Aberrant expression of Fos-related antigen-1 (Fra1) is commonly elevated in various malignant cancers and is strongly implicated in invasion and metastasis. However, the molecular mechanisms underlying its dysregulation in human glioma remain poorly understood. In the present study, we demonstrate that up-regulation of Fra1 plays a crucial role in the glioma aggressiveness and epithelial–mesenchymal transition (EMT) activated by Wnt/β-catenin signal pathway. In glioma cells, activation of Wnt/β-catenin signalling by Wnt3a administration obviously induced EMT and directly activated the transcription of Fra1. Phenotype experiments revealed that up-regulation of Fra1 induced by Wnt/β-catenin signalling drove the EMT of glioma cells. Furthermore, it was found that the cisplatin resistance acquired by Wnt/β-catenin signalling activation depended on increased expression of Fra1. Analysis of clinical specimens verified a positive correlation between Fra1 and β-catenin as well as a poor prognosis in glioma patients with double-high expressions of them. These findings indicate that an aberrant Wnt/β-catenin signalling leads to the EMT and drug resistance of glioma via Fra1 induction, which suggests novel therapeutic strategies for the malignant disease.

OBJECTIVES:To investigate the clinical significance of microstructure imaging based on MR cytometry in the qualitative diagnosis of musculoskeletal tumors (MSTs). MATERIALS AND METHODS:Participants with clinically suspected MSTs between March 2024 and March 2025 were prospectively enrolled. Conventional apparent diffusion coefficient (ADC) value and four microstructural parameters, including cell diameter (d), intracellular volume fraction (vin), Cellularity index, and extracellular diffusivity (Dex), were estimated using the IMPULSED method (a form of MR cytometry). The ADCPGSE, ADC25 Hz and ADC40 Hz were measured at three different effective diffusion times, and the change ADC (cADC) and relative change ADC (rcADC) were measured. The performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. RESULTS:A total of 62 participants with benign and malignant MSTs (mean age, 47.7 ± 20.9 [SD] years; 21 women, 41men) were enrolled. Among these ADC parameters, rcADC had the highest AUC (AUC = 0.737; 95 %Cl: 0.600, 0.874; p = 0.002). Among the four microstructure parameters derived from MR cytometry, Cellularity index had the highest AUC (AUC = 0.735; 95 %Cl: 0.601, 0.868; p = 0.002). Among all parameters, the AUC of rcADC and Cellularity index is higher than that of conventional ADC (AUC = 0.728; 95 %Cl: 0.594, 0.863; p = 0.003). The combination of the four microstructure parameters of MR cytometry further improved the diagnostic performance (AUC = 0.758; 95 %Cl: 0.626, 0.890; p = 0.001). CONCLUSION:MR cytometry was an effective method for helping to predict the benign and malignant MSTs and is superior to conventional ADC values.

Background:Knee osteoarthritis (KOA) is a common knee joint degenerative disorder. Total knee arthroplasty (TKA) is a surgical treatment option that not only corrects knee flexion contraction but also impacts spino-pelvic alignment, leading to altered standing posture and gait. This study aimed to utilize a low-dose biplanar X-ray device (EOS) whole-body X-ray imaging to assess the impact of knee flexion deformity correction by TKA on spino-pelvic alignment in patients with KOA. Methods:A retrospective cohort study was conducted in patients with varus-type KOA who underwent TKA between April 2023 and May 2024. Both sagittal and coronal alignments were measured in preoperative and postoperative EOS images. Patients were categorized into Group A or B based on Δknee flexion angle (ΔKFA) (<10° and ≥10°, respectively). Changes in postoperative alignment were analyzed using the paired t-test or the Wilcoxon signed-rank test. Pearson or Spearman correlation and multivariate linear regression analysis were employed to evaluate the relationship between changes in KFA and spino-pelvic parameters. Results:Among the 70 patients, the average knee flexion corrections were 5.1° in Group A (n=39) and 13.2° in Group B (n=31). Postoperative assessments showed decreases in hip-knee-ankle, pelvic tilt, spino sacral angle, T1 spinopelvic inclination (T1SPI), and T9 spinopelvic inclination, whereas increases in sacral slope, lumbar lordosis (LL), and C7 sagittal vertical axis (P<0.001). No significant inter-group differences were observed in these changes, except that LL increased in Group B and remained unchanged in Group A following surgery. Statistically significant correlations were noted between ΔKFA and both ΔT1SPI [Pearson correlation coefficient (PCC): -0.330, P=0.019] and ΔT9 spinopelvic inclination (ΔT9SPI) (PCC: -0.404, P=0.004). Multivariate linear regression analysis revealed that ΔKFA was significantly associated with ΔT1SPI [β=-0.341, 95% confidence interval (CI): -0.741 to 0.059, P=0.037] and ΔT9SPI (β=-0.437, 95% CI: -0.962 to 0.089, P=0.011). Conclusions:Following TKA in patients with KOA, knee flexion deformity changes may lead to improved spino-pelvic sagittal alignment, but not coronal alignment. Additionally, the KFA change was correlated with changes in T1SPI and T9SPI in these patients. Therefore, clinicians should be aware of potential changes in spino-pelvic alignment when correcting lower limb alignment. A comprehensive preoperative evaluation may enhance surgical outcomes and prognosis.

Objective Vertebral compression fractures (VCFs) represent a prevalent clinical problem, yet distinguishing acute benign variants from malignant pathological fractures constitutes a persistent diagnostic dilemma. To develop and validate a MRI-based nomogram combining clinical and deep learning radiomics (DLR) signatures for the differentiation of benign versus malignant vertebral compression fractures (VCFs).Methods A retrospective cohort study was conducted involving 234 VCF patients, randomly allocated to training and testing sets at a 7:3 ratio. Radiomics (Rad) features were extracted using traditional Rad techniques, while 2.5-dimensional (2.5D) deep learning (DL) features were obtained using the ResNet50 model. These features were combined through feature fusion to construct deep learning radiomics (DLR) models. Through a feature fusion strategy, this study integrated eight machine learning architectures to construct a predictive framework, ultimately establishing a visualized risk assessment scale based on multimodal data (including clinical indicators and Rad features).The performance of the various models was evaluated using the receiver operating characteristic (ROC) curve.Results The standalone Rad model using ExtraTrees achieved AUC=0.801 (95%CI:0.693-0.909) in testing, while the DL model an AUC value of 0.805 (95% CI: 0.690-0.921) in the testing cohort. Compared with the Rad model and DL model, the performance superiority of the DLR model was demonstrated. Among all these models, the DLR model that employed ExtraTrees algorithm performed the best, with area under the curve (AUC) values of 0.971 (95% CI: 0.948-0.995) in the training dataset and 0.828 (95% CI: 0.727-0.929) in the testing dataset. The performance of this model was further improved when combined with clinical and MRI features to form the DLR nomogram (DLRN), achieving AUC values of 0.981 (95% CI: 0.964-0.998) in the training dataset and 0.871 (95% CI: 0.786-0.957) in the testing dataset.Conclusion Our study integrates handcrafted radiomics, 2.5D deep learning features, and clinical data into a nomogram (DLRN). This approach not only enhances diagnostic accuracy but also provides superior clinical utility. The novel 2.5D DL framework and comprehensive feature fusion strategy represent significant advancements in the field, offering a robust tool for radiologists to differentiate benign from malignant VCFs.

[This corrects the article DOI: 10.3389/fonc.2025.1603672.].

BACKGROUND:Traditional biopsies pose risks and may not accurately reflect soft tissue sarcoma (STS) heterogeneity. MRI provides a noninvasive, comprehensive alternative.PURPOSE:To assess the diagnostic accuracy of histological grading and prognosis in STS patients when integrating clinical-imaging parameters with deep learning (DL) features from preoperative MR images.STUDY TYPE:Retrospective/prospective.POPULATION:354 pathologically confirmed STS patients (226 low-grade, 128 high-grade) from three hospitals and the Cancer Imaging Archive (TCIA), divided into training (n = 185), external test (n = 125), and TCIA cohorts (n = 44). 12 patients (6 low-grade, 6 high-grade) were enrolled into prospective validation cohort.FIELD STRENGTH/SEQUENCE:1.5 T and 3.0 T/Unenhanced T1-weighted and fat-suppressed-T2-weighted.ASSESSMENT:DL features were extracted from MR images using a parallel ResNet-18 model to construct DL signature. Clinical-imaging characteristics included age, gender, tumor-node-metastasis stage and MRI semantic features (depth, number, heterogeneity at T1WI/FS-T2WI, necrosis, and peritumoral edema). Logistic regression analysis identified significant risk factors for the clinical model. A DL clinical-imaging signature (DLCS) was constructed by incorporating DL signature with risk factors, evaluated for risk stratification, and assessed for progression-free survival (PFS) in retrospective cohorts, with an average follow-up of 23 ± 22 months.STATISTICAL TESTS:Logistic regression, Cox regression, Kaplan-Meier curves, log-rank test, area under the receiver operating characteristic curve (AUC)，and decision curve analysis. A P-value <0.05 was considered significant.RESULTS:The AUC values for DLCS in the external test, TCIA, and prospective test cohorts (0.834, 0.838, 0.819) were superior to clinical model (0.662, 0.685, 0.694). Decision curve analysis showed that the DLCS model provided greater clinical net benefit over the DL and clinical models. Also, the DLCS model was able to risk-stratify patients and assess PFS.DATA CONCLUSION:The DLCS exhibited strong capabilities in histological grading and prognosis assessment for STS patients, and may have potential to aid in the formulation of personalized treatment plans.LEVEL OF EVIDENCE: 4:TECHNICAL EFFICACY:Stage 2.

Background: Marathon training can reverse bone marrow conversion; however, little is known about the normal bone marrow whole-body diffusion-weighted imaging (WB-DWI) signal characteristics of amateur marathon runners. If marathon training can cause diffuse hyperintensity of bone marrow on WB-DWI is essential for correctly interpreting the diffusion-weighted (DW) images. This study sought to evaluate the WB-DWI signal characteristics of normal bone marrow in amateur marathon runners. Methods: In this prospective cross-sectional study, 30 amateur marathon runners who had trained for over 3 years for regular or half-marathon races and had a running frequency of more than 20 days a month at a distance of more than 100 km per month from the Chengde Marathon Outdoor Sports Association in Hebei, China, and 30 age- and gender-matched, healthy volunteers (the control group) who had no long-term heavy-load sports history were recruited between April 2021 to September 2021. All the subjects underwent WB-DWI (b-value: 0, 800 s/mm(2)) and lumbar vertebral transverse relaxation time (T2) mapping. The bone marrow WB-DWI signal characteristics were analyzed visually and statistically by chi-square (chi(2)) tests. The apparent diffusion coefficient (ADC), DWI signal intensity, and T2 values of the bone marrow were quantitatively and statistically analyzed by the independent sample t-test and Mann-Whitney U test. Results: No subjects were excluded from the study. The bone marrow of 30 of the 60 subjects (aged 30-50 years) showed diffuse hyperintensity in the DW images. However, in all 60 subjects, the humeral heads, femoral heads, and great trochanters had low signals. The frequency of diffuse bone marrow DWI hyperintensity was significantly higher in the male amateur marathon runners (50%) than the male controls (5%, P=0.003), but no such significant difference was found between the female amateur marathon runners (100%) and female controls (90%, P>0.99). The DW signal intensity ratios of bone marrow to muscle (SIRBM-muscle) were significantly higher in the male amateur marathon runners than the male controls in the thoracic vertebrae (4.68 vs. 3.57, P=0.021), lumbar vertebrae (4.49 vs. 3.01, P<0.001), sacrum (3.67 vs. 2.62, P=0.002), and hip (3.45 vs. 2.50, P=0.002), but were only significantly higher in the female amateur marathon runners than the female controls in the thoracic vertebrae (7.69 vs. 5.87, P=0.029) and hip (4.76 vs. 3.92, P=0.004). The mean T2 values of the lumbar vertebrae were significantly higher in the male amateur marathon runners than the male controls (116.76 vs. 97.63 ms, P=0.001), but no such significant difference was observed between the female amateur marathon runners and the corresponding controls (118.58 vs. 124.10 ms, P=0.386). Conclusions: Marathon training resulted in diffuse hyperintensity in the bone marrow based on WB-DWI in 50% of the male amateur marathon runners aged 30-50 years. Thus, when WB-DWI is used for bone marrow disease screening, marathon training history should be considered to avoid false-positive diagnoses.

目的 报道1例原发性中轴骨滑膜肉瘤的影像学表现并复习文献,提高对该病的认识.方法 回顾性分析经术后病理证实的1例中轴骨滑膜肉瘤患者的影像学资料,同时复习国内外文献,总结原发性中轴骨滑膜肉瘤的影像学特征.结果 原发性中轴骨滑膜肉瘤CT表现为膨胀性、溶骨性骨质破坏,密度不均,局部骨皮质缺损,伴或不伴周围软组织肿块;MRI特征性表现为"三重信号征"、"铺路石征".结论 原发性中轴骨滑膜肉瘤是一种罕见的间叶组织来源恶性肿瘤,其诊断依赖组织病理学、免疫组织化学和分子遗传学检查.

Background: This study sought to predict the early responses to neoadjuvant chemotherapy (NACT) of patients with primary conventional osteosarcoma (COS) using the apparent diffusion coefficient (ADC) and to evaluate the factors affecting the tumor necrosis rate (TNR). Methods: The data of 41 patients who underwent magnetic resonance imaging (MRI) and diffusion-weighted imaging sequence scans before NACT, 5 days after the end of the first phase of NACT, after the end of the whole course of chemotherapy, were prospectively collected. ADC1 refers to the ADC before chemotherapy, ADC2 refers to the ADC after the first phase of chemotherapy, and ADC3 refers to the ADC before surgery. The change in values before and after the first phase of chemotherapy was calculated as follows: ADC2-1 = ADC2 - ADC1. The change in values before and after the last phase of chemotherapy was calculated as follows: ADC3-1 = ADC3 - ADC1. The change in values after the first phase and the last phase of chemotherapy was calculated as follows: ADC3-2 = ADC3 - ADC2. We recorded the patient characteristics, including age, gender, pulmonary metastasis, alkaline phosphatase (ALP), and lactate dehydrogenase (LDH). The patients were divided into the following 2 groups based on their histological TNR after postoperative: (I) the good-response group (>= 90% necrosis, n=13) and (II) the poor-response group (<90% necrosis, n=28). Changes in the ADCs were compared between the good-response and poor-response groups. The different ADCs between the 2 groups were compared, and a receiver operating characteristic analysis was performed. A correlation analysis was performed to assess the correlations of the clinical features, laboratory features, and different ADCs with patients' histopathological responses to NACT. Results: The ADC2 (P<0.001), ADC3 (P=0.004), ADC3-1 (P=0.008), ADC3-2 (P=0.047), and ALP before NACT (P=0.019) were significantly higher in the good-response group than in the poor-response group. The ADC2 [ area under the curve (AUC) =0.723; P=0.023], ADC3 (AUC =0.747; P=0.012), and ADC3-1 (AUC =0.761; P=0.008) showed good diagnostic performance. Based on the univariate binary logistic regression analysis, the ADC2 (P=0.022), ADC3 (P=0.009), ADC2-1 (P=0.041), and ADC3-1 (P=0.014) were correlated with the TNR. However, based on the multivariate analysis, these parameters were not significantly correlated with the TNR. Conclusions: In patients with COS who are undergoing neoadjuvant chemotherapy, the ADC2 is a promisingindicator for predicting tumor response to chemotherapy in early.

患者 女,44 岁,因右手背肿痛 20 d 入院.查体:右手第 2 掌骨处肿胀伴压痛.影像学检查:右手 X线平片及 CT (图 1 ,2)示右手第 2 掌骨不规则溶骨性破坏,尺侧骨皮质不连续伴骨膜反应.右手MRI平扫及增强(图 3～5)示右手第 2 掌骨体部骨质破坏伴髓腔内及周围软组织肿块,呈等 T1 稍长 T2 信号,局部可见小囊状长 T2 信号,肿块实性部分明显强化,考虑恶性肿瘤.术中见右手第 2 掌骨体骨质破坏,被灰白、质韧肿瘤组织替代,内侧骨皮质破坏,向外形成软组织占位.大体病理示肿物切面灰白质韧,糟脆,侵破骨皮质.

Objective:To investigate the value of MRI DWI-based radiomics models for evaluating the treatment response in osteosarcoma after neoadjuvant chemoherapy.Methods:A retrospective analysis was conducted on the medical records and imaging data of 41 patients with osteosarcoma (26 males and 15 females; aged (22.0±11.0) years, range of 11–49 years) who underwent MRI examinations before and after receiving neoadjuvant chemotherapy, and confirmed by postoperative histopathological examinations at the Third Hospital of Hebei Medical University from June 2015 to November 2017. In accordance with the postoperative histopathological examination results, patients with a tumor tissue necrosis rate of ≥90% were included in the good-efficacy group, and those with a necrosis rate of <90% were included in the poor-efficacy group. The apparent diffusion coefficient (ADC, denoted as ADC0, ADC1, and ADC2) were measured in all patients before neoadjuvant chemotherapy, within 5 days after the end of the first stage of chemotherapy, and after the completion of the entire chemotherapy. The differences in ADC were compared between the two groups. The region of interest of the lesion was manually delineated on DWI (b=1 000 s/mm 2) and ADC images after the end of the first stage of chemotherapy, and the radiomics features were extracted. Data were divided into training set and validation set by using random grouping at 6∶4. The SMOTE algorithm was used to expand the data on the training set. The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) algorithm were used to screen the radiomics features. A radiomics model was constructed using a logistic regression classifier. Independent sample t-test or Wilcoxon rank sum test was used to compare the two groups. Receiver operating characteristic (ROC) curves were used to evaluate the predictive efficacy of traditional imaging (ADC) and radiomics models on the efficacy of neoadjuvant chemotherapy for osteosarcoma. Results:A total of 10 and 31 cases were included in the good-efficacy and poor-efficacy groups, respectively. No statistically significant difference was found in the ADC0 value between the two groups ((0.95±0.05)×10 -3 mm 2/s vs. (1.05±0.05)×10 -3 mm 2/s, t=1.14, P>0.05)). The values of ADC1 and ADC2 in the good-efficacy group were higher than those in the poor-efficacy group, with statistical significance ((1.44±0.10)×10 -3 mm 2/s vs. (1.10±0.06)×10 -3 mm 2/s, t=-2.92, P<0.05; 1.68 (1.55, 1.85)×10 -3 mm 2/s vs. (1.33±0.06)×10 -3 mm 2/s, Z=-2.61, P<0.01). ROC curve analysis showed that when ADC1 ≥1.34×10 -3 mm 2/s, the sensitivity for evaluating the efficacy of neoadjuvant chemotherapy in osteosarcoma was 80%, the specificity was 81%, and the area under the curve (AUC) was 0.797 (95% CI: 0.629-0.965). When ADC2 ≥1.51×10 -3 mm 2/s, the sensitivity for evaluating the efficacy of neoadjuvant chemotherapy in osteosarcoma was 90%, the specificity was 71%, and the AUC was 0.777 (95% CI: 0.588–0.967). A total of 1 409 radiomics features were extracted from the DWI and ADC images after the end of the first stage of chemotherapy. They were randomly divided into training set and validation set at a ratio of 6∶4 (24 (good efficacy: 6, poor efficacy: 18)∶17 (good efficacy: 4, poor efficacy: 13)). The training set data were expanded to 70 (good efficacy: 20, poor efficacy: 50). After the radiomics features were screened, five optimal radiomics features were ultimately obtained, including InterquartileRange, Skewness, Uniformity, Median, and Maximum. Logistic regression classifier was used to construct a radiomics model. The ROC curves showed that in the training set, the AUC of the model for predicting the efficacy of neoadjuvant chemotherapy in osteosarcoma was 0.881 (95% CI: 0.811-0.942), with sensitivity of 90% and specificity of 74%. Meanwhile, in the validation set, the AUC was 0.769 (95% CI: 0.515–0.933), with sensitivity of 75% and specificity of 69%. Conclusion:The radiomics model based on MRI DWI outperforms the traditional imaging (ADC) in evaluating the efficacy of neoadjuvant chemotherapy for osteosarcoma, showing great potential in clinical applications.

目的 基于磁共振T2 mapping技术分析膝骨性关节炎(knee osteoarthritis,KOA)患者膝关节周围肌肉T2值改变,及其与KOA严重程度的相关性.材料与方法 本研究前瞻性招募38例KOA患者与16名健康志愿者进行膝关节MRI检查及周围肌肉T2 mapping序列扫描,比较两组间肌肉T2值的差异,同时分析各个肌肉T2值与KOA严重程度的相关性.基于KOA患者膝关节MRI图像,采用膝关节全器官磁共振评分(Whole-Organ Magnetic Resonance Imaging Score,WORMS)评估KOA严重程度,WORMS得分越高,反映结构损伤的严重程度越高.分析各个肌肉T2值与膝关节WORMS相关性;同时关节软骨、骨髓下水肿评分与各肌肉T2值进行相关性分析.结果 KOA患者膝关节周围肌肉T2值均高于健康志愿者.KOA患者的缝匠肌、股内侧肌、腓肠肌内侧头T2值与WORMS总评分(r=0.678、0.674、0.466,P均＜0.05)、关节软骨评分(r=0.590、0.672、0.424,P均＜0.05)均呈正相关;缝匠肌、股内侧肌T2值与关节面下骨髓水肿呈正相关(r=0.527、0.538,P均＜0.05);半膜肌T2值与关节软骨评分呈正相关(r=0.347,P＜0.05).结论 磁共振T2 mapping技术可以敏感、定量评估KOA患者膝关节周围肌肉改变,随着KOA严重程度的增加,部分肌肉T2值升高.骨骼肌T2值可作为反映KOA患者骨骼肌内部结构、成分改变的敏感指标.

目的 分析骨小细胞恶性肿瘤(SCMT)的临床及影像表现,以提高临床诊断水平.方法 回顾性选取经病理证实的骨SCMT病人75例,其中恶性非霍奇金淋巴瘤(MNHL)25例[男20例、女5例,平均年龄(50.0±18.3)岁],骨的浆细胞瘤(PB)37例[男20例、女17例,平均年龄(59.1±12.4)岁]、尤文肉瘤(ES)13例[男11例、女2例,平均年龄(17.1±6.7)岁].分析3种肿瘤病人临床资料、病灶X线摄影或CT特征及MRI特征.采用单因素方差分析、卡方检验或Fisher确切概率检验比较3组病人的临床资料和影像特征分布.结果 3组间年龄、性别、骨质破坏类型、骨皮质破坏类型、膨胀性改变和残留骨嵴、病灶周围水肿、软组织肿块、骨膜反应差异均有统计学意义(均P<0.05).MNHL组及PB组病人的平均年龄均高于ES组,3组男性均多于女性.3组X线及CT特征中,PB组中骨质破坏、骨皮质破坏、膨胀性改变及病变内残留骨嵴征象的占比均高于其余2组;其中PB组病人均可见骨质破坏和骨皮质破坏(100％),且骨质破坏多呈溶骨性改变(90.6％),并以骨皮质缺损多见(84.4％).3组MRI特征中,ES组出现病灶周围水肿、软组织肿块和骨膜反应的占比均最高,其次为MNH组和PB组.ES组出现软组织肿块的占比高达100％,MNHL组和PB组中出现骨膜反应者仅占5.6％和4.8％.3组发生部位、T2WI信号特点间的差异均无统计学意义(均P>0.05).结论 结合病人的年龄及影像学表现有助于鉴别骨SCMT.

Background The cartilage segmentation algorithms make it possible to accurately evaluate the morphology and degeneration of cartilage. There are some factors (location of cartilage subregions, hydrarthrosis and cartilage degeneration) that may influence the accuracy of segmentation. It is valuable to evaluate and compare the accuracy and clinical value of volume and mean T2* values generated directly from automatic knee cartilage segmentation with those from manually corrected results using prototype software. Method Thirty-two volunteers were recruited, all of whom underwent right knee magnetic resonance imaging examinations. Morphological images were obtained using a three-dimensional (3D) high-resolution Double-Echo in Steady-State (DESS) sequence, and biochemical images were obtained using a two-dimensional T2* mapping sequence. Cartilage score criteria ranged from 0 to 2 and were obtained using the Whole-Organ Magnetic Resonance Imaging Score (WORMS). The femoral, patellar, and tibial cartilages were automatically segmented and divided into subregions using the post-processing prototype software. Afterwards, all the subregions were carefully checked and manual corrections were done where needed. The dice coefficient correlations for each subregion by the automatic segmentation were calculated. Results Cartilage volume after applying the manual correction was significantly lower than automatic segmentation ( P < 0.05). The percentages of the cartilage volume change for each subregion after manual correction were all smaller than 5%. In all the subregions, the mean T2* relaxation time within manual corrected subregions was significantly lower than in regions after automatic segmentation ( P < 0.05). The average time for the automatic segmentation of the whole knee was around 6 min, while the average time for manual correction of the whole knee was around 27 min. Conclusions Automatic segmentation of cartilage volume has a high dice coefficient correlation and it can provide accurate quantitative information about cartilage efficiently without individual bias. Advances in knowledge: Magnetic resonance imaging is the most promising method to detect structural changes in cartilage tissue. Unfortunately, due to the structure and morphology of the cartilages obtaining accurate segmentations can be problematic. There are some factors (location of cartilage subregions, hydrarthrosis and cartilage degeneration) that may influence segmentation accuracy. We therefore assessed the factors that influence segmentations error.

PurposeThe aim of this study is to compare the blood oxygen level–dependent (BOLD) fluctuation power in 96 frequency points ranging from 0 to 0.25 Hz between benign and malignant musculoskeletal (MSK) tumors via power spectrum analyses using functional magnetic resonance imaging (fMRI).Materials and methodsBOLD-fMRI and T1-weighted imaging (T1WI) of 92 patients with benign or malignant MSK tumors were acquired by 1.5-T magnetic resonance scanner. For each patient, the tumor-related BOLD time series were extracted, and then, the power spectrum of BOLD time series was calculated and was then divided into 96 frequency points. A two-sample t-test was used to assess whether there was a significant difference in the powers (the “power” is the square of the BOLD fluctuation amplitude with arbitrary unit) of each frequency point between benign and malignant MSK tumors. The receiver operator characteristic (ROC) analysis was used to assess the diagnostic capability of distinguishing between benign and malignant MSK tumors.ResultsThe result of the two-sample t-test showed that there was significant difference in the power between benign and malignant MSK tumor at frequency points of 58 (0.1508 Hz, P = 0.036), 59 (0.1534 Hz, P = 0.032), and 95 (0.247 Hz, P = 0.014), respectively. The ROC analysis of mean power of three frequency points showed that the area of under curve is 0.706 (P = 0.009), and the cutoff value is 0.73130. If the power of the tumor greater than or equal to 0.73130 is considered the possibility of benign tumor, then the diagnostic sensitivity and specificity values are 83% and 59%, respectively. The post hoc analysis showed that the merged power of 0.1508 and 0.1534 Hz in benign MSK tumors was significantly higher than that in malignant ones (P = 0.014). The ROC analysis showed that, if the benign MSK tumor was diagnosed with the power greater than or equal to the cutoff value of 1.41241, then the sensitivity and specificity were 67% and 68%, respectively.ConclusionThe mean power of three frequency points at 0.1508, 0.1534, and 0.247 Hz may potentially be a biomarker to differentiate benign from malignant MSK tumors. By combining the power of 0.1508 and 0.1534 Hz, we could better detect the difference between benign and malignant MSK tumors with higher specificity.

本文报道1例青年女性腕钩状骨原发性透明细胞肉瘤，患者无诱因腕部压痛，影像提示腕钩状骨占位，病理及免疫组化表现符合透明细胞肉瘤及恶性黑色素瘤。给予手术切除，预后良好。原发于骨骼的透明细胞肉瘤罕见，未见发生于腕骨的报道。该例患者的诊断过程提示对于黑色素小体存在的骨内占位，要考虑到透明细胞肉瘤及恶性黑色素瘤的诊断，确诊依靠病理及细胞遗传学检查。治疗方式以手术切除为主。

Objective: To study the effect of long-distance running on the morphological and T2* assessment of knee cartilage. Methods: 3D-DESS and T2* mapping was performed in 12 amateur marathon runners (age: between 21 and 37 years) without obvious morphological cartilage damage. MRI was performed three times: within 24 h before the marathon, within 12 h after the marathon, and after a period of convalescence of two months. An automatic cartilage segmentation method was used to quantitatively assessed the morphological and T2* of knee cartilage pre-and post-marathon. The cartilage thickness, volume, and T2* values of 21 sub-regions were quantitatively assessed, respectively. Results: The femoral lateral central (FLC) cartilage thickness was increased when 12-h post-marathon compared with pre-marathon. The tibial medial anterior (TMA) cartilage thickness was decreased when 2 months post-marathon compared with pre-marathon. The tibial lateral posterior (TLP) cartilage volume was increased when 12-h post-marathon compared with pre-marathon. The cartilage T2* value in most sub-regions had the upward trend when 12-h post-marathon and restored trend when 2 months post-marathon, compared with pre-marathon. The femoral lateral anterior (FLA) and TMA cartilage volumes were decreased 2 months post-marathon compared with pre-marathon. Conclusions: The marathon had some effects on the thickness, volume, and T2* value of the knee cartilages. The thickness and volume of knee cartilage in most sub-regions were without significantly changes post-marathon compared with pre-marathon. T2* value of knee cartilage in most sub-regions was increased right after marathon and recovered 2 months later. The TLP and TMA subregions needed follow-up after marathon. Advances in knowledge: The morphological and T2* changes of knee cartilage after marathon were evaluated by MRI and automatic segmentation software. This study was the first to use cartilage automatic segmentation software to evaluate the effects of marathon on the morphology and biochemical components of articular cartilage, and to predict the most vulnerable articular cartilage subregions, for the convenience of future exercise adjustment and the avoidance of sports cartilage injury.

目的:对比分析发生于不同部位的骨原发性非霍奇金淋巴瘤(PNLB)的影像表现.方法:搜集经手术病理及免疫组化证实的25例PNLB患者,按照病变发生部位分为四肢骨组(13例)和躯干骨组(12例).对两组的患者发病年龄、骨质破坏类型、软组织肿块大小及包绕骨病变形式、MRI信号特点进行分析.结果:四肢骨组的患者发病年龄(中位年龄44岁)较躯干骨组(中位年龄54岁)年轻.在四肢骨组中,以单发病变多见(77％),骨质破坏以溶骨性和混合性破坏为主(各占46％,6/13);在躯干骨组中,骨质破坏类型以溶骨性为主(50％),两组的骨质破坏类型差异无统计学意义(P>0.05).两组中以出现软组织肿块多见(72％),但四肢骨组中软组织肿块偏大,呈环绕型生长.MRI信号特点无特异性.结论:四肢骨组中,患者发病年龄相对年轻,骨质破坏以溶骨性和混合性破坏为主,单发病变多见,周围软组织肿块较躯干骨组大,且呈环周包绕性生长.躯干骨组中,患者发病年龄相对较大,骨质破坏类型以溶骨性为主,周围软组织肿块相对偏小,但也呈环绕型生长.

Purpose: Diffuse hyperintensities of the bone marrow in whole-body diffusion-weighted (DW) imaging (DWI) have been encountered more frequently in females aged 21-50 compared to elder females or men. Therefore, we aimed to visually evaluate DWI among pre-, peri- and postmenopausal women and to verify whether it correlates also quantitatively with hormonal status. Method: The prospective study was approved by our institutional review board and informed consent was obtained in a total of 70 healthy premenopausal, perimenopausal, and postmenopausal women aged 40-58 years from February 2017 to October 2017. The bone marrow DW imaging signal characteristics were visually evaluated in comparison to the erector spinae muscle. Imaging data were acquired using a 1.5 T MRI yielding signal intensity values from a DWI-pulse sequence (b-value of 800 s/mm2; apparent diffusion coefficient (ADC) maps from b-values of 0-800 s/mm2), and a T2 mapping sequence covering the L2-L4 lumbar vertebrae. Serous estradiol (E2), follicle stimulating hormone (FSH), and luteinizing hormone (LH) were measured through venous blood assay. The relationship of the mean DW signal intensity (SIDWI) with T2 values, female hormone level, and mean ADC were analyzed using Spearman's rho test. Results: The proportion of diffuse DWI hyperintensities of the bone marrow was significantly higher in premenopausal (91% (21/23)) women compared to peri- (75% (18/24)) and postmenopausal (8% (2/23)) women. A positive correlation was observed for the mean SIDWI (median [interquartile range], 47.33 [30.14]) and mean T2 (mean +/- SD, 121.01 +/- 13.54) (r = 0.438, p < 0.001) as well as for the mean SIDWI and E2 (median [interquartile range], 52.45 [92.78]) (r = 0.407, p < 0.001). A negative correlation was observed for the mean SIDWI and serous FSH (median [interquartile range], 15.55 [42.08]) as well as for the mean SIDWI and serous LH (median [interquartile range], 6.96 [31.06]) (r = - 0.557, p < 0.001; r = -0.535, p < 0.001; respectively), but no significant correlation was found for mean SIDWI and mean ADC (mean +/- SD, 599.36 +/- 82.70) (r = 0.099, p = 0.415). A negative correlation was also encountered for the mean T2 values and serous FSH (r = -0.339, p = 0.004) as well as for the mean T2 values and serous LH (r = -0.281, p = 0.018). Conclusions: The mean SIDWI correlates positively with mean T2 and serous E2 values, while there's no significant correlation with mean ADC, indicating that T2 shine-through effects might interfere with bone marrow signaling on DW images. Knowledge of the bone marrow signal characteristics changing in DW images in close relationship with menstrual status is essential to correctly interpret DWI in clinical practice.

Background: The outbreak of COVID-19 was an unprecedented health emergency, which affected everyone, including the medical students. We aimed to investigate the influence of COVID-19 on professional identity and career planning of clinical medical undergraduates, and propose strategies. Method: A cross-section survey was conducted via online questionnaire from January to March 2021 on clinical medical undergraduates in Hebei province. We collected the demographic information, the understanding status of the epidemic, the change of the attitude to professional identity and career planning after the epidemic. McNemar Tests were used to evaluate corresponding information. Findings: In 2754 respondents, over 80% students were aware about the transmission, incubation period, source, and first consultation department of COVID-19; however, the number of students knowing the name of the pathogen and therapy of this disease relatively fewer. Overall, the pandemic had a positive impact on their professional identity and career planning (all P <0·05, compared with the status before the epidemic). And the number of students who chose the department of infectious disease and respiration as the favorite after COVID-19 increased (184 (6·68%), vs 99 (3·59%), P <0·001). The number of students being willing to choose the above two departments after COVID-19 increased (956 (34·72%), vs 773 (28·07%), P <0·001). Interpretation: Overall, the pandemic had a positive impact on the professional identity and career planning of the students, but the number of students being willing to choose the department of infectious disease and respiration was still extremely low. In the future education, further attention should be paid to the cultivation of interest in above departments and the spirit of dedication.Funding Statement: None.Declaration of Interests: All authors declare that there are no conflicts of interest.Ethics Approval Statement: The study was approved by the medical ethics committee of the Third Hospital of Hebei Medical University. (Number: K 2021 -002-1)