Magnetic resonance imaging最新文献

{"title":"Association of preoperative MRI with breast cancer treatment and survival: A single institution observational study","authors":"B. Bersu Ozcan , Ann R. Mootz , Dogan S. Polat , Yin Xi , Asal Rahimi , Başak E. Dogan","doi":"10.1016/j.mri.2025.110343","DOIUrl":"10.1016/j.mri.2025.110343","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the association between preoperative breast MRI with surgery type, contralateral cancer, recurrence-free (RFS) and overall survival (OS) in women with early-stage breast cancer.</div></div><div><h3>Materials and methods</h3><div>In this dual-affiliated single institution, retrospective study, we identified women with Stage I-III breast cancer diagnosed between 03/01/2013–03/31/2016 with available follow-up. Patient and tumor characteristics were recorded. Two cohorts were created based on the use of preoperative MRI(PMRI) versus no preoperative MRI(no-MRI) with Wilcoxon signed-rank and χ2 tests utilized for cross-group comparisons. Kaplan-Meier, log-rank and cox proportional hazards model analysis were used to compare RFS and OS in women with and without MRI.</div></div><div><h3>Results</h3><div>593 eligible patients were included [322(54.3 %) with PMRI, 271(45.7 %) no-MRI]. Mean patient age was younger (53.8 ± 11.8vs59.3 ± 12.6 years, <em>p</em> < 0.001) and dense breasts more common (51.6 %vs22.5 %, p < 0.001) in PMRI group. Seventeen bilateral cancers (5.3 %) were in PMRI [14/17(82.4 %) detected only on MRI] vs 10 (3.7 %) in no-MRI (<em>p</em> = 0.34). Molecular subtype distribution(luminal A:27.2 % vs 31.1 %; luminal B:51.8 %vs44.2 %; HER2:5.4 %vs4.2 %; triple negative:15.6 %vs20.5 %, <em>p</em> = 0.28) were similar in PMRI vs no-MRI groups. PMRI group had higher rates of cT2–4(45.0 %vs28.8 %, <em>p</em> < 0.001), cN+(27.3 % vs 18.1 %, <em>p</em> < 0.01), and neoadjuvant therapy (NAC, 41.3 % vs 18.8 %, p < 0.001). Total mastectomy(57.8 %vs51.3 %, <em>p</em> = 0.12), margin positivity(6.2 %vs7.4 %, <em>p</em> = 0.63), recurrence(10.2 %vs7.0 %, <em>p</em> = 0.20) and death rates(8.1 %vs7.7 %, <em>p</em> = 0.88) were similar in PMRI vs no-MRI. Mastectomy rates remained comparable after adjusting for age and breast density (<em>p</em> = 0.28). At median follow-up of 70 months(IQR, 64–70), time to recurrence was [PMRI:30(IQR, 19–47)vs no-MRI:23(IQR, 9–31) months, <em>p</em> = 0.04]. Contralateral cancers were identified sooner and more frequently in the no-MRI group [4(2.1 %)vs2(0.9 %) cancers, <em>p</em> = 0.32, 21 ± 20vs48 ± 13 months, <em>p</em> = 0.27]. There was no significant difference in 5-year RFS[hazard ratio(HR) 1.05, 95 %CI 0.67–1.67, <em>p</em> = 0.84] and OS[HR 0.94, 95 %CI: 0.51–1.74, <em>p</em> = 0.85] between PMRI and no-MRI groups even after adjusting for age, cancer type, breast density, cN stage, and NAC. which were different between two groups (RFS, HR 0.87, 95 %CI: 0.53–1.43, <em>p</em> = 0.57; OS, HR 0.78, 95 %CI: 0.40–1.52, <em>p</em> = 0.46). NHW patients had higher RFS compared to Black patients in PMRI group (HR 0.45, 95 % CI: 0.21–0.96, <em>p</em> = 0.04) in adjusted analysis.</div></div><div><h3>Conclusions</h3><div>Preoperative MRI utilization is not associated with improved surgical margin, 5-year RFS or OS in our cohort. This effect persisted after adjusting for p","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"118 ","pages":"Article 110343"},"PeriodicalIF":2.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced diffusion-weighted imaging-derived quantitative parameters as biomarkers of fibrosarcoma-cell proliferation in nude mice: A study based on precise imaging-pathology correlation","authors":"Jie Huang ,&nbsp;Zhiqing Duan ,&nbsp;Yu Cheng ,&nbsp;Juan Tao ,&nbsp;Siyu Dai ,&nbsp;Jianwen Zhou ,&nbsp;Shaowu Wang","doi":"10.1016/j.mri.2025.110345","DOIUrl":"10.1016/j.mri.2025.110345","url":null,"abstract":"<div><h3>Purpose</h3><div>To determine whether quantitative parameters derived using diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) imaging reflect pathological changes in fibrosarcoma.</div></div><div><h3>Methods</h3><div>Thirty nude mouse models of fibrosarcoma underwent T1/T2-weighted imaging, DKI, and IVIM imaging on a 3.0-T scanner. Immunohistochemistry was utilized for the hematoxylin and eosin, aquaporin 1 (AQP1), aquaporin 4 (AQP4), and Ki-67 staining of fibrosarcoma tissue, and AQP1 and AQP4 staining of normal muscle tissue (NMT). The independent-sample <em>t</em>-test was used to compare AQP1 and AQP4 expression in fibrosarcoma and NMT. Pearson and Spearman correlation analyses were conducted to evaluate the correlation between imaging parameters and pathological indicators. Multiple linear regression analysis was employed to identify the pathological indicators independently associated with quantitative DKI and IVIM parameters.</div></div><div><h3>Results</h3><div>Apparent diffusion coefficient (ADC), D, f, and mean kurtosis (MK) indicated cell density and Ki-67 and AQP1 expression intensity. D values reflected AQP4 expression intensity, while MD reflected cell density and AQP1 expression intensity. Cell density (CD) independently influenced ADC and f values, while CD and AQP1 independently influenced D values.</div></div><div><h3>Conclusion</h3><div>CD and Ki-67 independently influenced MK. DKI- and IVIM imaging-derived ADC, D, f, MD, and MK were correlated with AQP1, AQP4, Ki-67, and CD in nude mice with fibrosarcoma.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"118 ","pages":"Article 110345"},"PeriodicalIF":2.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of metachronous liver metastasis in mid-low rectal cancer using quantitative perirectal fat content from high-resolution MRI","authors":"Jiaming Qin ,&nbsp;Wenjin Dong ,&nbsp;Fengshu Zhao ,&nbsp;Tianqi Liu ,&nbsp;Mengxin Chen ,&nbsp;Rui Zhang ,&nbsp;Yumeng Zhao ,&nbsp;Cheng Zhang ,&nbsp;Wenhong Wang","doi":"10.1016/j.mri.2025.110338","DOIUrl":"10.1016/j.mri.2025.110338","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate the relationship between perirectal fat content and metachronous liver metastasis (MLM) in patients with Mid-low rectal cancer (MLRC).</div></div><div><h3>Materials and methods</h3><div>A retrospective analysis was conducted on 254 patients who underwent curative surgery for MLRC between December 2016 and December 2021. Preoperative MRI measurements of the rectal mesenteric fat area (MFA), rectal posterior mesorectal thickness (PMT), and rectal mesenteric fascia envelopment volume (MFEV) were performed, along with collection of relevant clinical, pathological, and imaging data. Patients were categorized into the MLM group (Group A), other recurrence or metastasis group (Group B), and no recurrence and metastasis group (Group C). Analyze the differences between Group A and the other groups, and independent risk factors for MLM were explored. Kaplan-Meier analysis and log-rank test were used to validate independent predictive biomarkers for MLM.</div></div><div><h3>Results</h3><div>Patients with MLM from MLRC had later pathological and imaging T stages and lower perirectal fat content (all <em>P</em> &lt; 0.05). Compared to patients with other types of recurrent metastasis, male gender, poorly differentiated tumors, and advanced tumor N stage were more likely to develop MLM (all <em>P</em> &lt; 0.05). In Cox univariate and multivariate regression analysis, smaller rectal PMT (hazard ratio (HR) 0.361 [0.154–0.846], <em>P</em> = 0.019) and MFEV (HR 0.983 [0.968–0.998], <em>P</em> = 0.022) were independently associated with MLM in MLRC (HR 0.361;0.983). Kaplan-Meier analysis showed that patients with rectal PMT &lt;1.43 cm and rectal MFEV &lt;137.46 cm³ had a significantly higher risk of MLM compared to patients with rectal PMT ≥1.43 cm and rectal MFEV ≥137.46 cm³ (all <em>P</em> &lt; 0.05).</div></div><div><h3>Conclusion</h3><div>Rectal PMT and rectal MFEV can serve as novel parameters for predicting MLM in patients with MLRC.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"118 ","pages":"Article 110338"},"PeriodicalIF":2.1,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of MRI radiomics and clinical data for preoperative prediction of vascular invasion in breast cancer: A deep learning approach.","authors":"Guihai Pan, Zejun Pan, Wubiao Chen, Yongjun Wu, Xiaoqing Di, Fei Zhou, Yuting Liao","doi":"10.1016/j.mri.2025.110339","DOIUrl":"https://doi.org/10.1016/j.mri.2025.110339","url":null,"abstract":"<p><strong>Background: </strong>Accurate preoperative prediction of vascular invasion in breast cancer is crucial for surgical planning and patient management. MRI radiomics has shown promise in enhancing diagnostic precision. This study aims to evaluate the effectiveness of integrating MRI radiomic features with clinical data using a deep learning approach to predict vascular invasion in breast cancer patients.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 102 patients with invasive breast cancer confirmed by surgical pathology. Using the MR750 3.0 T as the examination device, the subject underwent the examination in standard breast positions and sequences. Diffusion-weighted imaging (DWI) was performed with two selected b-values, specifically 0 and 1000 s/mm². Following the injection of the contrast agent, dynamic scans were conducted across six phases, and delayed phase sagittal images were acquired using the VIBRANT sequence. Texture features were extracted from MRI images, and key radiomic and clinical features were selected using variance thresholding, correlation filtering, and logistic regression. A predictive model was developed combining these features, and its performance was evaluated through sensitivity, specificity, and area under the curve (AUC) metrics.</p><p><strong>Results: </strong>The univariate models based on individual MRI sequences or clinical data demonstrated variable diagnostic performance. In contrast, the multifactorial model that combined radiomic features with clinical data achieved significantly higher accuracy, with an AUC of 0.829, sensitivity of 76.9 %, and specificity of 83.3 %.</p><p><strong>Conclusion: </strong>Integrating MRI radiomics and clinical data enhances the preoperative prediction of vascular invasion in breast cancer. This approach can improve diagnostic accuracy, providing valuable insights for clinical decision-making and personalized treatment strategies.</p>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":" ","pages":"110339"},"PeriodicalIF":2.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143066616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffusion-weighted arterial spin labeling MRI to investigate mannitol-induced blood brain barrier disruption","authors":"Yash Vardhan Tiwari ,&nbsp;Eric R. Muir ,&nbsp;Zhao Jiang ,&nbsp;Tim Q. Duong","doi":"10.1016/j.mri.2025.110335","DOIUrl":"10.1016/j.mri.2025.110335","url":null,"abstract":"<div><h3>Purpose</h3><div>Diffusion-weighted arterial spin labeling (DW-ASL) MRI has been proposed to determine the rate of water exchange (K_w) across the blood brain barrier (BBB). This study aims to further evaluate K_w MRI by comparing it with standard dynamic contrast-enhanced (DCE) MRI and histology in association with mannitol-induced disruption of the BBB.</div></div><div><h3>Methods</h3><div>DW-ASL was measured using a multiple b-value MRI protocol in normal rats at three post-labeling delays (<em>N</em> = 19), before and after intra-carotid injection of mannitol to disrupt BBB in one hemisphere (<em>N</em> = 13). An approach using only two b-values to detect mannitol-induced changes was also tested. DCE MRI and Evans blue histology were performed on the same animals. Quantitative analysis and pixel-by-pixel correlation were performed amongst K_w, DCE MRI and Evans blue histology.</div></div><div><h3>Results</h3><div>K_w in the grey matter in the normal rat brain was 252 ± 38 min⁻¹ (±standard error of the mean). The two b-value approach provided reasonable approximation of multiple-b DW-ASL parameters, reducing acquisition time. K_w is sensitive to mannitol-induced changes in BBB permeability and was reduced to 89 ± 17 min⁻¹ in the affected hemisphere compared to 191 ± 22 min⁻¹ in the unaffected hemisphere (<em>P</em> &lt; 0.05). Regions with abnormality in K_w maps were in general agreement with DCE and Evans blue maps, although there are some distinct differences in location and the change in values.</div></div><div><h3>Conclusion</h3><div>K_w is sensitive to mannitol-induced changes in the BBB, with BBB disruption confirmed by DCE MRI and Evans blue histology.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110335"},"PeriodicalIF":2.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FDuDoCLNet: Fully dual-domain contrastive learning network for parallel MRI reconstruction","authors":"Huiyao Zhang ,&nbsp;Tiejun Yang ,&nbsp;Heng Wang ,&nbsp;Jiacheng Fan ,&nbsp;Wenjie Zhang ,&nbsp;Mingzhu Ji","doi":"10.1016/j.mri.2025.110336","DOIUrl":"10.1016/j.mri.2025.110336","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that is widely used for high-resolution imaging of soft tissues and organs. However, the slow speed of MRI imaging, especially in high-resolution or dynamic scans, makes MRI reconstruction an important research topic. Currently, MRI reconstruction methods based on deep learning (DL) have garnered significant attention, and they improve the reconstruction quality by learning complex image features. However, DL-based MR image reconstruction methods exhibit certain limitations. First, the existing reconstruction networks seldom account for the diverse frequency features in the wavelet domain. Second, existing dual-domain reconstruction methods may pay too much attention to the features of a single domain (such as the global information in the image domain or the local details in the wavelet domain), resulting in the loss of either critical global structures or fine details in certain regions of the reconstructed image. In this work, inspired by the lifting scheme in wavelet theory, we propose a novel Fully Dual-Domain Contrastive Learning Network (FDuDoCLNet) based on variational networks (VarNet) for accelerating PI in both the image and wavelet domains. It is composed of several cascaded dual-domain regularization units and data consistency (DC) layers, in which a novel dual-domain contrastive loss is introduced to optimize the reconstruction performance effectively. The proposed FDuDoCLNet was evaluated on the publicly available fastMRI multi-coil knee dataset under a 6× acceleration factor, achieving a PSNR of 34.439 dB and a SSIM of 0.895.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110336"},"PeriodicalIF":2.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial-frequency aware zero-centric residual unfolding network for MRI reconstruction","authors":"Yupeng Lian ,&nbsp;Zhiwei Liu ,&nbsp;Jin Wang ,&nbsp;Shuai Lu","doi":"10.1016/j.mri.2025.110334","DOIUrl":"10.1016/j.mri.2025.110334","url":null,"abstract":"<div><div>Magnetic Resonance Imaging is a cornerstone of medical diagnostics, providing high-quality soft tissue contrast through non-invasive methods. However, MRI technology faces critical limitations in imaging speed and resolution. Prolonged scan times not only increase patient discomfort but also contribute to motion artifacts, further compromising image quality. Compressed Sensing (CS) theory has enabled the acquisition of partial k-space data, which can then be effectively reconstructed to recover the original image using advanced reconstruction algorithms. Recently, deep learning has been widely applied to MRI reconstruction, aiming to reduce the artifacts in the image domain caused by undersampling in k-space and enhance image quality. As deep learning continues to evolve, the undersampling factors in k-space have gradually increased in recent years. However, these layers are limited in compensating for reconstruction errors in the unsampled areas, impeding further performance improvements. To address this, we propose a learnable spatial-frequency difference-aware module that complements the learnable data consistency layer, mapping k-space domain differences to the spatial image domain for perceptual compensation. Additionally, inspired by wavelet decomposition, we introduce explicit priors by decomposing images into mean and residual components, enforcing a refined zero-mean constraint on the residuals while maintaining computational efficiency. Comparative experiments on the FastMRI and Calgary-Campinas datasets demonstrate that our method achieves superior reconstruction performance against seven state-of-the-art techniques. Ablation studies further confirm the efficacy of our model's architecture, establishing a new pathway for enhanced MRI reconstruction.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110334"},"PeriodicalIF":2.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving MRI turbulence quantification by addressing the measurement errors caused by the derivatives of the turbulent velocity field – Sequence development and in-vitro validation","authors":"Swantje Romig ,&nbsp;Kristine John ,&nbsp;Simon Schmidt ,&nbsp;Sebastian Schmitter ,&nbsp;Sven Grundmann ,&nbsp;Martin Bruschewski","doi":"10.1016/j.mri.2025.110333","DOIUrl":"10.1016/j.mri.2025.110333","url":null,"abstract":"<div><h3>Purpose</h3><div>To improve the current method for MRI turbulence quantification which is the intravoxel phase dispersion (IVPD) method. Turbulence is commonly characterized by the Reynolds stress tensor (RST) which describes the velocity covariance matrix. A major source for systematic errors in MRI is the sequence's sensitivity to the variance of the derivatives of velocity, such as the acceleration variance, which can lead to a substantial measurement bias.</div></div><div><h3>Methods</h3><div>We developed a Cartesian phase contrast sequence with FAST velocity encoding and two separately measured partial echoes with opposite readout directions. This design aims to reduce the high-order gradient moments that are responsible for the described measurement error. Velocity encoding directions follow the ICOSA6 scheme to capture the full RST. Turbulence data is reconstructed using the intra-voxel phase dispersion (IVPD) technique. We validated this sequence in vitro using a periodic hill flow benchmark with highly anisotropic turbulence. MRI data underwent extensive averaging, with multiple velocity encoding values employed to reduce noise and isolate systematic effects.</div></div><div><h3>Results</h3><div>The RST data obtained from the new sequence agree well with the ground truth. Compared to a state-of-the-art sequence, the maximum errors were reduced by factor five.</div></div><div><h3>Conclusion</h3><div>Simple adjustments to current MRI protocols can greatly enhance turbulence measurement accuracy through the reduction of high-order gradient moments. The proposed measures include applying FAST velocity encoding, high readout bandwidth, and a highly asymmetric readout. Ringing artifacts due to the asymmetric readout can be removed via a second, inverted readout.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110333"},"PeriodicalIF":2.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The relative contrast ratio of malignant bone lesion is highest on relative Fat Fraction maps compared with T1-weighted and b900 DWI images","authors":"Francesca Castagnoli ,&nbsp;Alina Dragan ,&nbsp;Antonio Candito ,&nbsp;Nina Tunariu ,&nbsp;Christina Messiou ,&nbsp;Dow-Mu Koh","doi":"10.1016/j.mri.2025.110331","DOIUrl":"10.1016/j.mri.2025.110331","url":null,"abstract":"<div><h3>Introduction</h3><div>Whole-body MRI (WB-MRI) is increasingly used in clinical practice for detection of malignant bone disease. A high relative contrast ratio (RCR) of malignant bone lesions compared with normal bone can improve disease detection in breast, prostate and myeloma malignancies. However, the RCR of malignant bone lesions on T1w, DWI and relative Fat Fraction (rFF) maps derived from Dixon T1w have not been compared.</div></div><div><h3>Methods</h3><div>110 baseline WB-MRI of patients with suspected malignant bone lesions were reviewed retrospectively. On each scan, up to four active bone lesions were identified, one each at the cervicothoracic spine, lumbosacral spine, pelvis and extremity, and their ROI signal intensity measured on rFF, T1w and DWI b = 900. The signal intensity of background bone was measured by placing an ROI on the nearest normal-appearing bone to each lesion, for each sequence. The mean lesion signal-to-background ratio (taken as RCR) for each lesion was calculated. We compared the RCR of bone lesions on rFF, T1w and DWI (Mann-Whitney test).</div></div><div><h3>Results</h3><div>The median rFF RCR of malignant bone lesions was highest compared with normal bone marrow than that of T1w (<em>p</em> &lt; 0.0001) and DWI (p &lt; 0.0001). There was no significant difference in the median rFF RCR of malignant bone lesions from breast cancer, myeloma and prostate cancer (<em>p</em> &gt; 0.017, Bonferroni correction) or according to their anatomical locations (<em>p</em> &gt; 0.012, Bonferroni correction).</div></div><div><h3>Conclusions</h3><div>Malignant bone lesion RCR measured by lesion/background signal intensity was higher on rFF than on T1w and DWI b = 900 in patients with prostate, breast and myeloma malignancies, indicating its value for disease detection.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110331"},"PeriodicalIF":2.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognosticating WHO/ISUP grade in clear cell renal cell carcinoma: Insights from amide proton transfer-weighted MRI","authors":"Qingxuan Wan ,&nbsp;Yun Xu ,&nbsp;Yutao Jiang ,&nbsp;Jinglu Li ,&nbsp;Yulan Li ,&nbsp;Xihui Ren ,&nbsp;Fang Wang ,&nbsp;Xuyou Zhu ,&nbsp;Gang Wu ,&nbsp;Peng WU ,&nbsp;Peijun Wang ,&nbsp;Aijun Shen","doi":"10.1016/j.mri.2025.110332","DOIUrl":"10.1016/j.mri.2025.110332","url":null,"abstract":"<div><h3>Background</h3><div>Preoperative prediction of clear cell renal cell carcinoma (ccRCC) grade can support optimal selection of surgical resection strategies. Currently, there is no effective preoperative method for accurately assessing the histologic grade of ccRCC. More precise, non-invasive prediction methods are needed.</div></div><div><h3>Purpose</h3><div>To investigate the parameter of amide proton transfer-weighted (APTw) MRI for prediction of pathological ccRCC grade and whether the combination of the APTw, Diffusion-Weighted Imaging (DWI), and mDixon MRI techniques can improve the prediction performance.</div></div><div><h3>Materials and methods</h3><div>This prospective study included ccRCC patients who underwent multiparametric MRI examinations (including APTw, DWI, and mDixon) along with contrast-enhanced CT or MRI examinations from March 2021 to June 2024. The mean, maximum, minimum, and standard deviation (SD) values from APTw and apparent diffusion coefficient (ADC) imaging of the solid tumor portions and the fat fraction (FF) values from mDixon imaging were measured. The area under the receiver operating characteristic curve (AUC) values for the parameters were calculated to evaluate the diagnostic performance.</div></div><div><h3>Results</h3><div>A total of 48 ccRCC patients (mean age, 65 years; 32 males) were included, of whom 10 had high-grade ccRCC and 38 had low-grade ccRCC. Compared with low-grade ccRCC, high-grade ccRCC exhibited higher APTw_mean (2.28 % vs. 4.17 %; <em>P</em> &lt; .05), APTw_max (4.97 % vs. 8.48 %; <em>P</em> &lt; .01), and APTwSD (1.10 vs. 1.75; <em>P</em> &lt; .01) values as well as lower ADC_min values (1.23 × 10⁻³ mm²/s vs. 0.94 × 10⁻³ mm²/s; <em>P</em> &lt; .05). The AUC values for the ability of APTw_mean, APTw_max, APTwSD, ADC_min, and these factors combined to distinguish between low-grade and high-grade ccRCC were 0.728, 0.786, 0.832, 0.741, and 0.858, respectively.</div></div><div><h3>Conclusion</h3><div>APTw_mean, APTw_max, APTwSD, and ADC_min values are potential indicators for preoperative prediction of ccRCC pathologic grade. APTw parameters offer certain advantages for use alone or in combination with other parameters.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"117 ","pages":"Article 110332"},"PeriodicalIF":2.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143039642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}