BACKGROUND:Accurate segmentation of pelvic and sacral tumors (PSTs) in multi-sequence magnetic resonance imaging (MRI) is essential for effective treatment and surgical planning. PURPOSE:To develop a deep learning (DL) framework for efficient segmentation of PSTs from multi-sequence MRI. MATERIALS AND METHODS:This study included a total of 616 patients with pathologically confirmed PSTs between April 2011 to May 2022. We proposed a practical DL framework that integrates a 2.5D U-net and MobileNetV2 for automatic PST segmentation with a fast annotation strategy across multiple MRI sequences, including T1-weighted (T1-w), T2-weighted (T2-w), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted (CET1-w). Two distinct models, the All-sequence segmentation model and the T2-fusion segmentation model, were developed. During the implementation of our DL models, all regions of interest (ROIs) in the training set were coarse labeled, and ROIs in the test set were fine labeled. Dice score and intersection over union (IoU) were used to evaluate model performance. RESULTS:The 2.5D MobileNetV2 architecture demonstrated improved segmentation performance compared to 2D and 3D U-Net models, with a Dice score of 0.741 and an IoU of 0.615. The All-sequence model, which was trained using a fusion of four MRI sequences (T1-w, CET1-w, T2-w, and DWI), exhibited superior performance with Dice scores of 0.659 for T1-w, 0.763 for CET1-w, 0.819 for T2-w, and 0.723 for DWI as inputs. In contrast, the T2-fusion segmentation model, which used T2-w and CET1-w sequences as inputs, achieved a Dice score of 0.833 and an IoU value of 0.719. CONCLUSIONS:In this study, we developed a practical DL framework for PST segmentation via multi-sequence MRI, which reduces the dependence on data annotation. These models offer solutions for various clinical scenarios and have significant potential for wide-ranging applications.

To develop a nomogram based on CT and clinical features to predict R0 resection in patients with stage IIB–IV epithelial ovarian cancer (EOC). 209 patients with stage IIB–IV EOC from three independent medical institutions were stratified into training cohort (from institutions 1 and 2, n = 144) and independent validation cohort (from institution 3, n = 65). Univariate and multivariate logistic analyses of CT and clinical features obtained within two weeks before debulking surgery were used to determine the independent predictors of R0 resection in the training cohort. Nomogram was developed based on the predictors. Receiver operating characteristic (ROC) curves and calibration curves were performed to evaluate the predictive performance of the nomogram. R0 resection was achieved in 66.00 and 61.50

This study developed an end-to-end deep learning (DL) model using non-enhanced MRI to diagnose benign and malignant pelvic and sacral tumors (PSTs). Retrospective data from 835 patients across four hospitals were employed to train, validate, and test the models. Six diagnostic models with varied input sources were compared. Performance (AUC, accuracy/ACC) and reading times of three radiologists were compared. The proposed Model SEG-CL-NC achieved AUC/ACC of 0.823/0.776 (Internal Test Set 1) and 0.836/0.781 (Internal Test Set 2). In External Dataset Centers 2, 3, and 4, its ACC was 0.714, 0.740, and 0.756, comparable to contrast-enhanced models and radiologists (P > 0.05), while its diagnosis time was significantly shorter than radiologists (P < 0.01). Our results suggested that the proposed Model SEG-CL-NC could achieve comparable performance to contrast-enhanced models and radiologists in diagnosing benign and malignant PSTs, offering an accurate, efficient, and cost-effective tool for clinical practice.

This study aims to develop an end-to-end deep learning (DL) model to predict neoadjuvant chemotherapy (NACT) response in osteosarcoma (OS) patients using routine magnetic resonance imaging (MRI). We retrospectively analyzed data from 112 patients with histologically confirmed OS who underwent NACT prior to surgery. Multi-sequence MRI data (including T2-weighted and contrast-enhanced T1-weighted images) and physician annotations were utilized to construct an end-to-end DL model. The model integrates ResUNet for automatic tumor segmentation and 3D-ResNet-18 for predicting NACT efficacy. Model performance was assessed using area under the curve (AUC) and accuracy (ACC). Among the 112 patients, 51 exhibited a good NACT response, while 61 showed a poor response. No statistically significant differences were found in age, sex, alkaline phosphatase levels, tumor size, or location between these groups (P > 0.05). The ResUNet model achieved robust performance, with an average Dice coefficient of 0.579 and average Intersection over Union (IoU) of 0.463. The T2-weighted 3D-ResNet-18 classification model demonstrated superior performance in the test set with an AUC of 0.902 (95

Background: This study aimed to explore optimal computed tomography (CT)-based machine learning and deep learning methods for the identification of pelvic and sacral osteosarcomas (OS) and Ewing's sarcomas (ES). Methods: A total of 185 patients with pathologically confirmed pelvic and sacral OS and ES were analyzed. We first compared the performance of 9 radiomics-based machine learning models, 1 radiomics- based convolutional neural networks (CNNs) model, and 1 3-dimensional (3D) CNN model, respectively. We then proposed a 2-step no-new-Net (nnU-Net) model for the automatic segmentation and identification of OS and ES. The diagnoses by 3 radiologists were also obtained. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate the different models. Results: Age, tumor size, and tumor location showed significant differences between OS and ES (P<0.01). For the radiomics-based machine learning models, logistic regression (LR; AUC =0.716, ACC =0.660) performed best in the validation set. However, the radiomics-based CNN model had an AUC of 0.812 and ACC of 0.774 in the validation set, which were higher than those of the 3D CNN model (AUC =0.709, ACC = 0.717). Among all the models, the nnU-Net model performed best, with an AUC of 0.835 and an ACC of 0.830 in the validation set, which was significantly higher than the primary physician's diagnosis (ACCs ranged from 0.757 to 0.811) (P<0.01). Conclusions: The proposed nnU-Net model could be an end-to-end, non-invasive, and accurate auxiliary diagnostic tool for the differentiation of pelvic and sacral OS and ES.

PURPOSE:To assess the performance of random forest (RF)-based radiomics approaches based on 3D computed tomography (CT) and clinical features to predict the types of pelvic and sacral tumors.MATERIALS AND METHODS:A total of 795 patients with pathologically confirmed pelvic and sacral tumors were analyzed, including metastatic tumors (n = 181), chordomas (n = 85), giant cell tumors (n =120), chondrosarcoma (n = 127), osteosarcoma (n = 106), neurogenic tumors (n = 95), and Ewing's sarcoma (n = 81). After semi-automatic segmentation, 1316 hand-crafted radiomics features of each patient were extracted. Four radiomics models (RMs) and four clinical-RMs were built to identify these seven types of tumors. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models.RESULTS:In total, 795 patients (432 males, 363 females; mean age of 42.1 ± 17.8 years) were consisted of 215 benign tumors and 580 malignant tumors. The sex, age, history of malignancy and tumor location had significant differences between benign and malignant tumors (P < 0.05). For the two-class models, clinical-RM2 (AUC = 0.928, ACC = 0.877) performed better than clinical-RM1 (AUC = 0.899, ACC = 0.854). For the three-class models, the proposed clinical-RM3 achieved AUCs between 0.923 (for chordoma) and 0.964 (for sarcoma), while the AUCs of the clinical-RM4 ranged from 0.799 (for osteosarcoma) to 0.869 (for chondrosarcoma) in the validation set.CONCLUSIONS:The RF-based clinical-radiomics models provided high discriminatory performance in predicting pelvic and sacral tumor types, which could be used for clinical decision-making.

PURPOSE:To assess the performance of deep neural network (DNN) and machine learning based radiomics on 3D computed tomography (CT) and clinical characteristics to predict benign or malignant sacral tumors. MATERIALS AND METHODS:This single-center retrospective analysis included 459 patients with pathologically proven sacral tumors. After semi-automatic segmentation, 1,316 hand-crafted radiomics features of each patient were extracted. All models were built on training set (321 patients) and tested on validation set (138 patients). A DNN model and four machine learning classifiers (logistic regression [LR], random forest [RF], support vector machine [SVM] and k-nearest neighbor [KNN]) based on CT features and clinical characteristics were built, respectively. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. RESULTS:In total, 459 patients (255 males, 204 females; mean age of 42.1 ± 17.8 years, range 4-82 years) were enrolled in this study, including 206 cases of benign tumor and 253 cases of malignant tumor. The sex, age and tumor size had significant differences between the benign tumors and malignant tumors (χ2 sex = 10.854, Zage = -6.616, Zsize = 2.843, P < 0.05). The radscore, sex, and age were important indicators for differentiating benign and malignant sacral tumors (odds ratio [OR]1 = 2.492, OR2 = 2.236, OR3 = 1.037, P < 0.01). Among the four clinical-radiomics models (RMs), clinical-LR had the best performance in the validation set (AUC = 0.84, ACC = 0.81). The clinical-DNN model also achieved a high performance (an AUC of 0.83 and an ACC of 0.76 in the validation set) in identifying benign and malignant sacral tumors. CONCLUSIONS:Both the clinical-LR and clinical-DNN models would have a high impact on assisting radiologists in their clinical diagnosis of sacral tumors.

OBJECTIVE:To retrospectively explore correlation of the resected specimen volume of breast microcalcification lesions and endogenous and exogenous factors of stereotactic needle localization biopsy (SNLB). MATERIALS AND METHODS:Totally 214 patients underwent SNLB for non-palpable breast lesion with microcalcification lesions. Of 211 patients, 198 patients underwent single needle localization and 13 patients underwent multi-needle localization (26 lesions). Lesion sizes, distribution characteristics, lesion localization accuracy and resected specimen volumes were recorded and analyzed using a generalized linear model (GLM). RESULTS:The average lesion diameter is 2.63±1.73 cm. The localization accuracy of 187 lesions were moderate, 26 were too deep and 11 were too superficial. The mean resected specimen volume (V) was 17.51±5.14 cm3. One-way ANOVA analysis showed that 3 factors, including lesion sizes, distribution characteristics and the localization accuracy were associated with resected specimen volume (F = 67.56–112.78, P < 0.001). GLM revealed that lesion sizes, single clustered distribution and accurate localization were significant factors for resected specimen volume (F = –4.82–11.36, P < 0.05). The ratio (%) of the resected specimen volume to the involved breast volume (V0) was defined as the degree of breast defect. The mean breast defect of 125 benign patients (V/V0) was 27.5% ranging from 10.1% to 42.3%. CONCLUSION:Average lesion diameter and localization accuracy are highly significant variables for the resected specimen volume. Localization accuracy as a subjective controllable variable is one of the important factors that determine the volume of lesion resection. Single clustered distribution was more susceptible localization accuracy than other characteristic distributions. Improving localization accuracy can reduce resected specimen volume, which can reduce breast defect to a certain extent.

BackgroundChondrosarcoma (CS) is the second most common primary malignant bone tumor, with a relatively high recurrence rate. However, an effective method that estimates whether pelvic CS will recur after surgery, which influences the formulation of a clinical treatment plan, remains lacking.PurposeTo develop and validate a clinical radiomics nomograms based on 3D multiparametric magnetic resonance imaging (mpMRI) features and clinical characteristics that could estimate early recurrence (ER) (≤1 year) of pelvic CS.Study TypeRetrospective.PopulationIn all, 103 patients (ER = 41, non‐ER = 62) with histologically proven CS were retrospectively analyzed and divided into a training set (n = 72) and a validation set (n = 31).Field Strength/Sequence3.0T axial T1‐weighted (T1‐w), T2‐weighted (T2‐w), diffusion weighted imaging (DWI), contrast‐enhanced T1‐weighted (CET1‐w).AssessmentRisk factors (sex, age, type, grade, resection margins, etc.) associated with ER were evaluated. Five individual models based on T1‐w, T2‐w, DWI, CET1‐w, and clinical data were built. Then we compared the performance of models based on T1‐w, T2‐w, CET1‐w and their combination. Lastly, two nomograms based on the best model + clinical data and DWI + clinical data were built.Statistical TestsThe area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models.ResultsGrade was the most important univariate clinical predictor of ER of pelvic CS patients (odds ratio [OR]1 = 4.616, OR2 = 8.939, P < 0.05). T1‐w + T2‐w + CET1‐w had a significantly higher performance than CET1‐w in the training set (P = 0.01). Radiomics features are more important than clinical characteristics in clinical radiomics nomograms, especially for multisequence combined features (OR = 3.208, P < 0.01). Clinical radiomics nomogram based on combined features (T1‐w + T2‐w + CET1‐w) + clinical data achieved an AUC of 0.891 and ACC of 0.857, followed by DWI + clinical data (AUC = 0.882, ACC = 0.760) in the validation set.Data ConclusionThe clinical radiomics nomogram had good performance in estimating ER of pelvic CS patients, which would be helpful in clinical decision‐making.Level of Evidence: 4Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2020;51:435–445.

目的 评价两种表面防护材料在CT扫描野内的辐射防护效果和扫描野的图像质量.方法 使用CTDI模体测量颈部扫描时周边的辐射量及CTDIw,采用铋屏蔽和硫酸钙屏蔽为辐射防护材料的辐射量及CTDIw,比较有无屏蔽以及两种屏蔽的防护效果差异.低剂量CT筛查60例,随机分为3组,分别为无屏蔽组以及使用铋屏蔽和硫酸钙屏蔽防护组,自动曝光控制扫描,管电压100 kV,参考管电流为23 mAs.对患者甲状腺和肺尖层面的图像质量评分,比较屏蔽对图像质量的影响.结果模型研究中,无屏蔽组的探测点辐射量为6.17±0.21 mGy;小于床板侧(7.61 ±0.14) mGy和中心侧为(7.14 ±0.11)mGy(P ＜0.01);而屏蔽组CTDIw均小于无屏蔽组,且铋屏蔽组和硫酸钙屏蔽组与无屏蔽组CTDIw分别为5.78、5.02、6.92 mGy (P＜ 0.05).临床研究中,3组低剂量筛查的有效剂量分别为(0.92±0.05)、(0.90±0.06)、(0.90 ±0.05)mSv(P ＞0.05).3组甲状腺和肺尖成像质量均可满足诊断要求,且屏蔽组的甲状腺成像质量评分明显低于无屏蔽组(F=14.69,P＜0.01),其中硫酸钙屏蔽组评分最低;3组肺尖图像质量评分间差异没有统计学意义(F=1.57,P＞0.05).结论 铋屏蔽与硫酸钙屏蔽均可以作为肺癌CT筛查的甲状腺表面屏蔽使用,不影响肺组织诊断.

To identify the magnetic resonance imaging (MRI) features of hands and wrists in early rheumatoid arthritis (RA). A total of 129 early arthritis patients (≤1 year) were enrolled in the study. At presentation, MRI of the hands was performed, with clinical and laboratory analyses. After a 1-year follow-up, clinical diagnosis of early RA or non-RA was confirmed by two rheumatologists. The characteristics of MRI variables at baseline in RA patients not fulfilling ACR 1987 criteria [RA-87(−)] were compared with those fulfilling ACR1987 criteria [RA-87(+)] and non-RA. In the 129 early arthritis patients, 90 were diagnosed with RA in a 1-year follow-up. There were 47.8 % (43/90) of the RA patients not fulfilling ACR 1987 criteria [RA-87(−)]. The scores of synovitis in RA-87(−) patients were similar with those in RA-87(+) [Synovitis score, 14.0 (IQR, 4.0–25.0) vs. 14.0 (IQR, 10.0–25.0), p > 0.05]. Compared with those in non-RA, RA-87(−) patients had higher synovitis scores and occurrence of synovitis in proximal interphalangeal (PIP) joints [synovitis score, 14.0 (IQR, 4.0–25.0) vs. 6.0 (IQR, 2.0–14.5), p = 0.046; occurrence of PIP synovitis: 53.5 vs. 27.3 %, p = 0.02]. There was no significant difference of bone marrow edema, bone erosion, and tenosynovitis between RA-87(−) and non-RA. Synovitis in PIP joints was independent predictor for RA-87(−) [OR, 3.1 (95 %CI 1.2–8.1)]. High synovitis scores and synovitis in PIP joints on MRI were important in early RA, especially those not fulfilling ACR 1987 criteria.

ObjectiveLeukocyte immunoglobulin‐like receptor A3 belongs to a family of receptors with inhibitory or activating functions. Since Caucasian individuals lacking LILRA3 have been found to be susceptible to multiple sclerosis and Sjögren's syndrome, we undertook this study to examine whether LILRA3 deletion is a novel genetic risk factor for rheumatoid arthritis (RA) (another autoimmune disease), whether there are sex‐specific effects, and whether LILRA3 influences the subtype and severity of RA.MethodsThe LILRA3 deletion and its tagging single‐nucleotide polymorphism rs103294 were genotyped in a Northern Han Chinese cohort (N‐Han) (1,618 cases and 1,658 controls) and a Southern Han Chinese cohort (S‐Han) (575 cases and 549 controls). Association analyses were performed on the complete data set and subsets. The effect of the nondeleted (functional) LILRA3 allele on radiographic severity and LILRA3 expression was evaluated.ResultsIn the N‐Han discovery cohort, we unexpectedly observed a higher frequency of the functional LILRA3 in RA patients compared with healthy individuals (10.1% versus 6.3%; P = 4.01 × 10−5, odds ratio [OR] 1.92). The association was replicated in the S‐Han cohort and confirmed by meta‐analysis (P = 5.63 × 10−6, OR 1.83). Functional LILRA3 conferred greater risk for RA in males (P = 1.09 × 10−6, OR 4.47), and was specifically associated with anti–citrullinated protein antibody (ACPA)–positive RA (P = 3.05 × 10−4, OR 1.75). Furthermore, functional LILRA3 was associated with higher radiographic scores in ACPA‐positive patients with early RA (P = 9.70 × 10−3) and higher LILRA3 messenger RNA levels (P = 3.31 × 10−8).ConclusionOur study provides the first evidence that functional LILRA3 is a novel genetic risk factor for RA, especially in males. It appears to highly predispose to ACPA‐positive RA and confers an increased risk of disease severity in patients with early RA.

The purpose of this study was to investigate the feasibility of diffusion-weighted imaging (DWI) in detecting synovitis of wrist and hand in patients with rheumatoid arthritis (RA) and evaluate its sensitivity, specificity and accuracy as compared to T2-weighted imaging (T2WI) with short tau inversion recovery (STIR) with the reference standard contrast-enhanced magnetic resonance imaging (CE-MRI). Twenty-five patients with RA underwent MR examinations including DWI, T2WI with STIR and CE-MRI. MR images were reviewed for the presence and location of synovitis of wrist and hand. The sensitivity, specificity and accuracy of DWI and T2WI with STIR were calculated respectively and then compared. All patients included in this study completed MR examinations and yielded diagnostic image quality of DWI. For individual joint, there was good to excellent inter-observer agreement (k = 0.62–0.83) using DWI images, T2WI with STIR images and CE-MR images, respectively. There was a significance between DWI and T2WI with STIR in analyzing proximal interphalangeal joints II–V, respectively (P < 0.05). The k-values for the detection of synovitis indicated excellent overall inter-observer agreements using DWI images (k = 0.86), T2WI with STIR images (k = 0.85) and CE-MR images (k = 0.91), respectively. Overall, DWI demonstrated a sensitivity, specificity and accuracy of 75.6%, 89.3% and 84.6%, respectively, for detection of synovitis, while 43.0%, 95.7% and 77.6% for T2WI with STIR, respectively. DWI showed positive lesions much better and more than T2WI with STIR. Our results indicate that DWI presents a novel non-invasive approach to contrast-free imaging of synovitis. It may play a role as an addition to standard protocols.

PURPOSE:To evaluate the diagnostic performance of three-dimensional (3D) MR maximum intensity projection (MIP) in the assessment of synovitis of the hand and wrist in rheumatoid arthritis (RA) compared to 3D contrast-enhanced magnetic resonance imaging (CE-MRI). MATERIALS AND METHODS:Twenty-five patients with RA underwent MR examinations. 3D MR MIP images were derived from the enhanced images. MR images were reviewed by two radiologists for the presence and location of synovitis of the hand and wrist. The diagnostic sensitivity, specificity and accuracy of 3D MIP were, respectively, calculated with the reference standard 3D CE-MRI. RESULTS:In all subjects, 3D MIP images yielded directly and clearly the presence and location of synovitis with just one image. Synovitis demonstrated high signal intensity on MIP images. The k-values for the detection of articular synovitis indicated excellent interobserver agreements using 3D MIP images (k=0.87) and CE-MR images (k=0.91), respectively. 3D MIP demonstrated a sensitivity, specificity and accuracy of 91.07%, 98.57% and 96.0%, respectively, for the detection of synonitis. CONCLUSION:3D MIP can provide a whole overview of lesion locations and a reliable diagnostic performance in the assessment of articular synovitis of the hand and wrist in patients with RA, which has potential value of clinical practice.

Objective:To assess and measure the double-bundle structure of anterior cruciate ligament(ACL) using 3D Cube T2 weighted Magnetic Resonance(MR) imaging.Methods:Nineteen healthy volunteers with intact ACLs underwent anatomic MR acquisition including 3D Cube T2 weighed imaging.The anatomic characteristics of the anteromedial bundle(AMB) and the posterolateral bundle(PLB) were assessed.The lengths,course angles of the ligaments,and the widths of the femoral and tibial attachments were measured and compared using Mann-Whitney test.The factors associated with differences in measurements were analyzed by Logistic regression test.Results:The median lengths of AMBs and PLBs were 31.01 and 25.38 mm;widths of the femoral attachment were 10.6 and 9.47 mm;widths of the tibial attachment were 11.28 and 8.49 mm;and the course angles were 72.01° and 64.97°.Regarding the AMBs and PLBs,the differences in the widths of femoral and tibial attachment and the course angles between male and female were indicated,however,a significance was not demonstrated(P0.05).The median length of ligaments of male was longer than that of female and a significance was seen(P0.05).The difference in the lengths of ligament had close association with height of human being(P0.05).Conclusion:The assessment and measurement of double bundle ACL can be performed on reconstructed 3D Cube T2WI MR imaging.The accurate measurement can be help to make a personal strategy before ACLs operation.

Objective:To investigate an ideal MR dynamic contrast-enhanced imaging sequence for bilateral hands and wrists by comparing liver acquisition with 3D volume acceleration(LAVA) sequence with 3D fast acquisition with multiphase enhanced fast GRE(FAME) sequence.Methods:Thirty-four patients with polyarthralgia including involvement of hands and wrists during their first visit had MR dynamic contrast-enhanced imaging performed.3D FAME sequence was used in 9 cases and 3D LAVA sequence in 25 cases.Image quality was evaluated with a 3-point system.Results:Of the 25 cases with 3D LAVA sequence,3 points were scored in 22 cases and 2 points were scored in 3 cases.Of the 9 cases with 3D FAME sequence,2 points were scored in 6 cases and 1 point was scored in 3 cases.3D LAVA sequence was superior to 3D FAME sequence in image quality by achieving more good images(P=0.014).Conclusion:The image quality of images achieved with 3D LAVA sequence was better than that of 3D FAME sequence.3D LAVA sequence might be an ideal MR dynamic contrast-enhanced imaging sequence for bilateral hands and wrists.

A total of 18 variably-presented gene clusters (LVPCs) and nine previously characterized variable-number tandem repeats (VNTRs), and all known virulence markers were screened for their frequency and/or copy number in 251 global strains of Vibrio parahaemolyticus using PCR and gel or capillary electrophoresis. A two-step genotyping approach combining the use of LVPCs and VNTRs was established accordingly. The frequency profiles of LVPCs and virulence markers were primarily used to group the strains into six distinct complexes with different potential pathogenicity natures. The strains from each of these complexes were further analyzed with VNTRs to give a much more detailed discrimination of the strains. A genetic fingerprint-like database of a large collection of strains established with this two-stage approach would be very useful for identification, genotyping, origin tracing, and risk estimation of V. parahaemolyticus.

Rationale and objective: The aim of this study is to develop a novel MR probe containing arginine-glycine-aspartic acid (RGD) motif for imaging integrin on alpha nu beta 3 receptor-expressed tumor.Materials and methods: Commercially available HYNIC-RGD conjugated with co-ligand EDDA was labeled with Gd3+, and the mixture was isolated and purified by solid phase extract (SPE) to get the entire probe Gd-EDDA/HYNIC-RGD. Human hepatocellular carcinoma (HHCC) cell line BEL-7402 was cultured and the cells harvested and suspended in serum-free Dulbecco's modified Eagle medium (DMEM) were subcutaneously inoculated into athymic nude mice for tumor growth. In vitro cell binding assay to integrin alpha nu beta 3 receptor and cell viability experiments were conducted. The in vivo imaging of the three arms of xenografts were performed by MR scan with a dedicated animal coil at time points of 0, 30, 60, 90 min and 24-h post-intravenous injection (p.i.). Three arms of nude mice then were sacrificed for histological examination to confirm the imaging results.Results: Gd-EDDA/HYNIC-RGD was successfully isolated by SPE and validity was verified on signal enhancement through in vitro and in vivo experiments. The nude mice model bearing HHCC was well established. There was approx. 30% signal enhancement on T1WI FSE images at 90 min post-intravenous injection of the Gd-EDDA/HYNIC-RGD compared with baseline, and the signal to time curve is straightforward over time in the span of 0-90 min p.i., while the control arms do not show this tendency.Conclusion: Gd-EDDA/HYNIC-RGD has the potential to serve as an MR probe detecting integrin alpha nu beta 3 receptor-expressed tumor. (C) 2008 Elsevier Ireland Ltd. All rights reserved.

Objective To investigatie incidene rate the and influencing factors of the focal high signal intensity in the splenium of the corpus callosum on FLAIR(fluid-attenuated inversion-recovery) images. Methods We reviewed the FLAIR and T2WI images of 250 patients for routine head MR examinations. All FLAIR images were evaluated for focal signal intensity lesions in the splenium and for leukoaraiosis. Results Among the 250 patients,focal high signal intensities in the splenium were found in 77 patients. The incidence rate was 30.8%. The incidence relatives to the age and leukoaraiosis,and the grade of the focal lesion was associated with the severity of leukoaraiosis. Conclusion High signal intensity in the splenium of the corpus callosum on FLAIR images is a common finding in aging people and may be accompanied with leukoaraiosis. The radiologists should be aware of the common finding and differ with other more commonly causes of splenial lesions.

OBJECTIVE To prepare magnetic resonance (MR) molecular probe for somatostain receptor expressed on breast cancer cell membranes and investigate its physico-chemical properties and imaging features in vitro. METHODS Molecular probe was prepared through superparamagnetic iron oxide (SPIO) conjugated to somatostatin analog-octreotide (OCT) using chemical method. Its features at different Fe(2+) concentrations were tested by MTT assay and Prussian blue staining respectively. Molecular probes at different Fe(2+) concentration and various numbers of cells labeled with the probe at Fe(2+) concentrations of 20 mg/L were scanned with 1.5 Tesla MR. Resovist was used in control group when labeling cells. RESULTS Various blue-staining particles were found in the cytoplasms of labeled cells with the molecular probes at different concentrations after Prussion blue staining and there were more particles with the increase of Fe(2+) concentration. The label rate of the probe was 96.15% which was higher than that in control group (80.00%). The bioactivity had no difference between labeled and non-labeled cells (P>0.05). There was remarkable low signal intensity on T(2)-weighed imaging and no evident artifacts for molecular probe when the concentration of Fe(2+) was 20 mg/L. The least number of labeled cells detected by MR in vitro was 6 x 10(6) when the concentration of Fe(2+) was 20 mg/L. CONCLUSION Molecular probe, SPIO-OCT, can effectively label breast cells which express SSTR. The reasonable Fe(2+) concentration of labeled cells and imaging was 20 mg/L. There is a correlation between MR signal intensity in vitro and the number of labeled cells.