To investigative potential clinicopathological characteristics and imaging-related risk factors of clinically significant prostate cancer (csPCa) undercategorized in patients with negative or equivocal MRI. This retrospective study included 581 patients with pathologically confirmed csPCa (Gleason score ≥ 3 + 4), including 108 undercategorized csPCa and 473 detected csPCa. All patients underwent multiparametric MRI (mpMRI). The undercategorized csPCa was defined as a MRI result with PI-RADS ≤ 3. The clinicopathological characteristics and imaging-related factors were compared between the undercategorized group(Group A) (PI-RADS 1–3) and detected group (Group B) (PI-RADS 4–5). The age, total PSA levels, PSAD, free PSA, prostate imaging quality (PI-QUAL) scores, and Gleason scores were significantly lower in the Group A than Group B. The lesions were larger and involved in peripheral and transition zones in the Group B. A significant difference in the second reading opinion. Age (odds ratio [OR], 0.94), PSAD (OR, 0.09), and PI-QUAL scores (OR, 0.25) were significantly associated with the undercategorized csPCa. The rate of undercategorized csPCa with these three risk factors (age, PSAD, and PI-QUAL scores of < 71, < 0.355, and < 3, respectively) was 68.62

Background:A new diffusion-weighted imaging (DWI) technique, known as zoomed-field-of-view echo-planar DWI (z-DWI), has been developed to reduce geometric distortions and susceptibility artifacts and to achieve higher spatial resolution. However, it remains unclear whether z-DWI, compared with the traditional DWI technique, can enhance the diagnostic performance of deep-learning-based computer-aided diagnosis (DL-CAD) and radiologists using DL-CAD in detecting prostate cancer (PCa). This study aims to evaluate and compare the diagnostic performance and PI-RADS scores of DL-CAD in detecting PCa using conventional full-field-of-view single-shot echo-planar DWI (f-DWI) and advanced z-DWI and to extend this comparison to clinical practice, in which radiologists use DL-CAD. Methods:This study retrospectively included magnetic resonance imaging from 359 patients for suspected PCa. There were 496 prostate lesions included, with 253 (51%) being malignant. Using a DL-CAD system, images of f-DWI and z-DWI were uploaded separately to obtain the localizations and the prostate imaging reporting and data system (PI-RADS) scores of suspected malignant lesions. The results were compared to histopathologic results. The diagnostic performance of f-DWI and z-DWI were evaluated using the free-response receiver operating characteristics and the alternative free-response receiver operating characteristics curves. Discrepancies in PI-RADS scores were analyzed. Additionally, two radiologists participated in consensus reading images by using DL-CAD with different DWI techniques, and their performance and PI-RADS scores were compared. Lastly, the relationship between PI-RADS discrepancies and clinically significant prostate cancer (csPCa) risk was analyzed. Results:z-DWI enabled DL-CAD to exhibit better diagnostic performance [area under the curve (AUC), 0.857 vs. 0.841; P=0.02], with a higher mean PI-RADS score for PCa lesions (4.26 vs. 3.92; P<0.001), and improved scores for 66 PCa lesions compared to f-DWI. When radiologists used DL-CAD, z-DWI also enabled radiologists to exhibit a higher mean PI-RADS score for PCa lesions (4.31 vs. 4.02; P<0.001) and improved scores for 56 PCa lesions compared to f-DWI, however, no statistically significant difference was found in diagnostic performance (AUC, 0.887 vs. 0.881; P=0.16). In multivariable logistic regression analyses, upgraded PI-RADS scores by z-DWI were significantly associated with csPCa risk. Conclusions:z-DWI, in comparison to f-DWI, enhances the diagnostic performance of DL-CAD for PCa, assigning higher PI-RADS scores to malignant lesions. Despite offering limited improvement for radiologists using DL-CAD, z-DWI shows promise in enhancing the detection of csPCa.

Purpose: To develop an MRI-based model for clinically significant prostate cancer (csPCa) diagnosis that can resist rectal artifact interference. Materials and Methods: This retrospective study included 2203 male patients with prostate lesions who underwent biparametric MRI and biopsy between January 2019 and June 2023. Targeted adversarial training with proprietary adversarial samples (TPAS) strategy was proposed to enhance model resistance against rectal artifacts. The automated csPCa diagnostic models trained with and without TPAS were compared using multicenter validation datasets. The impact of rectal artifacts on the diagnostic performance of each model at the patient and lesion levels was compared using the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC). The AUC between models was compared using the DeLong test, and the AUPRC was compared using the bootstrap method. Results: The TPAS model exhibited diagnostic performance improvements of 6% at the patient level (AUC: 0.87 vs 0.81, P < .001) and 7% at the lesion level (AUPRC: 0.84 vs 0.77, P = .007) compared with the control model. The TPAS model demonstrated less performance decline in the presence of rectal artifact-pattern adversarial noise than the control model (Delta AUC: -17% vs -19%, Delta AUPRC: -18% vs -21%). The TPAS model performed better than the control model in patients with moderate (AUC: 0.79 vs 0.73, AUPRC: 0.68 vs 0.61) and severe (AUC: 0.75 vs 0.57, AUPRC: 0.69 vs 0.59) artifacts. Conclusion: This study demonstrates that the TPAS model can reduce rectal artifact interference in MRI-based csPCa diagnosis, thereby improving its performance in clinical applications.

Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency. This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant. DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUCpatient: 0.89 vs. 0.86; AUClesion: 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (ORrectal susceptibility artifact = 5.46; ORdiameter, = 1.12; ORADC = 0.998; all P < .001) and false negatives (ORrectal susceptibility artifact = 3.31; ORdiameter = 0.82; ORADC = 1.007; all P ≤ .03) of DL-CAD. Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD. ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021.

Brain iron homeostasis is maintained through the normal function of blood-brain barrier and iron regulation at the systemic and cellular levels, which is fundamental to normal brain function. Excess iron can catalyze the generation of free radicals through Fenton reactions due to its dual redox state, thus causing oxidative stress. Numerous evidence has indicated brain diseases, especially stroke and neurodegenerative diseases, are closely related to the mechanism of iron homeostasis imbalance in the brain. For one thing, brain diseases promote brain iron accumulation. For another, iron accumulation amplifies damage to the nervous system and exacerbates patients' outcomes. In addition, iron accumulation triggers ferroptosis, a newly discovered iron-dependent type of programmed cell death, which is closely related to neurodegeneration and has received wide attention in recent years. In this context, we outline the mechanism of a normal brain iron metabolism and focus on the current mechanism of the iron homeostasis imbalance in stroke, Alzheimer's disease, and Parkinson's disease. Meanwhile, we also discuss the mechanism of ferroptosis and simultaneously enumerate the newly discovered drugs for iron chelators and ferroptosis inhibitors.

心源性脑卒中(cardioembolic stroke,CES)主要由心房颤动(atrial fibrillation,AF)诱发,是缺血性脑卒中的重要原因之一,具有较高的致残率和致死率.研究发现,炎症在心源性脑卒中的病理过程中发挥了重要作用,C 反应蛋白(C-reactive protein,CRP)等传统炎症标志物的水平对 CES的预测、预防和治疗有一定的帮助,但受限于疾病特异性,它们在临床 CES中应用并不广泛.因此,特异性更高的生物学标志物是近年来研究者寻找的目标,从而更好地帮助临床决策、治疗及改善 CES预后.因此,本文对 CES传统炎症标志物的应用价值及局限性进行介绍,并就心源性脑卒中在 miRNA和基因领域新的生物学标志物的研究现状做一综述.

Background:To use adversarial training to increase the generalizability and diagnostic accuracy of deep learning models for prostate cancer diagnosis.Methods:This multicenter study retrospectively included 396 prostate cancer patients who underwent magnetic resonance imaging (development set, 297 patients from Shanghai Jiao Tong University Affiliated Sixth People's Hospital and Eighth People's Hospital; test set, 99 patients from Renmin Hospital of Wuhan University). Two binary classification deep learning models for clinically significant prostate cancer classification [PM1, pretraining Visual Geometry Group network (VGGNet)-16-based model 1; PM2, pretraining residual network (ResNet)-50-based model 2] and two multiclass classification deep learning models for prostate cancer grading (PM3, pretraining VGGNet-16-based model 3; PM4: pretraining ResNet-50-based model 4) were built using apparent diffusion coefficient and T2-weighted images. These models were then retrained with adversarial examples starting from the initial random model parameters (AM1, adversarial training VGGNet-16 model 1; AM2, adversarial training ResNet-50 model 2; AM3, adversarial training VGGNet-16 model 3; AM4, adversarial training ResNet-50 model 4, respectively). To verify whether adversarial training can improve the diagnostic model's effectiveness, we compared the diagnostic performance of the deep learning methods before and after adversarial training. Receiver operating characteristic curve analysis was performed to evaluate significant prostate cancer classification models. Differences in areas under the curve (AUCs) were compared using Delong's tests. The quadratic weighted kappa score was used to verify the PCa grading models.Results:AM1 and AM2 had significantly higher AUCs than PM1 and PM2 in the internal validation dataset (0.84 vs. 0.89 and 0.83 vs. 0.87) and test dataset (0.73 vs. 0.86 and 0.72 vs. 0.82). AM3 and AM4 showed higher κ values than PM3 and PM4 in the internal validation dataset {0.266 [95% confidence interval (CI): 0.152-0.379] vs. 0.292 (95% CI: 0.178-0.405) and 0.254 (95% CI: 0.159-0.390) vs. 0.279 (95% CI: 0.163-0.396)} and test set [0.196 (95% CI: 0.029-0.362) vs. 0.268 (95% CI: 0.109-0.427) and 0.183 (95% CI: 0.015-0.351) vs. 0.228 (95% CI: 0.068-0.389)].Conclusions:Using adversarial examples to train prostate cancer classification deep learning models can improve their generalizability and classification abilities.

Background This study aimed to exam the effects of thin-slab maximum intensity projection (TS-MIP) of computed tomography angiography (CTA) for collateral score (CS) and clot burden score (CBS) evaluation in patients with large-vessel-occlusion (LVO) stroke in the anterior circulation. Methods Of 241 consecutive patients with LVO stroke admitted to our center between August 2015 and June 2020, 187 patients were enrolled. CS and CBS were evaluated on conventional CTA and TS-MIP separately. Outcome at 90 days was classified as good if modified Rankin scale (mRS) was ≤2 and as poor if mRS was >2. The correlations between CS and CBS and clinical outcomes were assessed. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic values of CS and CBS. Multivariate logistic regression analysis was performed to identify the independent predictors of 90-day good clinical outcomes. Results The correlation coefficient for clinical outcomes was significantly better for CS based on TS-MIP than that based on conventional CTA (-0.444 vs. -0.285, P=0.039); no significant difference was found in the CBS evaluation (TS-MIP: -0.356 vs. conventional CTA: -0.320, P=0.348). For predicting good clinical outcomes, TS-MIP-based CS was associated with larger area under the curve (AUC) (0.709 vs. 0.609, P=0.004) and higher sensitivity (69.1% vs. 42.0%, P=0.001) than CS based on CTA. In multivariable logistic regression analysis, the factors independently associated with good outcomes were National Institutes of Health Stroke Scale (NIHSS) score at admission (OR =1.147; P<0.001), TS-MIP-based CS (OR =0.326; P<0.001), final modified treatment in cerebral infarction (mTICI) score of 2b/3 (OR =0.098; P<0.001), and hemorrhagic transformation (OR =3.662; P<0.001). Conclusions TS-MIP-CTA is superior to conventional CTA for evaluation CS and CBS, and TS-MIP-based CS may be a useful predictor of clinical outcome.

目的 应用三维CT重建图像测量肩关节前向不稳患者的盂肱关节骨性数据.方法 回顾性分析有肩关节前脱位史的患者48例(病例组)和正常肩关节50例(对照组)的CT图像,测量其肩胛盂高宽比、盂窝深度、肱骨头包含角度及喙肱距离.结果 对照组和病例组的肩胛盂高宽比为1.52±0.03和1.64±0.05,肱骨头包含角度为(68.54±2.07)°和(62.55±2.43)°,2组间有统计学差异;对照组和病例组的盂窝深度为(2.12±0.29)mm和(2.09±0.28)mm,喙肱距离为(11.55±0.41)mm和(11.44±0.38)mm,2组间无统计学差异.结论 肩胛盂高宽比的增大和肱骨头包含角度的缩小,与肩关节前向不稳相关.

Objectives We aim to explore and analyze the related influencing factors of liver and cardiac iron overload in MDS patients detected by magnetic resonance imaging (MRI).Methods We have detected cardiac T2* and liver T2* by MRI in 105 MDS patients. Among them, 20 patients accepted MRI examination before and after iron chelation therapy (ICT). Results: We found that adjusted ferritin (ASF) was significantly correlated with liver T2* and cardiac T2*. RBC transfusion volume, brain natriuretic peptide (BNP) and age were the related factors of cardiac T2*, while RBC transfusion volume and erythropoietin (EPO) were related factors of liver T2*. After ICT, the changes of ASF and liver T2* were earlier than cardiac T2*. Chronic hepatitis but virus copy normal's has no significant effect on liver iron deposition.Conclusion These results showed special attention should be paid to these related influencing factors of liver and cardiac T2* expression when we evaluated iron overload and detected the efficacy of ICT in MDS patients.

Insulin release index (IRI) based on 72-h fasting test has been used for the definitive diagnosis of insulinoma; however, hospitalization and subsequent costs contribute to the disadvantage of IRI. Therefore, a simple and cost-effective screening procedure for the diagnosis of insulinoma for outpatients are crucially needed. Continuous glucose monitoring (CGM) has been widely used for monitoring high level of glucose in diabetic patients. The aim of the study is to determine the potential contribution or implementation of CGM in the screening of the insulinoma. We performed a single-center prospective study with the demographics and laboratory data including 28 patients with the pathological diagnosis of insulinoma and 25 patients with functional hypoglycemia as control group. The analysis showed that areas under the receiver operating characteristic (ROC) curve of coefficient of variation (CV) was 0.914. The CV cutoff point was 19% with the Youden 62.1%, the corresponding sensitivity and specificity were 82.1 and 80%, respectively. In patients with CV greater than the median, more than 60% of insulinomas were located in the head of the pancreas; most Ki-67 values were more than 2% and when compared with the group with CV smaller than the median, the average tumor size was 2.7 times larger. In conclusion, CGM can be used as a valuable tool in not only monitoring high glucose levels in diabetic patients but also identifying the etiology of insulinoma. CV greater than 19% can be highly effective for the screening of insulinoma in outpatients.

Background Apparent diffusion coefficients (ADCs) obtained with diffusion-weighted imaging (DWI) are highly valuable for the detection and staging of prostate cancer and for assessing the response to treatment. However, DWI suffers from significant anatomic distortions and susceptibility artifacts, resulting in reduced accuracy and reproducibility of the ADC calculations. The current methods for improving the DWI quality are heavily dependent on software, hardware, and additional scan time. Therefore, their clinical application is limited. An accelerated ADC generation method that maintains calculation accuracy and repeatability without heavy dependence on magnetic resonance imaging scanners is of great clinical value. Objectives We aimed to establish and evaluate a supervised learning framework for synthesizing ADC images using generative adversarial networks. Methods This prospective study included 200 patients with suspected prostate cancer (training set: 150 patients; test set #1: 50 patients) and 10 healthy volunteers (test set #2) who underwent both full field-of-view (FOV) diffusion-weighted imaging (f-DWI) and zoomed-FOV DWI (z-DWI) with b-values of 50, 1,000, and 1,500 s/mm2. ADC values based on f-DWI and z-DWI (f-ADC and z-ADC) were calculated. Herein we propose an ADC synthesis method based on generative adversarial networks that uses f-DWI with a single b-value to generate synthesized ADC (s-ADC) values using z-ADC as a reference. The image quality of the s-ADC sets was evaluated using the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity (SSIM), and feature similarity (FSIM). The distortions of each ADC set were evaluated using the T2-weighted image reference. The calculation reproducibility of the different ADC sets was compared using the intraclass correlation coefficient. The tumor detection and classification abilities of each ADC set were evaluated using a receiver operating characteristic curve analysis and a Spearman correlation coefficient. Results The s-ADCb1000 had a significantly lower RMSE score and higher PSNR, SSIM, and FSIM scores than the s-ADCb50 and s-ADCb1500 (all P < 0.001). Both z-ADC and s-ADCb1000 had less distortion and better quantitative ADC value reproducibility for all the evaluated tissues, and they demonstrated better tumor detection and classification performance than f-ADC. Conclusion The deep learning algorithm might be a feasible method for generating ADC maps, as an alternative to z-ADC maps, without depending on hardware systems and additional scan time requirements.

Purpose To develop and evaluate a diffusion-weighted imaging (DWI) deep learning framework based on the generative adversarial network (GAN) to generate synthetic high-b-value (b =1500 sec/mm2) DWI (SYNb1500) sets from acquired standard-b-value (b = 800 sec/mm2) DWI (ACQb800) and acquired standard-b-value (b = 1000 sec/mm2) DWI (ACQb1000) sets. Materials and Methods This retrospective multicenter study included 395 patients who underwent prostate multiparametric MRI. This cohort was split into internal training (96 patients) and external testing (299 patients) datasets. To create SYNb1500 sets from ACQb800 and ACQb1000 sets, a deep learning model based on GAN (M0) was developed by using the internal dataset. M0 was trained and compared with a conventional model based on the cycle GAN (Mcyc). M0 was further optimized by using denoising and edge-enhancement techniques (optimized version of the M0 [Opt-M0]). The SYNb1500 sets were synthesized by using the M0 and the Opt-M0 were synthesized by using ACQb800 and ACQb1000 sets from the external testing dataset. For comparison, traditional calculated (b =1500 sec/mm2) DWI (CALb1500) sets were also obtained. Reader ratings for image quality and prostate cancer detection were performed on the acquired high-b-value (b = 1500 sec/mm2) DWI (ACQb1500), CALb1500, and SYNb1500 sets and the SYNb1500 set generated by the Opt-M0 (Opt-SYNb1500). Wilcoxon signed rank tests were used to compare the readers' scores. A multiple-reader multiple-case receiver operating characteristic curve was used to compare the diagnostic utility of each DWI set. Results When compared with the Mcyc, the M0 yielded a lower mean squared difference and higher mean scores for the peak signal-to-noise ratio, structural similarity, and feature similarity (P < .001 for all). Opt-SYNb1500 resulted in significantly better image quality (P ≤ .001 for all) and a higher mean area under the curve than ACQb1500 and CALb1500 (P ≤ .042 for all). Conclusion A deep learning framework based on GAN is a promising method to synthesize realistic high-b-value DWI sets with good image quality and accuracy in prostate cancer detection.Keywords: Prostate Cancer, Abdomen/GI, Diffusion-weighted Imaging, Deep Learning Framework, High b Value, Generative Adversarial Networks© RSNA, 2021 Supplemental material is available for this article.

Background Endothelial progenitor cell (EPC) dysfunction contributes to vascular disease in diabetes mellitus. However, the molecular mechanism underlying EPC dysfunction and its contribution to delayed reendothelialization in diabetes mellitus remain unclear. Our study aimed to illustrate the potential molecular mechanism underlying diabetic EPC dysfunction in vivo and in vitro. Furthermore, we assessed the effect of EPC transplantation on endothelial regeneration in diabetic rats. Methods Late outgrowth EPCs were isolated from the bone marrow of rats for in vivo and in vitro studies. In vitro functional assays and Western blotting were conducted to reveal the association between C-X-C chemokine receptor type 7 (CXCR7) expression and diabetic EPC dysfunction. To confirm the association between cellular CXCR7 levels and EPC function, CXCR7 expression in EPCs was upregulated and downregulated via lentiviral transduction and RNA interference, respectively. Western blotting was used to reveal the potential molecular mechanism by which the Stromal-Derived Factor-1 (SDF-1)/CXCR7 axis regulates EPC function. To elucidate the role of the SDF-1/CXCR7 axis in EPC-mediated endothelial regeneration, a carotid artery injury model was established in diabetic rats. After the model was established, saline-treated, diabetic, normal, or CXCR7-primed EPCs were injected via the tail vein. Results Diabetic EPC dysfunction was associated with decreased CXCR7 expression. Furthermore, EPC dysfunction was mimicked by knockdown of CXCR7 in normal EPCs. However, upregulating CXCR7 expression reversed the dysfunction of diabetic EPCs. The SDF-1/CXCR7 axis positively regulated EPC function by activating the AKT-associated Kelch-like ECH-associated protein 1 (keap-1)/nuclear factor erythroid 2-related factor 2 (Nrf2) axis, which was reversed by blockade of AKT and Nrf2. Transplantation of CXCR7-EPCs accelerated endothelial repair and attenuated neointimal hyperplasia in diabetes mellitus more significantly than transplantation of diabetic or normal EPCs. However, the therapeutic effect of CXCR7-EPC transplantation on endothelial regeneration was reversed by knockdown of Nrf2 expression. Conclusions Dysfunction of diabetic EPCs is associated with decreased CXCR7 expression. Furthermore, the SDF-1/CXCR7 axis positively regulates EPC function by activating the AKT/keap-1/Nrf2 axis. CXCR7-primed EPCs might be useful for endothelial regeneration in diabetes-associated vascular disease.

目的 评价卵巢Brenner瘤在CT、MRI上的特征,提高卵巢Brenner瘤的术前诊断水平.方法 回顾性分析经手术病理证实的20例Brenner瘤和31例附件区其他实性肿瘤患者的临床影像学资料,对肿瘤大小、部位、密度及信号特征以及强化程度等进行统计学分析.结果 卵巢Brenner瘤多发生在绝经期妇女,常合并有黏液性囊腺瘤.CT表现囊实性或实性肿块,肿块内伴有钙化,强化略高于同层子宫肌层,差异无显著性(P>0.05);MRI显示肿瘤实性部分T2 WI信号低于同层面骨骼肌信号,肿瘤实性部分强化低于同层面子宫肌层信号并且高于同层面骨骼肌信号,不伴腹盆腔积液.而卵泡膜纤维瘤虽也表现为T2 WI低信号,但强化多低于卵巢Brenner瘤,常伴有腹盆腔积液;阔韧带肌瘤表现为实性肿块,强化程度高于Brenner瘤.Brenner瘤这些特点与附件区易混淆的肿瘤存在显著差异(P<0.05).结论 卵巢Brenner瘤CT表现实性或囊实性病灶,瘤体内常伴有钙化;肿瘤实性部分MRI则表现为T2 WI低信号,强化程度低于同层面子宫肌层信号并且高于同层面骨骼肌信号,这些半定量特点有助于与易混淆的附件区其他实性肿瘤相鉴别.

Objective: The purpose of this study was to identify the difference between dual energy spectral computed tomography (DECT) and magnetic resonance imaging (MRI) used to detect liver/cardiac iron content in Myelodysplastic syndrome (MDS) patients with differently adjusted serum ferritin (ASF) levels. Method: Liver and cardiac iron content were detected by DECT and MRI. Patients were divided into different subgroups according to the level of ASF. The receiver operating characteristic curve (ROC) analysis was applied in each subgroup. The correlation between iron content detected by DECT/MRI and ASF was analyzed in each subgroup. Result: ROC curves showed that liver virtual iron content (LVIC) Az was significantly less than liver iron concentration (LIC) Az in the subgroup with ASF < 1,000 ng/ml. There was no significant difference between LVIC Az and LIC Az in the subgroup with 1,000 ≤ ASF < 2,500 ng/ml and 2,500 ≤ ASF < 5,000 ng/ml. LVIC Az was significantly higher than LIC Az in the subgroup with ASF <5,000 and 5,000 ≤ ASF ng/ml. In patients undergoing DECT and MRI examination on the same day, ASF was significantly correlated with LVIC, whereas no significant correlation was observed between ASF and LIC. After removing the data of ASF > 5,000 mg/L in LIC, LIC became correlated with ASF. There was no significant difference between the subgroup with 2,500 ≤ ASF < 5,000 ng/ml and 5,000 ng/ml ≤ ASF in LIC expression. Furthermore, both LIC and liver VIC had significant correlations with ASF in patients with ASF < 2,500 ng/ml, while LVIC was still correlated with ASF, LIC was not correlated with ASF in patients with 2,500 ng/ml ≤ ASF. Moreover, neither cardiac VIC nor myocardial iron content (MIC) were correlated with ASF in these subgroups. Conclusion: MRI and DECT were complementary to each other in liver iron detection. In MDS patients with high iron content, such as ASF ≥ 5,000 ng/ml, DECT was more reliable than the MRI in the assessment of iron content. But in patients with low iron content, such as ASF < 1,000 ng/ml, MRI is more reliable than DECT. Therefore, for the sake of more accurately evaluating the iron content, the appropriate detection method can be selected according to ASF.

目的 探讨定量CT测量的腹部脂肪分布和肝脏脂肪含量与非酒精性脂肪肝(NAFL)的相关性.方法 选取63例行定量CT(QCT)检查的NAFL患者为病例组,同期检查的63例非脂肪肝患者为对照组.测量两组患者的腹部脂肪分布情况及肝脏脂肪含量(FatQI％).比较两组间的差异与相关性,利用受试者工作特征(ROC)曲线计算NAFL的诊断效能.结果 病例组的FatQI％、SAA、VAA、SMA、TAA、V/S及V/T均显著高于对照组,而年龄差异无统计学意义(P＞0.05);CTL/S值与肝脏脂肪含量呈高度相关(r=-0.776,P＜0.001)、与SAA、VAA、SMA及TAA呈中度相关(r=-0.499、-0.593、-0.409和-0.639,P＜0.001);VAA和FatQI％是NAFL的独立危险因素;通过ROC曲线显示,FatQI％的曲线下面积(AUC)为0.952(95％CI为0.919 ～0.985).结论 肝脏脂肪含量、SAA、VAA、SMA及TAA与NAFL有相关性,VAA和肝脏脂肪含量可预测和评估NAFL的指标之一,肝脏脂肪含量可作为NAFL影像诊断的参考指标.

Background It is generally believed that gait characteristics of diabetic neuropathic patients differ from non-diabetic ones. However, it is still unclear whether these changes during walking could also be extended for different conditions of type 2 diabetes mellitus (T2DM), especially diabetic individuals with lower extremity complications. Methods In this investigation, gait was examined among 1861 participants with or without T2DM from three study centers. Subjects with NGT(normal glucose tolerance) and IGT (impaired glucose tolerance) were taken as control groups, patients with T2DM were divided into 4 groups: DM (no chronic complications), DPN (DM complicated with diabetic peripheral neuropathy), LEAD (DM complicated with lower extremity artery disease) and DPN + LEAD. Analyses of variance were employed to verify possible differences of gait parameters between these groups. Results Results showed lowered cadence, increased step time and decreased walking speed in diabetes with lower extremity neural and/or vascular complications (all p < 0.05). Increased duty factor_double stance was displayed in participants with LEAD alone (32.51%, p < 0.05). And the results displayed lower SDA (1.32 vs. 1.57, p < 0.01) and SDB (0.38 vs. 0.51, p < 0.01) of subjects with both DPN and LEAD. All these indicated the subjects with diabetic lower extremity complications showed much more conservative gait pattern. Stepwise multivariate regression models showed that independent variables were sex, age and leg length (p < 0.01). While, VPT was listed as significant independent predictor of cadence, step time, SDA and SDB (p < 0.05). And ABI was documented as significant independent predictor of stride length, duty-factor_Double stance, SDA and SDB (p < 0.05). Binary logistic regression analysis revealed a significant positive association between decreased stride length and the lowest ABI group (OR = 112.19, 95% CI: 3.11- 4040.13, p = 0.01). ROC analysis showed a significant discriminatory power of step time for occurrence of DPN. The AUC value was 0.752 (95% CI: 0.721–0.782, p < 0.01) . Conclusions In total, the cut-off point was 526.13 ms for predicting DPN. Maybe, this non-invasive and non-irritating gait examination could be an alternative measurement that could help distinguish diabetic neuropathy conveniently.

We aimed to compare the efficiency of prostate cancer (PCa) detection using a radiomics signature based on advanced zoomed diffusion-weighted imaging and conventional full-field-of-view DWI. A total of 136 patients, including 73 patients with PCa and 63 without PCa, underwent multi-parametric magnetic resonance imaging (mp-MRI). Radiomic features were extracted from prostate lesion areas segmented on full-field-of-view DWI with b-value = 1500 s/mm2 (f-DWIb1500), advanced zoomed DWI images with b-value = 1500 s/mm2 (z-DWIb1500), calculated zoomed DWI with b-value = 2000 s/mm2 (z-calDWIb2000), and apparent diffusion coefficient (ADC) maps derived from both sequences (f-ADC and z-ADC). Single-imaging modality radiomics signature, mp-MRI radiomics signature, and a mixed model based on mp-MRI and clinically independent risk factors were built to predict PCa probability. The diagnostic efficacy and the potential net benefits of each model were evaluated. Both z-DWIb1500 and z-calDWIb2000 had significantly better predictive performance than f-DWIb1500 (z-DWIb1500 vs. f-DWIb1500: p = 0.048; z-calDWIb2000 vs. f-DWIb1500: p = 0.014). z-ADC had a slightly higher area under the curve (AUC) value compared with f-ADC value but was not significantly different (p = 0.127). For predicting the presence of PCa, the AUCs of clinical independent risk factors model, mp-MRI model, and mixed model were 0.81, 0.93, and 0.94 in training sets, and 0.74, 0.92, and 0.93 in validation sets, respectively. Radiomics signatures based on the z-DWI technology had better diagnostic accuracy for PCa than that based on the f-DWI technology. The mixed model was better at diagnosing PCa and guiding clinical interventions for patients with suspected PCa compared with mp-MRI signatures and clinically independent risk factors. • Advanced zoomed DWI technology can improve the diagnostic accuracy of radiomics signatures for PCa. • Radiomics signatures based on z-calDWIb2000 have the best diagnostic performance among individual imaging modalities. • Compared with the independent clinical risk factors and the mp-MRI model, the mixed model has the best diagnostic efficiency.

目的 探讨胆囊癌及非胆囊癌的CT征象差异在临床中应用价值.方法 对经手术病理证实的112例胆囊病变CT征象进行回顾性分析,采用t检验及卡方检验比较胆囊壁增厚类型、强化方式、黏膜完整性及胆囊周围情况等10项差异,回归分析胆囊癌的相关因素和诊断胆囊癌的敏感性、特异性等.结果 112例胆囊病变中,胆囊癌共66例,非胆囊癌46例;胆囊病变的CT征象中,发现:胆囊病变的类型(P =0.003)、胆囊壁增厚程度(P＜0.001)、动脉期CT值是否高于肝实质(P =0.009)、是否合并有结石(P=0.023)、是否伴有肿大淋巴结(P＜0.001)、胆囊黏膜完整性(P ＜0.001)及胆囊壁周围肝情况(P=0.003),此7项影像征象在胆囊癌与非胆囊癌差异有显著统计学意义(P＜0.05).回归分析表明,动脉期CT值高于同期肝实质及黏膜不完整性与胆囊癌有密切相关,其诊断胆囊癌的敏感性、特异性、准确性分别为57.57％、67.39％、61.61％及87.87％、80.43％、84.82％.结合两项可进一步提高胆囊癌的诊断准确性.结论 胆囊的一些非肿瘤性病变貌似胆囊癌,但如出现动脉期CT值高于同期肝实质及黏膜壁不完整征象则多为胆囊癌.