (PFS) analysis by independent central review based on Response Assessment in Neuro-Oncology (RANO) criteria using MRI recovered from 118 nGBM patients enrolled in the JCOG0911 trial. A radiomic analysis was also performed to identify radiomic features predictive of nGBM prognosis.

Results: The distribution of the Gd-enhancing and T2-weighted image/fluid attenuated inversion recovery-high intensity lesions mainly occupied white matter. JCOG0911 consisted of more subjects with right-sided lesions. The median extent of resection of the Gd-enhancing lesions was 99%. The overall survival showed a nonsignificant negative trend with postoperative Gd-enhancing lesion volume (P = 0.22), with the hazard ratio increasing in parallel with its volume. The median PFS after registration was 302 and 308 days for local Response Evaluation Criteria in Solid Tumors (RECIST)-based and central RANO-based diagnoses. However, an apparent discrepancy was observed between the two in the early phase, presumably due to the misdiagnosis of pseudoprogression by local RECIST-based diagnosis. Radiomic analysis identified 28 radiomic features predictive of nGBM prognosis, 5 of which were those previously identified in a separate cohort. The constructed radiomics-based prognostic model stratified the cohort into high- and low-risk groups (P = 0.028).

Conclusion: Novel analytical methods that could be incorporated into future clinical trials were successfully tested. RANO and RECIST may not differ in progression calls if pseudoprogression is appropriately handled.

Purpose: The purposes of this study were 1) to improve vessel visibility of our MR sequence by modifying k-space filling and 2) to verify the usefulness of applying artificial intelligence (AI) for volume isotropic simultaneous interleaved bright- and black-blood examination (VISIBLE) with compressed sensitivity encoding (CS) in autodetecting brain metastases.

Methods: We modified 3 sequences of VISIBLE (Centric, Reversed Centric, and Startup Echo 30). The Centric sequence is a prototype. The Reversed Centric filled the k-space in a reversed centric manner to improve vessel visibility. The Startup Echo 30 implemented dummy echoes to further improve vessel visibility. Vessel visibility was evaluated in one slice at the level of the centrum semiovale. The sensitivity, specificity, the area under the curve (AUC), and false positives of detecting brain metastases using AI were evaluated among 3 sequences. Statistical comparisons were performed using a one-way analysis of variance, followed by Friedman and Dunn's multiple comparison tests.

Results: The number of visualized vessels was significantly lower in the Centric (39.3 ± 9.7, P < 0.05) and Reversed Centric (44.2 ± 9.8, P < 0.05) methods than in the magnetization-prepared rapid gradient echo (49.3 ± 9.1) but comparable in the Startup Echo 30 method (44.9 ± 8.8, P > 0.05). No significant differences existed in sensitivity, specificity, and AUC among the 3 methods. False positives achieved using the Reversed Centric method were significantly fewer (54 false positives) than those achieved using the Centric (85 false positives) and Startup Echo 30 (68 false positives) methods (P = 0.0092).

Conclusion: Vessel visibility was improved by modifying the k-space filling, which may reduce false positives. The AI model for VISIBLE with CS achieved good performance in autodetection of brain metastases. The AI model for VISIBLE with CS can help radiologists diagnose brain metastases in clinical practice.

Purpose: This study investigated the ability of three-dimentional motion-sensitized driven-equilibrium prepared T₁-weighted fast spin echo (3D MSDE-FSE) imaging to identify distal dural rings (DDRs) and paraclinoid aneurysms (ParaC-ANs) and differentiate between intradural and extradural ParaC-ANs and compared it with that of established MR cisternography-based techniques.

Methods: 3D MSDE-FSE images were acquired along with fast imaging employing steady state acquisition (FIESTA), and time-of-flight magnetic resonance angiography (TOF-MRA) on a 3T MRI system in 53 patients with unruptured and untreated ParaC-ANs. Two radiologists applied a 3-point scale to rate the clarity with which the DDR (53 left and 53 right) and ParaC-ANs (total of 55) were depicted in the 3D MSDE-FSE and FIESTA images. The clarity scores, which were determined by averaging the scores of the 2 assessors, on the 3D MSDE-FSE and FIESTA images were compared using the Wilcoxon signed-rank test. Furthermore, the same radiologists classified the ParaC-ANs as intradural, extradural, or transitional on 3D MSDE-FSE images. A third radiologist independently classified the ParaC-ANs as intradural, extradural, or transitional based on the FIESTA and MRA fusion images. The kappa coefficient was used to compare this classification with that based on 3D MSDE-FSE images.

Results: The Wilcoxon signed-rank test revealed no significant difference between 3D MSDE-FSE images and FIESTA images in the scores for the clarity of depiction of the DDRs (P = 0.119). However, the scores for the clarity of the depiction of the ParaC-ANs were significantly greater for the 3D MSDE-FSE images than for the FIESTA images (P < 0.001). The kappa coefficient for comparison of classification based on 3D MSDE-FSE images and FIESTA and MRA fusion images was 0.82.

Conclusion: 3D MSDE-FSE imaging has the potential to differentiate between intradural and extradural ParaC-ANs by directly recognizing the DDR.

The integration of deep learning (DL) in breast MRI has revolutionized the field of medical imaging, notably enhancing diagnostic accuracy and efficiency. This review discusses the substantial influence of DL technologies across various facets of breast MRI, including image reconstruction, classification, object detection, segmentation, and prediction of clinical outcomes such as response to neoadjuvant chemotherapy and recurrence of breast cancer. Utilizing sophisticated models such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, DL has improved image quality and precision, enabling more accurate differentiation between benign and malignant lesions and providing deeper insights into disease behavior and treatment responses. DL's predictive capabilities for patient-specific outcomes also suggest potential for more personalized treatment strategies. The advancements in DL are pioneering a new era in breast cancer diagnostics, promising more personalized and effective healthcare solutions. Nonetheless, the integration of this technology into clinical practice faces challenges, necessitating further research, validation, and development of legal and ethical frameworks to fully leverage its potential.

This review explores the significant progress and applications of artificial intelligence (AI) in obstetrics and gynecological MRI, charting its development from foundational algorithmic techniques to deep learning strategies and advanced radiomics. This review features research published over the last few years that has used AI with MRI to identify specific conditions such as uterine leiomyosarcoma, endometrial cancer, cervical cancer, ovarian tumors, and placenta accreta. In addition, it covers studies on the application of AI for segmentation and quality improvement in obstetrics and gynecology MRI. The review also outlines the existing challenges and envisions future directions for AI research in this domain. The growing accessibility of extensive datasets across various institutions and the application of multiparametric MRI are significantly enhancing the accuracy and adaptability of AI. This progress has the potential to enable more accurate and efficient diagnosis, offering opportunities for personalized medicine in the field of obstetrics and gynecology.

Purpose: To evaluate the clinical value of early renal changes in type 2 diabetes mellitus (T2DM) using multiparameter MRI.

Methods: The study included 41 diabetics (normoalbuminuria: n = 23; microalbuminuria: n = 18) and 30 healthy controls. All subjects underwent intravoxel incoherent motion diffusion-weighted imaging (IVIM), blood oxygen level dependent (BOLD) and arterial spin labeling (ASL) examinations. One-way analysis of variance was used to compare MRI parameters among the three groups. Pearson correlation analysis was used to evaluate the relationship between MRI parameters and estimated glomerular filtration rate (eGFR) and albumin-creatinine ratio (ACR). Receiver operating characteristic analysis was performed to assess the diagnostic performance.

Results: There were statistical differences in cortical D, D*, f, renal blood flow (RBF) and medulla D, D*, f, R2* among the three groups (P < 0.05). The cortical or medullary D, cortical f, and RBF were significantly positively correlated with eGFR (all P < 0.01). The cortical or medullary D, D*, f, cortical RBF were negatively correlated with ACR (all P < 0.05).To evaluate early kidney changes and degree of diabetes, cortical combined D and RBF (AUC [area under the curve]  = 0.796 and 0.947, respectively) was better than single D or RBF (all P > 0.05); medullary combined D and R2* (AUC = 0.899 and 0.923, respectively) was better than single D or R2* (all P > 0.05), except single D (P = 0.005) in differentiating normoalbuminuria group from control group.

Conclusion: The early changes of renal diffusion and perfusion, oxygenation level, and blood flow in T2DM could be evaluated noninvasively and quantitatively using IVIM, BOLD and ASL. Renal medullary combined IVIM-derived D and BOLD-derived R2* and cortical combined IVIM-derived D and ASL-derived RBF were better for evaluating early renal changes in T2DM.

Purpose: T₂ values are hypothesized to be reduced where protein accumulates in the cerebrospinal fluid (CSF). We aimed to verify the accuracy of Carr-Purcell-Meiboom-Gil (CPMG) pulses and non-negative least squares (NNLS) analysis in visualizing protein concentrations by mapping the T₂ values.

Methods: We first dissolved 1.2g of bovine serum albumin powder in 4 mL of artificial CSF to purify an albumin solution with a concentration of 4.5 mM. Artificial CSF was added thereto, and eight types of albumin solutions, with concentrations of 0.002-4.5 mM, were purified. We acquired this albumin solution with CPMG pulses and NNLS, decomposed the T₂ values per pixel, and derived 25 T₂ component values of 60-2000 ms. We assessed the change of T₂ values by the difference in albumin concentration of a single voxel. Finally, we used the method to assess T₂ values from two patients, one with a subdural hematoma and one with a suprasellar cystic tumor. T₂ component values were plotted graphically, presented individually, and created in color maps.

Results: T₂ component values for albumin concentrations ranging from 0.056 to 4.55 mM showed different T₂ peaks, whereas, for concentrations 0.002 to 0.019 mM, the peaks were similar heights and overlapped. Peak width was similar for all concentrations. The color maps successfully reflected the changes in T₂ values across both RGB color patterns. T₂ components for albumin samples with 2.5 mM and 6.1 mM concentrations within a single voxel were represented separately and reflected the ratio of the two samples in nine different regions of interest within one slice. In the clinical cases, the T₂ component map imaged differences in albumin concentrations, similar to those observed in the albumin samples.

Conclusion: The present method with CPMG sequences and NNLS provide adequate images to differentiate accumulating protein concentrations in the CSF, even at the level of a single pixel.

Purpose: This study investigated the breast lesion conspicuity and apparent diffusion coefficient (ADC) reliability for three different diffusion-weighted imaging (DWI) protocols: spatiotemporal encoding (SPEN), single-shot echo-planar imaging (SS-EPI), and readout segmentation of long variable echo-trains (RESOLVE).

Methods: Sixty-five women suspected of having breast tumors were included in this study, with 44 lesions (36 malignant, 8 benign) analyzed further. Breast MRI was performed on a 3 Tesla (3T) system (MAGNETOM Prisma, Siemens) equipped with a dedicated 18-channel breast array coil for a phantom and patients. Three DWI protocols-SPEN, SS-EPI, and RESOLVE-were used. SS-EPI was acquired with an in-plane resolution of 2 × 2 mm², a slice thickness of 3 mm, and b-values of 0 and 1000 s/mm². SPEN had a higher in-plane resolution of 1 × 1 mm², a slice thickness of 1.5 mm, and b-values of 0, 850, and 1500 s/mm². RESOLVE was acquired with an in-plane resolution of 1 × 1 mm², a slice thickness of 1.5 mm, and b-values of 0 and 850 s/mm². Lesion conspicuity and ADC values were evaluated.

Results: The average lesion conspicuity scores were significantly higher for RESOLVE (3.54 ± 0.65) than for SPEN (3.07 ± 0.91) or SS-EPI (2.48 ± 0.78) (P < 0.01). The SPEN score was significantly higher than the SS-EPI score (P < 0.01). Phantom measurements indicated marginally lower ADC values for SPEN compared to SS-EPI and RESOLVE across all concentrations. The results revealed that SPEN (b = 0, 850, 1500 sec/mm²) yielded significantly lower ADC values compared to SPEN (b = 0, 850 sec/mm²) in malignant lesions (P < 0.01), with no significant difference observed between SPEN (b = 0, 850 sec/mm²), SS-EPI, and RESOLVE. For benign lesions, no significant difference in ADC values was found between SPEN (b = 0, 850 sec/mm²), SPEN (b = 0, 850, 1500 sec/mm²), SS-EPI, and RESOLVE.

Conclusion: RESOLVE provided the highest lesion conspicuity, and ADC values in breast lesions were not significantly different among sequences ranging b values 850-1000 sec/mm². SPEN with higher b-values (0, 850, 1500 vs. 0, 850 sec/mm²) yielded significantly lower ADC values in malignant lesions, highlighting the importance of b-value selection in ADC quantification.

Purpose: To assess the utility of apparent diffusion coefficient maps (ADC) for diagnosing myometrial invasion (MI) in endometrial cancer (EC).

Methods: This retrospective study included 164 patients (mean age, 56 years; range, 25-89 years) who underwent preoperative MRI for EC with <1/2 MI or no MI between April 2016 and July 2023. Five sequences were evaluated: T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), ADC, dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI), and contrast-enhanced T1WI (CE-T1WI). Three experienced radiologists independently assessed the sequences for MI. For ADC, MI was determined if the endometrial-myometrial junction-tumor boundary had disappeared. Additionally, the assessment of MI was performed using the combination of T2WI, DWI, and ADC, as well as T2WI, DCE-T1WI, and CE-T1WI. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for the presence of MI were calculated and compared between the sequences and combinations. Inter-reader agreement was assessed using kappa (κ) statistics.

Results: The sensitivity of ADC was significantly higher than T2WI (P < 0.001) and DCE-T1WI (P = 0.018) for one reader and significantly higher than CE-T1WI (P = 0.045 and 0.043) for two readers. The specificity of ADC was significantly lower than T2WI (P = 0.015 and < 0.001) and CE-T1WI (P = 0.031 and 0.01) for two readers and significantly lower than DCE-T1WI (P = 0.031) for one reader. The AUC of ADC was significantly higher than T2WI (P = 0.048) and DCE-T1WI (P = 0.049) for one reader. The combination including ADC showed higher positive predictive value for all three readers compared to any sequence or combination including contrast enhancement. Additionally, ADC demonstrated the highest agreement rates.

Conclusion: ADC had high sensitivity for MI and the highest agreement rate among all sequences. Thus, this sequence, combined with other sequences, can be crucial for a comprehensive evaluation of MI.