BACKGROUND:To ascertain the diagnostic value of radiomic features of pericoronary adipose tissue (PCAT) and other coronary computed tomography angiography (CCTA) parameters for differentiating non-ST-segment-elevation myocardial infarction (NSTEMI) from unstable angina (UA). METHODS:This study included NSTEMI and UA patients (n = 102 each). The radiomic features of PCAT were selected according to the intraclass correlation coefficient, Pearson's coefficient, the t test, and least absolute shrinkage and selection operator. Six classifiers-random forest, support vector machine, naive Bayes, K-nearest neighbors, extreme gradient boosting, and light gradient boosting machine (LightGBM)-were used to build radiomics models, and the best were selected. Four CCTA parameter models, encapsulating plaque parameters (model 1), plaque parameters + fatty attenuation index (FAI) (model 2), plaque parameters + CT fractional flow reserve (CT-FFR) (model 3), and plaque parameters + CT-FFR + FAI (model 4), were constructed. Finally, we established a fusion model (nomogram) with all CCTA parameters and radiomics model scores. All models were compared regarding their performance. RESULTS:The LightGBM radiomics model achieved the highest AUC. Among CCTA parameter models, only model 4 achieved a predictive performance similar to that of the radiomics model in the training and test cohorts (AUC = 0.904 vs. 0.898 and 0.860 vs. 0.877). The combined model (nomogram) showed greater predictive efficacy (AUC = 0.963, 0.910) than model 4 or the radiomics model. CONCLUSION:The PCAT-based radiomics model accurately distinguishes between NSTEMI and UA, with similar diagnostic performance as the model that combined all the significant CCTA parameters. The nomogram integrating CCTA parameters and the radiomic score has good clinical application prospects.

Objective:This study assessed whether radiomics features could stratify parotid gland tumours accurately based on only noncontrast CT images and validated the best classifier of different radiomics models. Methods:In this single-centre study, we retrospectively recruited 249 patients with a diagnosis of pleomorphic adenoma (PA), Warthin tumour (WT), basal cell adenoma (BCA) or malignant parotid gland tumours (MPGTs) from June 2020 to August 2022. Each patient was randomly classified into training and testing cohorts at a ratio of 7:3, and then, pairwise comparisons in different parotid tumour groups were performed. CT images were transferred to 3D-Slicer software and the region of interest was manually drawn for feature extraction. Feature selection methods were performed using the intraclass correlation coefficient, t test and least absolute shrinkage and selection operator. Five common classifiers, namely, random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbours (KNN) and general Bayesian network (Gnb), were selected to build different radiomics models. The receiver operating characteristic curve, area under the curve (AUC), accuracy, sensitivity, specificity and F-1 score were used to assess the prediction performances of these models. The calibration of the model was calculated by the Hosmer-Lemeshow test. DeLong's test was utilized for comparing the AUCs. Results:The radiomics model based on the RF, SVM, Gnb, LR, LR and RF classifiers obtained the highest AUC in differentiating PA from MPGTs, WT from MPGTs, BCA from MPGTs, PA from WT, PA from BCA, and WT from BCA, respectively. Accordingly, the AUC and the accuracy of the model for each classifier were 0.834 and 0.71, 0.893 and 0.79, 0.844 and 0.79, 0.902 and 0.88, 0.602 and 0.68, and 0.861 and 0.94, respectively. Conclusion:Our study demonstrated that noncontrast CT-based radiomics could stratify refined pathological types of parotid tumours well but could not sufficiently differentiate PA from BCA. Different classifiers had the best diagnostic performance for different parotid tumours. Our study findings add to the current knowledge on the differential diagnosis of parotid tumours.

The fat attenuation index (FAI) is a radiological parameter that represents pericoronary adipose tissue (PCAT) inflammation, along with myocardial bridging (MB), which leads to pathological shear stress in the coronary vessels; both are associated with coronary atherosclerosis. In the present study, we assessed the predictive value of FAI values and MB parameters through coronary computed tomography angiography (CCTA) for predicting the risk of coronary atherosclerosis and vulnerable plaque in patients with MB. We included 428 patients who underwent CCTA and were diagnosed with MB. FAI values, MB parameters, and high-risk coronary plaque (HRP) characteristics were recorded. The subjects were classified into two groups (A and B) according to the absence or presence of coronary plaque in the segment proximal to the MB. Group B was further divided into Groups B1 (HRP-positive) and B2 (HRP-negative) according to the HRP characteristic classification method. The differences among the groups were analysed. Multiple logistic regression analysis was performed to determine the independent correlation between FAI values and MB parameters and coronary atherosclerosis and vulnerable plaque risk. Compared to the subjects in Group A, those in Group B presented greater MB lengths, MB depths and muscle index values, more severe MB systolic stenosis and higher FAIlesion values (all P < 0.05). In multivariate logistic analysis, age (OR 1.076, P < 0.001), MB systolic stenosis (OR 1.102, P < 0.001) and FAIlesion values (OR 1.502, P < 0.001) were independent risk factors for the occurrence of coronary atherosclerosis. Compared to subjects in Group B2, those in Group B1 presented greater MB lengths and higher FAI values (both P < 0.05). However, only the FAIlesion value was an independent factor for predicting HRP (OR 1.641, P < 0.001). In patients with MB, MB systolic stenosis was associated with coronary plaque occurrence in the segment proximal to the MB. The FAI value was not only closely related to coronary atherosclerosis occurrence but also associated with plaque vulnerability. FAI values may provide more significant value in the prediction of coronary atherosclerosis than MB parameters in CCTA.

Objective:To evaluate the diagnostic value of ultrasound in isoechoic and hyperechoic thyroid nodules.Methods:A retrospectively analysis was performed on the ultrasonographic data of 128 cases of isoechoic and hyperechoic thyroid nodules confirmed by surgery and pathology at the Third Affiliated Hospital of Soochow University from August 2019 to December 2020. Based on the pathological results as the gold standard, there were 94 cases of benign nodules and 34 cases of malignant nodules. The ultrasonic features of nodules, including echo, margin, growth mode, calcification, halo, echo texture, posterior echo, nodule-in-nodule architecture, cystic change, and blood flow, were evaluated. Independent sample t-test, χ2 test, or Fisher's exact test was used for statistical comparison of ultrasonic features between groups. The diagnostic efficacy of each parameter was analyzed using the receiver operating characteristic (ROC) curve and the area under curve (AUC).Results:In the benign and malignant groups, irregular margin of the nodule (38.2% vs 5.3%, P＜0.001), vertical growth (17.6% vs 2.1%, P=0.004), calcification (73.5% vs 17.0%, P＜0.001), uneven echo texture (94.1% vs 55.3%, χ2=16.53, P＜0.001), and a little blood flow (79.4% vs 57.4%, P=0.017) all suggested the risk of malignancy. There was no difference in the presence or absence of halo between the two groups (P＞0.05). In nodules with halo, inconsistent halo thickness (84.2% vs 8.2%, χ2=37.58, P＜0.001) suggested the risk of malignancy. The diagnostic sensitivity of uneven echo texture of nodules (94.12%) was the highest, and the diagnostic specificity of vertical growth (97.87%) was the highest. The AUC and diagnostic accuracy of inconsistent sound halo thickness were the highest (0.880 and 89.71%, respectively).Conclusion:Isoechoic and hyperechoic nodules with irregular edges, vertical growth, coarse calcification or microcalcification, inconsistent thickness of acoustic halo, uneven echo texture, and a little blood supply are helpful to the diagnosis of malignant nodules.

磨玻璃结节(GGN)是指在薄层肺窗CT影像上观察到局部密度增加,但不遮盖肺内血管和支气管的模糊影.肺癌筛查中可以检出影像表现为GGN且病理结果 为肺腺癌的病人.影像表现为GGN肺腺癌病理亚型不尽相同,早期诊断和准确鉴别对病人的治疗及改善预后具有重要的临床价值.就GGN的CT、MRI及PET/CT影像特征在鉴别表现为GGN的肺腺癌病理亚型方面的研究进展予以综述.

Superb microvascular imaging (SMI) is an innovative Doppler technique for vascular examination. It uses an intelligent algorithm that efficiently separates low-speed flow signals from motion artifacts so that it can assess microvessels and the vessel distribution in detail. This article reviews the clinical applications of SMI in the disorders of superficial tissues and organs including thyroid nodules, breast tumors and lymph node diseases etc. More information of diseases that are closely associated with angiogenesis can be shown by SMI than other noninvasive examinations. Although some limitations exist, this safe and convenient technique is becoming acceptable and would play a more important role in disease diagnosis and therapeutic responses evaluation.

Rationale and Objectives: To evaluate qualitative and quantitative indicators generated from Dual-energy computed tomography (DECT) for preoperatively differentiating between invasive adenocarcinoma (IAC) and preinvasive or minimally invasive adenocarcinoma (MIA) lesions manifesting as ground-glass opacity-predominant (GGO-predominant) nodules. Materials and Methods: We retrospectively enrolled 143 cases of completely resected GGO-predominant lung adenocarcinoma with DECT examinations between December 2017 and July 2019. Qualitative and quantitative parameters of GGO-predominant nodules were compared after grouping nodules into IAC and preinvasive-MIA groups. A multivariate logistic regression models were used for analyzing these parameters. The diagnostic performance of different parameters was compared by receiver operating characteristic (ROC) curves and Z tests. Results: This study included 137 patients (58 years +/- 11; male: female = 52:91) with 143 GGO-predominant nodules. The proportion of margins, internal dilated/distorted/cut-off bronchi, internal thickened/stiff/distorted vasculature, pleural indentation, and vascular convergence were higher in the IAC group than in the preinvasive-MIA group, as were the maximum diameter (Dmax), the diameter of the solid component (Dsolid) and the enhanced monochromatic CT value at 40 keV-190 keV (CT40 keV-190 keV) (p range: 0.001-0.019). Logistic regression analyses revealed that margin, Dmax, and CT60 keV values were independent predictors of the IAC group. The area under the curve (AUC) for the combination of margin, Dmax, and CT60 keV was 0.896 (90.2% sensitivity, 70.7% specificity, 84.6% accuracy), which was significantly higher than that for each two of them (all p < 0.05). Conclusion: The combined prediction model generated from DECT allows for effective preoperative differentiation between IAC and preinvasive-MIA in GGO-predominant lung adenocarcinomas. (c) 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

目的:通过MR血氧水平依赖成像(BOLD)初步评价兔肾脏铁过载病理变化的可行性.方法:将24只纯种健康新西兰大白兔随机分为对照组(n=12)和铁过载组(n=12).铁过载组兔经后腿肌内注射右旋糖酐铁(60mg/kg),对照组后腿肌内注射同等剂量0.9％生理盐水,分别于建模后即刻(0周)及第12周对2组兔行左肾MR扫描,12周扫描结束后立即切除左肾行病理学检查.在T2*map图像上分别测量计算肾皮质、外髓的R2*值及R2*外髓/皮质比值(MCR).采用Mann-Whitney U检验比较肌内注射后0周及第12周对照组与铁过载组之间R2*值的差异;采用Wilcoxon检验分别比较对照组及铁过载组实验兔肌内注射后0周与第12周R2*值的差异.结果:肌内注射后0周时,对照组与铁过载组之间皮髓质R2*值及MCR值差异无统计学意义(P＞0.05).肌内注射后第12周时,铁过载组皮质、外髓R2*值显著高于对照组(P＜0.05),MCR值显著低于对照组(P＜0.05).铁过载组第12周肾皮质及髓质R2*值较0周增高,MCR值较0周减低(P＜0.05).结论:铁过载时兔肾皮质R2*值的变化较外髓R2*变化更为明显:兔肾皮质及外髓R2*值增高,MCR值减低.BOLD评价兔铁过载介导肾损伤的病理变化具有可行性.

目的 探讨CT征象联合双能CT(DECT)定量指标鉴别磨玻璃结节(GGN)型肺腺癌病理亚型的价值.方法 回顾性分析2017年12月至2019年3月经手术病理证实的87枚磨玻璃结节型肺腺癌的DECT图像.所有GGN分为浸润性腺癌组(n=62)和浸润前-微浸润组(n=25).单因素分析比较两组GGN CT征象和DECT定量指标的差异.采用二元Logistics回归分析建立联合预测模型,采用受试者工作特性曲线和Z检验比较各参数及联合模型的诊断效能.结果 浸润性腺癌组GGN最大直径(Dmax)、边缘分叶或毛刺、内部支气管扩张/扭曲/截断、内部血管增粗/僵硬/扭曲、血管集束征的比例及40～190 keV增强单能量图像对应的CT值(CT40～190keV)均高于浸润前-微浸润组(P值范围:0.001～0.020).其中,Dmax、内部血管形态及CT60keV为独立预测因子(OR=30.921、6.750、50.361;P值均＜0.05),由此构建联合预测模型的AUC为0.922,诊断准确性达88.5％.联合预测模型的AUC显著高于Dmax、内部血管形态及CT60keV的AUC(P值均＜0.05).结论 CT征象联合DECT定量指标是术前鉴别磨玻璃结节型肺腺癌病理亚型的有效方法,并达到了较高的准确性.