Magnetic resonance vessel wall imaging (MR-VWI) is an emerging imaging technology used to assess the progressive risk of unruptured intracranial aneurysms (UIAs). Unlike the standard evaluation model, MR-VWI is still debatable. This study aims to further define the potential relationship between aneurysm wall enhancement (AWE) and aneurysm stability. Using \"intracranial aneurysm\", \"magnetic resonance\", and \"enhancement\" as keywords, relevant studies were systematically searched in PubMed, Embase, and Cochrane, and the qualified studies were enrolled for further analysis. There were 13 case-control studies, 4 cohort studies, and 2,678 cases of intracranial aneurysms included in the meta-analysis. It was shown that AWE was correlated with intracranial aneurysm rupture (OR = 35.90, 95% CI: 15.58 to 82.75, p < 0.001), growth (OR = 6.69, 95% CI: 2.69 to 16.63, p < 0.001), and presence of symptoms (OR = 14.46, 95% CI: 9.07 to 23.05, p < 0.001). This finding had a high diagnostic value, but the correlation was probably not independent of aneurysm size. The pooled relative risks of the follow-up studies revealed that the risk of UIA progression was approximately 3.33 times higher with AWE than without AWE (RR = 3.33, 95% CI: 2.33 to 4.78, p < 0.001). In addition, the pooled results demonstrated that quantitative indices of VWI enhancement were equally linked with aneurysm stability (OR = 19.61, 95% CI: 10.63 to 36.17, p < 0.001). AWE is an effective imaging method to assess the stability of UIAs, and it can be a marker for the prophylactic treatment of small unruptured intracranial aneurysms in the future, which remains to be validated by prospective studies with large samples.

BACKGROUND: To investigate the risk factors affecting patients with placenta previa (PP) and to construct an effective prediction model for the severity of PAS in PP.
METHODS: A total of 240 pregnant women with PP were enrolled in this study. An MRI+Ultrasound-based model was developed to classify patients into placental implantation and non-placental implantation groups. Multivariate nomograms were created based on imaging features. The model was evaluated using Receiver Operating Characteristic (ROC) curve analysis. The predictive accuracy of the nomogram was assessed through calibration plots and decision curve analysis.
RESULTS: The MRI+Ultrasound-based prediction model demonstrated favorable discrimination between the placental implantation and non-placental implantation groups. The calibration curve exhibited agreement between the estimated and actual probability of placental implantation. Additionally, decision curve analysis indicated a high clinical benefit across a wide range of probability thresholds. The Area under the ROC curve (AUC) was 0.911 (95 % CI: 0.76-0.947), with a sensitivity of 88.40 % and specificity of 88.10 %.
CONCLUSIONS: The MRI+Ultrasound-based prediction model could be a valuable tool for preoperative prediction of the percentage of implantation. Our study enables obstetricians to conduct more adequate preoperative evaluations.

OBJECTIVE: This study aimed to assess the practical application of conventional two-dimensional (2D) pelvic ultrasound in conjunction with three-dimensional (3D) ultrasound for evaluating obstructive Müllerian abnormalities.
METHODS: Respective study in tertiary referral hospital METHOD: Computerized stored data was used to collect surgical confirmed obstructive Müllerian anomalies cases between December 2022 and October 2023 with presurgical imagings being evaluated. Acute presentation with abdominal pain and clinical suspicion of obstructive Müllerian abnormality were required for inclusion. All study participants underwent pelvic ultrasound prior to the definitive surgery, with or without a repeat MRI if one was performed previous to admission. Those situations where both MRI and ultrasound were not conducted were excluded, such as the transverse vaginal septum, imperforate hymen, iatrogenic cervical injury or Müllerian malformation alone without obstructive outflow anomalies like didelphys, bicornuate, or septate uterus.
METHODS: The concordance between the surgically confirmed diagnosis and the pelvic ultrasound was reported in 27 of 29 women (93.1%). In contrast, only 24 of 29 cases were correctly diagnosed with MRI in this study (82.8%). This pilot study presents a comparison of two techniques, with a specific focus on obstructive Müllerian anomalies. The use of pelvic ultrasound not only assisted in our surgical practice but also significantly improved patient-doctor counseling.
CONCLUSIONS: In managing obstructive Müllerian abnormalities, 3D-enhanced conventional pelvic ultrasound was found effective in diagnosis and was comparable to MRI.

UNASSIGNED: We aimed to integrate MR radiomics and dynamic hematological factors to build a model to predict pathological complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in esophageal squamous cell carcinoma (ESCC).
UNASSIGNED: Patients with ESCC receiving NCRT and esophagectomy between September 2014 and September 2022 were retrospectively included. All patients underwent pre-treatment T2-weighted imaging as well as pre-treatment and post-treatment blood tests. Patients were randomly divided to training set and testing set at a ratio of 7:3. Machine learning models were constructed based on MR radiomics and hematological factors to predict pCR, respectively. A nomogram model was developed to integrate MR radiomics and hematological factors. Model performances were evaluated by areas under curves (AUCs), sensitivity, specificity, positive predictive value and negative.
UNASSIGNED: A total of 82 patients were included, of whom 39 (47.6 %) achieved pCR. The hematological model built with four hematological factors had an AUC of 0.628 (95%CI 0.391-0.852) in the testing set. Two out of 1106 extracted features were selected to build the radiomics model with an AUC of 0.821 (95%CI 0.641-0.981). The nomogram model integrating hematological factors and MR radiomics had best predictive performance, with an AUC of 0.904 (95%CI 0.770-1.000) in the testing set.
UNASSIGNED: An integrated model using dynamic hematological factors and MR radiomics is constructed to accurately predicted pCR to NCRT in ESCC, which may be potentially useful to assist individualized preservation treatment of the esophagus.

暂无摘要。

OBJECTIVE: Peritoneal free cancer cells can negatively impact disease progression and patient outcomes in gastric cancer. This study aimed to investigate the feasibility of using golden-angle radial sampling dynamic contrast-enhanced magnetic resonance imaging (GRASP DCE-MRI) to predict the presence of peritoneal free cancer cells in gastric cancer patients.
METHODS: All enrolled patients were consecutively divided into analysis and validation groups. Preoperative magnetic resonance imaging (MRI) scans and perfusion were performed in patients with gastric cancer undergoing surgery, and peritoneal lavage specimens were collected for examination. Based on the peritoneal lavage cytology (PLC) results, patients were divided into negative and positive lavage fluid groups. The data collected included clinical and MR information. A nomogram prediction model was constructed to predict the positive rate of peritoneal lavage fluid, and the validity of the model was verified based on data from the verification group.
RESULTS: There was no statistical difference between the proportion of PLC-positive cases predicted by GRASP DCE-MR and the actual PLC test. MR tumor stage, tumor thickness, and perfusion parameter Tofts-Ketty model volume transfer constant (Ktrans) were independent predictors of positive peritoneal lavage fluid. The nomogram model featured a concordance index (C-index) of 0.785 and 0.742 for the modeling and validation groups, respectively.
CONCLUSIONS: GRASP DCE-MR could effectively predict peritoneal free cancer cells in gastric cancer patients. The nomogram model constructed using these predictors may help clinicians to better predict the risk of peritoneal free cancer cells being present in gastric cancer patients.
胃癌腹腔游离癌细胞可对疾病进展和患者预后产生不利影响。本研究旨在探讨金角径向采样动态增强磁共振成像（GRASP DCE-MRI）预测胃癌患者腹膜游离癌细胞存在的可行性。对胃癌患者进行术前磁共振成像（MRI）扫描和灌注后处理，并采集患者术前腹腔灌洗标本进行检测。根据患者入组顺序将其分为实验组及验证组，将实验组数据进行多元回归分析并筛选有意义的变量，建立预测腹膜灌洗液阳性率的nomogram预测模型，并根据验证组数据对模型的有效性进行验证。研究发现，GRASP DCE-MR预测的腹膜灌洗细胞学（PLC）阳性病例比例与实际的PLC检测结果无统计学差异。肿瘤T分期、肿瘤厚度和灌注参数容积转移常数（Ktrans）均是腹膜灌洗液阳性的独立预测因子。用这些预测因子构建的nomogram模型可以帮助临床医生更好地预测胃癌患者腹膜游离癌细胞存在的风险。.

OBJECTIVE: Peritoneal free cancer cells can negatively impact disease progression and patient outcomes in gastric cancer. This study aimed to investigate the feasibility of using golden-angle radial sampling dynamic contrast-enhanced magnetic resonance imaging (GRASP DCE-MRI) to predict the presence of peritoneal free cancer cells in gastric cancer patients.
METHODS: All enrolled patients were consecutively divided into analysis and validation groups. Preoperative magnetic resonance imaging (MRI) scans and perfusion were performed in patients with gastric cancer undergoing surgery, and peritoneal lavage specimens were collected for examination. Based on the peritoneal lavage cytology (PLC) results, patients were divided into negative and positive lavage fluid groups. The data collected included clinical and MR information. A nomogram prediction model was constructed to predict the positive rate of peritoneal lavage fluid, and the validity of the model was verified based on data from the verification group.
RESULTS: There was no statistical difference between the proportion of PLC-positive cases predicted by GRASP DCE-MR and the actual PLC test. MR tumor stage, tumor thickness, and perfusion parameter Tofts-Ketty model volume transfer constant (Ktrans) were independent predictors of positive peritoneal lavage fluid. The nomogram model featured a concordance index (C-index) of 0.785 and 0.742 for the modeling and validation groups, respectively.
CONCLUSIONS: GRASP DCE-MR could effectively predict peritoneal free cancer cells in gastric cancer patients. The nomogram model constructed using these predictors may help clinicians to better predict the risk of peritoneal free cancer cells being present in gastric cancer patients.
胃癌腹腔游离癌细胞可对疾病进展和患者预后产生不利影响。本研究旨在探讨金角径向采样动态增强磁共振成像（GRASP DCE-MRI）预测胃癌患者腹膜游离癌细胞存在的可行性。对胃癌患者进行术前磁共振成像（MRI）扫描和灌注后处理，并采集患者术前腹腔灌洗标本进行检测。根据患者入组顺序将其分为实验组及验证组，将实验组数据进行多元回归分析并筛选有意义的变量，建立预测腹膜灌洗液阳性率的nomogram预测模型，并根据验证组数据对模型的有效性进行验证。研究发现，GRASP DCE-MR预测的腹膜灌洗细胞学（PLC）阳性病例比例与实际的PLC检测结果无统计学差异。肿瘤T分期、肿瘤厚度和灌注参数容积转移常数（Ktrans）均是腹膜灌洗液阳性的独立预测因子。用这些预测因子构建的nomogram模型可以帮助临床医生更好地预测胃癌患者腹膜游离癌细胞存在的风险。.

To promote the development of auto-segmentation methods for head and neck (HaN) radiation treatment (RT) planning that exploit the information of computed tomography (CT) and magnetic resonance (MR) imaging modalities, we organized HaN-Seg: The Head and Neck Organ-at-Risk CT and MR Segmentation Challenge.
The challenge task was to automatically segment 30 organs-at-risk (OARs) of the HaN region in 14 withheld test cases given the availability of 42 publicly available training cases. Each case consisted of one contrast-enhanced CT and one T1-weighted MR image of the HaN region of the same patient, with up to 30 corresponding reference OAR delineation masks. The performance was evaluated in terms of the Dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD95), and statistical ranking was applied for each metric by pairwise comparison of the submitted methods using the Wilcoxon signed-rank test.
While 23 teams registered for the challenge, only seven submitted their methods for the final phase. The top-performing team achieved a DSC of 76.9 % and a HD95 of 3.5 mm. All participating teams utilized architectures based on U-Net, with the winning team leveraging rigid MR to CT registration combined with network entry-level concatenation of both modalities.
This challenge simulated a real-world clinical scenario by providing non-registered MR and CT images with varying fields-of-view and voxel sizes. Remarkably, the top-performing teams achieved segmentation performance surpassing the inter-observer agreement on the same dataset. These results set a benchmark for future research on this publicly available dataset and on paired multi-modal image segmentation in general.

Pelvic masses frequently originate from the pelvic cavity and are often associated with uterine, ovarian, or intestinal disorders. This report describes the case of a patient with a pelvic mass diagnosed as a retroperitoneal dermoid cyst at our hospital. We analyzed this case and conducted a literature review, to mitigate the risk of misdiagnosis and enhance the treatment of retroperitoneal masses.

UNASSIGNED: Currently, there is a lack of an objective quantitative measure to comprehensively evaluate the inflammatory activity of axSpA, which poses certain challenges in accurately assessing the disease activity.
UNASSIGNED: To explore the value of combined-parameter models of sacroiliac joints (SIJs) MRI relaxometry and peripheral blood Mucosal-associated invariant T (MAIT) cells in evaluating the inflammatory activity of axial spondyloarthritis (axSpA).
UNASSIGNED: This retrospective clinical study included 88 axSpA patients (median age 31.0 (22.0, 41.8) years, 21.6% females) and 20 controls (median age 28.0 (20.5, 49.5) years, 40.0% females). The axSpA group was classified into active subgroup (n=50) and inactive subgroup (n=38) based on ASDAS-CRP. All participants underwent SIJs MRI examination including T1 and T2* mapping, and peripheral blood flow cytometry analysis of MAIT cells (defined as CD3+Vα7.2+CD161+) and their activation markers (CD69). The T1 and T2* values, as were the percentages of MAIT cells and CD69+MAIT cells were compared between different groups. Combined-parameter models were established using logistic regression, and ROC curves were employed to evaluate the diagnostic efficacy.
UNASSIGNED: The T1 values of SIJs and %CD69+MAIT cells in the axSpA group and its subgroup were higher than the control group (p<0.05), while %MAIT cells were lower than the control group (p<0.05). The T1 values and %CD69+MAIT cells correlated positively, while %MAIT cells correlated negatively, with the ASDAS-CRP (r=0.555, 0.524, -0.357, p<0.001). Between the control and axSpA groups, and between the inactive and active subgroups, the combined-parameter model T1 mapping+%CD69+MAIT cells has the best efficacy (AUC=0.959, 0.879, sensibility=88.6, 70%, specificity=95.0, 94.7%, respectively).
UNASSIGNED: The combined-parameter model T1 mapping+%CD69+MAIT cells allows a more accurate evaluation of the level of inflammatory activity.