Many researchers nowadays choose multi-omics techniques for myocardial infarction studies. However, there’s yet to be a review article integrating myocardial infarction multi-omics. Hence, this study adopts the popular bibliometrics. Based on its principles, we use software like R Studio, Vosviewer, Citespace, and SciMAT to analyze literature data of myocardial infarction omics research (1991–2022) from Web of Science. By extracting key information and calculating weights, we conduct analyses from 4 aspects: Collaboration Network Analysis, Co-word Analysis, Citing and Cited Journal Analysis, and Co-citation and Clustering Analysis, aiming to understand the field’s cooperation, research topic evolution, and knowledge flow. The results show that myocardial infarction omics research is still in its early stage with limited international cooperation. In terms of knowledge flow, there’s no significant difference within the discipline, but non-biomedical disciplines have joined, indicating an interdisciplinary integration trend. In the overall research field, genomics remains the main topic with many breakthroughs identifying susceptibility sites. Meanwhile, other omics fields like lipidomics and proteomics are also progressing, clarifying the pathogenesis. The cooperation details in this article enable researchers to connect with others, facilitating their research. The evolution trend of subject terms helps them set goals and directions, quickly grasp the development context, and read relevant literature. Journal analysis offers submission suggestions, and the analysis of research base and frontier provides references for the research’s future development.

Background Iron overload plays a critical role in the pathogenesis of diabetic nephropathy. Non-invasive evaluation of renal iron overload in diabetes in the management and intervention of diabetic nephropathy is of great significance. This study aimed to explore the feasibility of blood oxygen level-dependent (BOLD) magnetic resonance imaging (MRI) in evaluating renal iron overload in diabetes using a rabbit model. Methods The rabbits were randomly divided into control, iron-overload (I), diabetes (D), and diabetes with iron-overload (DI) groups (each n = 19). The diabetes models were generated by injecting intravenous alloxan solution, and the iron-overload models were generated by injecting intramuscular iron-dextran. BOLD MRI was performed immediately (week 0) and at week 4, 8, and 12 following modeling. The differences in renal cortex (CR 2 * ) and outer medulla R 2 * (MR 2 * ) and the ratio of MR 2 * –CR 2 * (MCR) across the different time points were compared. Results Iron was first deposited in glomeruli in the I group and in proximal tubular cells in renal cortex in the D group. In the DI group, there was iron deposition in both glomeruli and proximal tubular cells at week 4, and the accumulation increased subsequently. The degree of kidney injury and iron overload was more severe in the DI group than those in the I and D groups at week 12. At week 8 and 12, the CR 2 * and MR 2 * in the DI group were higher than those in the I and D groups (all P < 0.05). The MCR in the I, D, and DI groups decreased from week 0 to 4 (all P < 0.001), and that in the I group increased from week 8 to 12 ( P = 0.034). CR 2 * and MR 2 * values displayed different trends from week 0–12. Dynamic MCR curves in the D and DI groups were different from that in the I group. Conclusion It presents interactions between diabetes and iron overload in kidney injury, and BOLD MRI can be used to evaluate renal iron overload in diabetes.

Abstract Purpose To explore the predictive potential of intratumoral and multiregion peritumoral radiomics features extracted from multiparametric MRI for predicting pathological differentiation in hepatocellular carcinoma (HCC) patients. Methods A total of 265 patients with 277 HCCs (training cohort n = 193, validation cohort n = 84) who underwent preoperative MRI were retrospectively analyzed. The risk factors identified through stepwise regression analysis were utilized to construct a clinical model. Radiomics models based on MRI (arterial phase, portal venous phase, delayed phase) across various regions (entire tumor, Peri_5mm, Peri_10mm, Peri_20mm) were developed using the LASSO approach. The features obtained from the intratumoral region and the optimal peritumoral region were combined to design the IntraPeri fusion model. Model performance was assessed using the area under the curve (AUC). Results Larger size, non-smooth margins, and mosaic architecture were risk factors for poorly differentiated HCC (pHCC). The clinical model achieved AUCs of 0.77 and 0.73 in the training and validation cohorts, respectively, while the intratumoral model achieved corresponding AUC values of 0.92 and 0.82. The Peri_10mm model demonstrated superior performance to the Peri_5mm and Peri_20mm models, with AUC values of 0.87 vs. 0.84 vs. 0.73 in the training cohort and 0.80 vs. 0.77 vs. 0.68 in the validation cohort, respectively. The IntraPeri model exhibited remarkable AUC values of 0.95 and 0.86 in predicting pHCC in the training and validation cohorts, respectively. Conclusions Our study highlights the potential of a multiparametric MRI-based radiomic model that integrates intratumoral and peritumoral features as a tool for predicting HCC differentiation. Critical relevance statement Both clinical and multiparametric MRI-based radiomic models, particularly the intratumoral radiomic model, are non-invasive tools for predicting HCC differentiation. Importantly, the IntraPeri fusion model exhibited remarkable predictiveness for individualized HCC differentiation. Key points • Both the intratumoral radiomics model and clinical features were useful for predicting HCC differentiation. • The Peri_10mm radiomics model demonstrated better diagnostic ability than other peritumoral region-based models. • The IntraPeri radiomics fusion model outperformed the other models for predicting HCC differentiation. Graphical Abstract

Background: Predicting breast cancer molecular subtypes can help guide individualised clinical treatment of patients who need the rational preoperative treatment. This study aimed to investigate the efficacy of preoperative prediction of breast cancer molecular subtypes by contrast -enhanced mammography (CEM) radiomic features. Methods: This retrospective two -centre study included women with breast cancer who underwent CEM preoperatively between August 2016 and May 2022. We included 356 patients with 386 lesions, which were grouped into training ( n = 162), internal test ( n = 160) and external test sets ( n = 64). Radiomics features were extracted from low -energy (LE) images and recombined (RC) images and selected. Three dichotomous tasks were established according to postoperative immunohistochemical results: Luminal vs. non-Luminal, human epidermal growth factor receptor (HER2)-enriched vs. non-HER2-enriched, and triple -negative breast cancer (TNBC) vs. non-TNBC. For each dichotomous task, the LE, RC, and LE+RC radiomics models were built by the support vector machine classifier. The prediction performance of the models was assessed by the area under the receiver operating characteristic curve (AUC). Then, the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the models. DeLong's test was utilised to compare the AUCs. Results: Radiomics models based on CEM are valuable for predicting breast cancer molecular subtypes. The LE+RC model achieved the best performance in the test set. The LE+RC model predicted Luminal, HER2-enriched, and TNBC subtypes with AUCs of 0.93, 0.89, and 0.87 in the internal test set and 0.82, 0.83, and 0.69 in the external test set, respectively. In addition, the LE model performed more satisfactorily than the RC model. Conclusion: CEM radiomics features can effectively predict breast cancer molecular subtypes preoperatively, and the LE+RC model has the best predictive performance.

OBJECTIVES:To explore the value of whole tumour- and subregion-based radiomics of contrast-enhanced mammography (CEM) in differentiating the HER2 expression status of breast cancers. METHODS:352 patients underwent preoperative CEM from two centres were consecutively enroled and divided into the training, internal validation, and external validation cohorts. The lesions were divided into HER2-positive and HER2-negative groups. Besides the radiological features, radiomics features capturing the whole tumour-based (wITH) and subregion-based intratumoral heterogeneity (sITH) were extracted from the craniocaudal view of CEM recombined images. The XGBoost classifier was applied to develop the radiological, sITH, and wITH models. A combined model was constructed by fusing the prediction results of the three models. RESULTS:The mean age of the patients was 51.1 ± 10.7 years. Two radiological features, four wITH features, and three sITH features were selected to establish the models. The combined model significantly improved the AUC to 0.80 ± 0.03 (95% CI: 0.73-0.86), 0.79 ± 0.06 (95% CI: 0.67-0.90), and 0.79 ± 0.05 (95% CI: 0.69-0.89) in the training, internal validation, and external validation cohorts, respectively (All P < 0.05). The combined model showed good agreement between the predicted and observed probabilities and favourable net clinical benefit in the validation cohorts. CONCLUSIONS:Both whole tumour- and subregion-based ITH radiomics features of CEM exhibited potential for differentiating the HER2 expression status. Combining conventional radiological features and ITH features can improve the model's performance.

Abstract Objective To generate an image‐driven biomarker (Rad_score) to predict tumor‐infiltrating regulatory T lymphocytes (Treg) in breast cancer (BC). Methods Overall, 928 BC patients were enrolled from the Cancer Genome Atlas (TCGA) for survival analysis; MRI (n = 71 and n = 30 in the training and validation sets, respectively) from the Cancer Imaging Archive (TCIA) were retrieved and subjected to repeat least absolute shrinkage and selection operator for feature reduction. The radiomic scores (rad_score) for Treg infiltration estimation were calculated via support vector machine (SVM) and logistic regression (LR) algorithms, and validated on the remaining patients. Results Landmark analysis indicated Treg infiltration was a risk factor for BC patients in the first 5 years and after 10 years of diagnosis (p = 0.007 and 0.018, respectively). Altogether, 108 radiomic features were extracted from MRI images, 4 of which remained for model construction. Areas under curves (AUCs) of the SVM model were 0.744 (95% CI 0.622–0.867) and 0.733 (95% CI 0.535–0.931) for training and validation sets, respectively, while for the LR model, AUCs were 0.771 (95% CI 0.657–0.885) and 0.724 (95% CI 0.522–0.926). The calibration curves indicated good agreement between prediction and true value (p > 0.05), and DCA shows the high clinical utility of the radiomic model. Rad_score was significantly correlated with immune inhibitory genes like CTLA4 and PDCD1. Conclusions High Treg infiltration is a risk factor for patients with BC. The Rad_score formulated on radiomic features is a novel tool to predict Treg abundance in the tumor microenvironment.

病例资料 患者,女,88 岁,15 年前无意中发现左乳肿块,近年来肿块明显增大伴肿胀.查体:患者左乳触及一枚大小约 10 cm×5 cm肿块,突出于乳房表面,质硬,边界欠清,活动度欠佳,无酒窝征,无乳头溢液,双侧腋下未触及肿大淋巴结.

Motivation: Intravoxel incoherent motion (IVIM) imaging can evaluate tissue microperfusion and diffusion information respectively, which can more accurately evaluate renal fibrosis (RF) in chronic kidney disease. Goal(s): IVIM can reflect the occurrence and development of RF from two aspects of perfusion and diffusion respectively. It has a great application prospect for the evaluation of RF. Approach: The dynamic changes of renal cortical and medullary signal with the progression of fibrosis were monitored by IVIM, and the compensatory effect of the contralateral kidney was analyzed. Results: Some IVIM parameters of the renal cortex and medulla were moderately or strongly negatively correlated with RF. Impact: Early detection and dynamic evaluation of the process of RF are crucial for reversing renal function damage. IVIM can reflect the development of RF from two aspects of perfusion and diffusion, which has a great application prospect for evaluating RF.

Objective:To explore the diagnostic value of contrast-enhanced mammography (CEM) and MRI in differentiating benign and malignant breast lesions based on the 2013 breast imaging reporting and data system (BI-RADS) lexicon and the supplement on CEM.Methods:The clinical and imaging data of 83 patients with breast lesions from March 2019 to April 2022 in the Third Affiliated Hospital of Soochow University were retrospectively analyzed. Totally 100 breast lesions from 83 female patients aged 28 to 78 (49±14) years, were divided into benign lesions (50 lesions) and malignant lesions (50 lesions) according to the pathological results. The t-test, χ 2 test and Fisher′s exact test were used to compare the differences of clinical and imaging features between benign and malignant lesions, and these imaging features which had statistical differences were established CEM and MRI models by multivariate logistic regression analysis respectively. The receiver operating characteristic curves and the area under the curve (AUC) were used to assess the diagnostic efficacy of two models in differentiating benign and malignant breast lesions. Using the DeLong test compared the AUC. Results:Multivariate logistic regression analysis showed that associated features (OR=9.075,95%CI 1.430-57.570, P=0.019), lesion conspicuity (OR=6.180,95%CI 2.608-14.646, P<0.001), mass margin (OR=2.193,95%CI 1.405-3.422, P=0.001) and calcification distribution (OR=2.147,95%CI 1.157-3.986, P=0.015) were independent predictors of differentiating benign and malignant breast lesions in CEM, and then the predictive model of CEM was constructed. Time-signal intensity curve (OR=9.230, 95%CI 3.178-26.805, P<0.001), associated features (OR=5.289,95%CI 1.343-20.831, P=0.017) and mass margin (OR=2.192,95%CI 1.336-3.597, P=0.002) were independent predictors of differentiating benign and malignant breast lesions in MRI, and the predictive model of MRI was constructed. The AUC of CEM and MRI models for differentiating benign and malignant breast lesions were 0.947 and 0.930 respectively, and two models were no significant difference ( Z=0.68, P=0.494). Conclusion:The diagnostic efficacy of CEM and MRI in differentiating benign and malignant breast lesions is comparable based on the 2013 BI-RADS lexicon and the supplement on CEM.

目的:探讨肾脏表面结节分级能否预警高血压患者发生主动脉夹层(AD).方法:回顾性纳入高血压合并或不合并AD患者,分别设为AD组(n=93)和对照组(n=110).采集年龄、性别、高血压分级等临床信息,以及实验室检查和肾脏表面结节量化分级,采用logistic回归方法分析发生AD的预警征象,绘制ROC曲线比较模型的效能.结果:多因素logistic回归分析显示,未联合肾脏表面结节分级的logistic回归分析的ROC曲线下面积为0.749,联合肾脏表面结节分级的logistic回归分析的ROC曲线下面积为0.803,两者间有统计学差异(P=0.026).随着肾脏表面结节分级增加,发生AD的风险增加,肾脏表面结节1级、2级的OR值分别为4.284(P=0.004)、28.128(P=0.017).结论:肾脏表面结节分级有潜力成为预警高血压患者发生AD的征象之一.

目的 探讨Dixon联合磁敏感加权成像(SWI)鉴别肾脏偶发性实性肿块良恶性的价值.方法 回顾性分析128例恶性和29例良性肾脏偶发性实性肿块的MRI图像.肿块在Dixon反相位上出现信号减低区,且对应的同相位和纯脂肪相上表现为高信号,认为含有成熟脂肪;排除脂肪、血管后,肿块在SWI上出现低信号区,认为含有出血.Dixon显示有成熟脂肪,或Dixon显示无成熟脂肪且SWI显示无瘤内出血,判定肿块为良性;Dixon显示无成熟脂肪,但SWI显示有瘤内出血,判定肿块为恶性.分析Dixon联合SWI鉴别良恶性的诊断效能.结果 128例恶性肿块内均无成熟脂肪;29例良性肿块中22例有成熟脂肪.128例恶性肿块中124例有出血;29例良性肿块中仅1例有出血.128例恶性肿块中,判定为恶性124例、良性4例;29例良性肿块中,判定为良性28例、恶性1例.Dixon联合SWI鉴别肿块良恶性的正确率为96.8％.结论 Dixon联合SWI有助于鉴别肾脏偶发性实性肿块的良恶性.

Objective:To investigate the value of multimodal MRI radiomics in the preoperative prediction of Fuhrman nuclear grade of clear cell renal cell carcinoma (ccRCC).Methods:A total of 129 patients with ccRCC confirmed by pathology from April 2011 to April 2021 in Third Affiliated Hospital of Soochow University were collected, and the imaging and clinicopathological data were retrospectively analyzed. All patients were divided into training set ( n=90) and validation set ( n=39) at the ratio of 7∶3 using random indicator method. According to the postoperative pathological results, Fuhrman grades Ⅰ and Ⅱ were included in the low grade group (96 cases, 65 cases in the training set and 31 cases in the validation set), and Fuhrman grades Ⅲ and Ⅳ were included in the high grade group (33 cases, 25 cases in the training set and 8 cases in the validation set). Two radiologists manually delineated regions of interest (ROI) on T 1WI, T 2WI, Dixon-water, Dixon-fat, susceptibility weighted imaging (SWI), blood oxygen level dependent (BOLD) images, and 396 texture features were extracted from each ROI. In the training set, intra-class correlation coefficient, Mann-Whitney U test, minimum redundancy maximum relevance and least absolute shrinkage and selection operator method were used to reduce the dimension of features to obtain the best texture features. The logistic regression was used to develop the multimodal radiomics model, and the receiver operating characteristic (ROC) curve was used to evaluate the effectiveness of the model in identifying high and low-grade ccRCC in training set and validation set. Results:Four SWI, one T 2WI and one BOLD texture features were selected for modeling. The areas under the ROC curve (95%CI) of the multimodal radiomics model for identifying high and low grade ccRCC in the training and validation sets were 0.859 (0.770-0.923) and 0.883 (0.740-0.964), with the specificity at 95.4% and 87.1%, the sensitivity at 68.0% and 87.5%, the accuracy at 87.8% and 87.2%, respectively. Conclusion:The multimodal MRI radiomics model based on T 2WI, SWI and BOLD images has high effectiveness in preoperative predicting Fuhrman nuclear grade of ccRCC.

Background: Susceptibility weighted imaging (SWI) could reflect tissue blood oxygen levels, and then whether it could be used to evaluate renal injury remains to be further studied. This study aimed to examine the performance of SWI parameters and SWI-based texture features in evaluating renal dysfunction of type 2 diabetes mellitus (T2DM). Methods: Forty-five patients with T2DM were included. With the estimated glomerular filtration rate (eGFR), the patients were divided into non-moderate-severe renal injured group (non-msRI, eGFR >60 mL/min/1.73 m(2)) and moderate-severe renal injured group (msRI, eGFR <= 60 mL/min/1.73 m(2)). The 3 SWI parameters and 16 SWI-based texture features between non-msRI and msRI were compared. The correlation between the parameters and BUN, Scr was analyzed. Results: The signal intensity ratio of the medulla to psoas muscle (MPswi) was significantly lower than the signal intensity ratio of the cortex to psoas muscle (CPswi) in non-msRI and msRI group (t=8.619, 3.483, respectively, P<0.05). MPswi was higher, and the signal intensity ratio of the cortex to the medulla (CMswi), Skewness, Correlation were lower in msRI than in non-msRI (P<0.05). These parameters showed similar diagnostic efficacies for msRI (P>0.05), and AUCs were 0.703-0.854. CMswi was an independent protective factor for msRI (OR=0.026, P=0.003). MPswi and CMswi were correlated with BUN ( r=0.416, -0.545, P<0.05). CMswi and Correlation were correlated with Scr (r=-0.645, -0.411, P<0.05). Conclusions: SWI was valuable for assessing renal dysfunction, which may be helpful for the evaluation of moderate-severe renal injured patients with T2DM.

目的 探讨对比增强能谱乳腺X线摄影(CESM)量化评分对定性乳腺病变的价值.方法 回顾性分析2017年8月至2019年3月本院临床或超声检查怀疑有乳腺病变,并在术前一周内行CESM检查的患者资料.两名经验丰富的乳腺诊断放射科医师共同对同一患者的低能图进行乳腺影像报告和数据系统(BI-RADS)评分,对减影图进行病灶强化程度分级,计算CESM综合评分.通过与病理对照,使用受试者工作特征(ROC)曲线比较低能图BI-RADS评分、减影图病灶强化程度分级和CESM综合评分鉴别诊断乳腺良、恶性病变的效能.结果 检出190个乳腺病灶,其中良性病灶97个,恶性病灶93个.低能图BI-RADS评分、减影图病灶强化程度分级和CESM综合评分判别病灶良、恶性的曲线下面积(AUC)分别为0.83、0.87、0.95;CESM综合评分与低能图BI-RADS评分、减影图病灶强化程度分级的AUC有统计学差异(P均＜ 0.0001),后两者的AUC间无统计学差异(P =0.34);三者判别乳腺病灶为恶性的诊断敏感度分别为84.95％、92.47％、89.25％,特异度分别为71.13％、67.01％、93.81％.结论 综合低能图及减影图的CESM综合评分提高了定性乳腺病变的诊断效能.

Objective To investigate the diagnostic value of superb microvascular imaging ( SM I ) , advanced dynamic flow ( ADF ) and color Doppler flow imaging ( CDFI) in breast microcarcinoma ,and to compare the correlation coefficients between these three indicators and postoperative pathological tumor microvascular density ( M VD ) . Methods A total of 85 patients ( 87 lesions ) with BI‐RADS 4 and the maximum diameter≤1 cm were selected ,of which ,46 lesions were benign ( benign group) and 41 lesions were malignant( malignant group) . All the patients and the corresponding lesions were examined by SM I , ADF and CDFI . Blood flow grading was performed on the images using Adler grade of blood flow ,and the difference of blood flow among the 3 methods was compared . T he expression level of M VD in pathological tumor tissues was detected and analyzed for its correlation with Adler classification by three detection techniques . Results T he areas under the ROC curve( AUR) of CDFI ,ADF and SM I were 0 .694 ,0 .705 and 0 .776 respectively based on the gold standard with pathological diagnosis . T he sensitivity ,specificity , positive predictive value ,negative predictive value ,and accuracy of CDFI were 78 .0% ,54 .3% ,60 .4% , 73 .5% ,and 65 .5% , respectively ; those of ADF were 75 .6% , 60 .9% , 63 .3% , 73 .7% and 67 .8% , respectively ; and those of SM I were 78 .0% ,69 .6% ,69 .6% ,78 .0% and 73 .6% ,respectively . T he Adler grades of CDFI , ADF and SM I were positively correlated with M VD ( P ＜ 0 .05 ) , w hich the highest correlation coefficient between SM I and M VD ( r ＝0 .430 , P ＜0 .001 ) . Conclusions SM I is superior to ADF and CDFI in detecting the abundance of breast microcarcinoma , and has the highest correlation coefficient among those 3 detection techniques with tumor pathological M VD ,which indicates that SM I may be used for differential diagnosis of breast microcarcinoma and indirectly evaluate the prognosis of patients .

目的 探讨良性淋巴结吸入Carbogen气体的磁共振血氧水平依赖成像(MRI-BOLD)动态变化特征.方法 健康新西兰兔12只,建立炎性反应性淋巴结模型,对肿大淋巴结行5次MRI-BOLD检查,分别为基线BOLD(R2*0),实验兔吸入Carbogen气体10 min、20 min时的BOLD检查(R2*10、R2*20),恢复呼吸空气10 min、20 min时的BOLD检查(R2*30、R2*40).所有的实验兔在实验结束后处死取淋巴结行病理检查.选择MRI-BOLD中淋巴结的中心层面,勾画感兴趣区(ROI),记录对应的R2*值.Carbogen气体吸入的前、中、后的R2*比较采用单因素方差分析.结果 R2*0值为(24.9±1.5) s-1,R2*10为(22.8±3.0)s-1,R2*20为(21.7±2.8)s-1,R2*30为(24.4±2.9) s-1,R2*40为(25.0±2.3)s-1,R2*20与R2*0、R2*30、R2*40比较均有统计学意义(P＜0.05),R2*0、R2*30、R2*40之间无统计学差异.结论 MRI-BOLD可以评价炎性反应性淋巴结在吸入Carbogen气体后的氧合状态.

目的 探讨磁共振血氧水平依赖成像(blood oxygenation level-dependent MRI,BOLD-MRI)预测鼻咽癌化放疗近期疗效的可行性.材料与方法 收集我院经病理证实的鼻咽癌患者41例,所有患者接受诱导化疗(induction chemotherapy,IC)、同步放化疗(concurrent chemoradiotherapy,CCRT)和辅助化疗(adjuvant chemotherapy,AC)三个阶段治疗.分别利用IC前和CCRT+AC后的MRI常规成像测量原发肿瘤长径、病理淋巴结短径和转移灶长径,计算病灶的退缩率;利用IC前、后的BOLD成像测量肿瘤的T2*值(T2*baseline和T2*IC).参照实体瘤疗效评价标准1.1,将患者分为完全缓解组(complete response,CR组)及非完全缓解组(non-CR组).采用卡方检验或两独立样本t检验比较CR组和non-CR组的T分期、T2*baseline和T2*IC.将T分期、T2*baseline和T2*IC分别与退缩率进行相关性分析.采用ROC曲线分析T分期、T2*baseline和T2*IC预测鼻咽癌化放疗近期完全缓解的诊断价值.结果 T分期、T2*baseline和T2*IC在CR组与non-CR组间存在差异(P均<0.05).T分期、T2*baseline和T2*IC分别与退缩率呈不同程度相关(r分别依次为-0.481、0.748和0.617,P均<0.05).T分期、T2*baseline和T2*IC预测鼻咽癌CR的受试者特征曲线下面积分别依次为0.778、0.903和0.763,其中T2*baseline的受试者特征曲线下面积最大(P<0.05),T2*baseline诊断肿瘤完全缓解的最佳阈值为37.5 ms.结论 鼻咽癌治疗前BOLD的定量参数T2*baseline值能够预测鼻咽癌化放疗的早期疗效,可作为传统T分期疗效预测的有益补充.

目的 探讨DWI鉴别良恶性淋巴结的价值.方法 32只健康新西兰大白兔,随机分为两组(每组16只),分别建立良性(16个淋巴结)和恶性淋巴结(16个淋巴结)模型.对所有实验兔均行MRI检查.在淋巴结最大层面上选取感兴趣区,获得淋巴结表观扩散系数(ADClymph node);同时测量同层肌肉的ADC值(ADCmuscle),计算相对ADC(rADC=ADClymph node/ADCmuscle).采用两独立样本t检验分别比较良、恶性淋巴结ADC值与rADC值的差异;采用受试者工作特征曲线(ROC曲线),分析ADC及rADC定性诊断淋巴结的效能.结果 良、恶性淋巴结在DWI均表现为相对高信号.良、恶性淋巴结ADC值分别为(0.92±0.13)×103mm2/sa和(0.76±0.08)×10-3 mm2/s-1;良、恶性淋巴结rADC值分别为0.71±0.05和0.63±0.05,良、恶性组淋巴结的ADC值及rADC值差异均有统计学意义(P均＜0.05).利用ADC值鉴别良、恶性淋巴结的ROC曲线下面积为0.83(最佳阈值为0.84×10-3mm2/s-1),利用rADC值进行鉴别的ROC曲线下面积为0.88(最佳阈值为0.66),二者比较无统计学意义(P＞0.05).结论 DWI对定性淋巴结病变有一定的价值,且rADC未能改善淋巴结的定性诊断.

explore the feasibility of evaluating renal ischemia?reperfusion injury (IRI) at different time by using T2*mapping. Methods Eighteen New Zealand white rabbits were used to build therenal IRI injury model by blocked the left renal arteries and veins by using noninvasive arterial clip, left renal ischemia?reperfusion was performed by clamping of the left renal pedicle for 60 minutes, followed by reperfusion. All the rabbits underwent MRI examination including axial T2WI and T2*mapping before scanning and 1 h, 12 h, 24 h and 48 h after reperfusion. Every two rabbits were randomly sacrificed at 1 h, 12 h, and 24 h after reperfusion. The rest of the rabbits were sacrificed for pathological examination at 48 h after reperfusion. All specimens were cut into slices and stained with hematoxylin?eosin (HE). The values of T2*, R2* and the pathological scores of cell edema, cell necrosis, interstitial inflammation and tubular castin renal tissues at different time points were measured. Repetitive measurement deviation analysis was performed to compare difference of T2*and R2*at 5 time?points. The relationship between the value of T2*and R2*in renal tissues and the scores of cell edema, cell necrosis, interstitial inflammation and tubular castin renal tissues was analyzed by Spearman correlation analysis. Results T2*value and R2*value in both inner medulla and outer medulla were statistically significant (P<0.05), while there was no statistically significant in the cortex (P>0.05).Pairwise comparison of T2*and R2*at different time points in the cortex showed statistically significant difference between before and 24 h,before and 48 h, 12 h and 48 h were statistically significant (P<0.05), while the remaining were no statistically significant difference (P>0.05).T2*value of the outer medullar after IRI positively correlated with the scores of cell edema, interstitial inflammation and tubular castin renal tissues(r values were 0.57, 0.38, 0.33; P<0.05). R2* value of the outer medullar after IRI negatively correlated with the scores of cell edema(r value was -0.52, P<0.05). Conclusion T2* mapping could reflect the dynamic changes in different zones and different time points after renal IRI, especially in the outer medullary band which has good consistency with pathological score.

To explore the value of texture analysis based on diffusion-weighted imaging (DWI), blood oxygen level–dependent MRI (BOLD), and susceptibility-weighted imaging (SWI) in evaluating renal dysfunction.