我国老年人认知水平变化轨迹及其分化

目的 探究影响我国老年人认知水平的变化趋势，分离出年龄、队列效应。方法 基于CLHLS（2002—2018）多重队列追踪数据，以Stata16.0软件为工具，运用分层生长曲线模型进行统计分析。结果 本研究发现，个体行为生活方式、社会经济地位、性别、慢性病数量对认知水平均具有统计学意义；年龄、队列对认知水平的变化具有独立效应；随着年龄的增长，我国老年人认知水平下降，认知水平的城乡、性别差异明显；较年轻出生队列的老年人认知水平较好，认知水平的城乡差异随着队列的年轻化而变大，性别差异在较年轻队列有略微缩小的趋势。结论 影响认知水平因素复杂，认知障碍会增加医疗成本及照护负担，因此需准确把握老年认知水平的变化规律与作用路径，从而为卫生服务、养老保障、长期医疗照护的资源配置提供科学依据。     

Pharmacokinetics and Early Tumor Response to Conventional Transarterial Chemoembolization with Sorafenib and Doxorubicin in a VX2 Rabbit Tumor Model

PurposeTo investigate the pharmacokinetics (PK) and early effects of conventional transarterial chemoembolization (TACE) using sorafenib and doxorubicin on tumor necrosis, hypoxia markers, and angiogenesis in a rabbit VX2 liver tumor model.Materials and MethodsVX2 tumor-laden New Zealand White rabbits (N = 16) were divided into 2 groups: 1 group was treated with hepatic arterial administration of ethiodized oil and doxorubicin emulsion (DOX-TACE), and the other group was treated with ethiodized oil, sorafenib, and doxorubicin emulsion (SORA-DOX-TACE). Animals were killed within 3 days of the procedure. Levels of sorafenib and doxorubicin were measured in blood, tumor, and adjacent liver using mass spectrometry. Tumor necrosis was determined by histopathological examination. Intratumoral hypoxia-inducible factor (HIF) 1α, vascular endothelial growth factor (VEGF), and microvessel density (MVD) were determined by immunohistochemistry.ResultsThe median intratumoral concentration of sorafenib in the SORA-DOX-TACE group was 17.7 μg/mL (interquartile range [IQR], 7.42–33.5 μg/mL), and its maximal plasma concentration (C_max) was 0.164 μg/mL (IQR, 0.0798–0.528 μg/mL). The intratumoral concentration and C_max of doxorubicin were similar between the groups: 4.08 μg/mL (IQR, 3.18–4.79 μg/mL) and 0.677 μg/mL (IQR, 0.315–1.23 μg/mL), respectively, in the DOX-TACE group and 1.68 μg/mL (IQR, 0.795–4.08 μg/mL) and 0.298 μg/mL (IQR, 0.241–0.64 μg/mL), respectively, in the SORA-DOX-TACE group. HIF-1α expression was increased in the SORA-DOX-TACE group than in the DOX-TACE group. Tumor volume, tumor necrosis, VEGF expression, and MVD were similar between the 2 groups.ConclusionsThe addition of sorafenib to DOX-TACE delivered to VX2 liver tumors resulted in high intratumoral and low systemic concentrations of sorafenib without altering the PK of doxorubicin.     

Feasibility of a method for low contrast CT image quality assessment using difference detail curves for abdominal scans

This work describes a measurement method for assessing dose-related image-quality of CT scans based on the difference detail curve (DDC) method, and showcases its use in a low contrast setting. The method is based on a phantom consisting of elliptical slices of different sizes into which contrast object modules can be inserted. These modules contain contrast objects based on (synthetic) resin mixtures with sucrose (native) or sodium iodine (contrast medium). Mixing ratios are provided to achieve a range of clinically relevant CT-numbers with these materials. The phantom is characterized in terms of contrast accuracy, energy dependency and long-term drift with satisfying results. Contrast accuracy and energy dependency are similar to that of water or soft tissue. Image quality of 655 scans of the phantom acquired at 30 different clinical institutions and with 16 different CT scanner models from 4 manufacturers was assessed by calculating a difference detail curve (DDC) from evaluation of up to 5 human observers using a custom-made software (RadiVates) described in this work. Based on these measurements, inter-observer variability was quantified using a bootstrap method and was shown to be a large contributor to the overall variability. This work demonstrates that assessment of CT image quality is feasible with the aforementioned phantom and DDC method.     

Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study

IntroductionLiver cancer lesions on Computed Tomography (CT) withholds a great amount of data, which is not visible to the radiologists and radiographer. Radiomics features can be extracted from the lesions and used to train Machine Learning (ML) algorithms to predict between tumour and liver tissue. The purpose of this study was to investigate and classify Radiomics features extracted from liver tumours and normal liver tissue in a limited CT dataset.MethodsThe Liver Tumour Segmentation Benchmark (LiTS) dataset consisting of 131 CT scans of the liver with segmentations of tumour tissue and healthy liver was used to extract Radiomic features. Extracted Radiomic features included size, shape, and location extracted with morphological and statistical techniques according to the International Symposium on Biomedical Imaging manual. Relevant features was selected with chi2 correlation and principal component analysis (PCA) with tumour and healthy liver tissue as outcome according to a consensus between three experienced radiologists. Logistic regression, random forest and support vector machine was used to train and validate the dataset with a 10-fold cross-validation method and the Grid Search as hyper-parameter tuning. Performance was evaluated with sensitivity, specificity and accuracy.ResultsThe performance of the ML algorithms achieved sensitivities, specificities and accuracy ranging from 96.30% (95% CI: 81.03%–99.91%) to 100.00% (95% CI: 86.77%–100.00%), 91.30% (95% CI: 71.96%–98.93%) to 100.00% (95% CI: 83.89%–100.00%)and 94.00% (95% CI: 83.45%–98.75%) to 100.00% (95% CI: 92.45%–100.00%), respectively.ConclusionML algorithms classifies Radiomics features extracted from healthy liver and tumour tissue with perfect accuracy. The Radiomics signature allows for a prognostic biomarker for hepatic tumour screening on liver CT.Implications for practiceDifferentiation between tumour and liver tissue with Radiomics ML algorithms have the potential to increase the diagnostic accuracy, assist in the decision-making of supplementary multiphasic enhanced medical imaging, as well as for developing novel prognostic biomarkers for liver cancer patients.     

EEG functional connectivity contributes to outcome prediction of postanoxic coma

ObjectiveTo investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest.MethodsProspective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5).ResultsWe included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity.ConclusionFunctional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma.SignificanceFunctional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.     

血清淀粉样蛋白A联合神经元特异性烯醇化酶对脑出血的诊断价值

目的探讨血清淀粉样蛋白A(serum amyloid A,SAA)联合神经元特异性烯醇化酶(neuron-specific enolase,NSE)对诊断脑出血的临床价值。方法选取2018-03—2020-03郑州大学第二附属医院80例脑出血患者为观察组,其中轻度组40例,重度组40例;另外选取同期40例健康体检者为对照组。应用ROC曲线对血清淀粉样蛋白A联合NSE检测对脑出血的诊断价值进行分析。结果重度组血清中SAA、NSE水平均高于轻度组和对照组(P<0.05);联合检测的灵敏度、特异度高于SAA或NSE单独检测。结论SAA联合NSE对脑出血患者的早期诊断、临床干预具有重要意义。     

Automated assessment of the substantia nigra on susceptibility map-weighted imaging using deep convolutional neural networks for diagnosis of Idiopathic Parkinson's disease

ObjectivesDespite its use in determining nigrostriatal degeneration, the lack of a consistent interpretation of nigrosome 1 susceptibility map-weighted imaging (SMwI) limits its generalized applicability. To implement and evaluate a diagnostic algorithm based on convolutional neural networks for interpreting nigrosome 1 SMwI for determining nigrostriatal degeneration in idiopathic Parkinson's disease (IPD).MethodsIn this retrospective study, we enrolled 267 IPD patients and 160 control subjects (125 patients with drug-induced parkinsonism and 35 healthy subjects) at our institute, and 24 IPD patients and 27 control subjects at three other institutes on approval of the local institutional review boards. Dopamine transporter imaging served as the reference standard for the presence or absence of abnormalities of nigrosome 1 on SMwI. Diagnostic performance was compared between visual assessment by an experienced neuroradiologist and the developed deep learning-based diagnostic algorithm in both internal and external datasets using a bootstrapping method with 10000 re-samples by the “pROC” package of R (version 1.16.2).ResultsThe area under the receiver operating characteristics curve (AUC) (95% confidence interval [CI]) per participant by the bootstrap method was not significantly different between visual assessment and the deep learning-based algorithm (internal validation, .9622 [0.8912–1.0000] versus 0.9534 [0.8779-0.9956], P = .1511; external validation, 0.9367 [0.8843-0.9802] versus 0.9208 [0.8634-0.9693], P = .6267), indicative of a comparable performance to visual assessment.ConclusionsOur deep learning-based algorithm for assessing abnormalities of nigrosome 1 on SMwI was found to have a comparable performance to that of an experienced neuroradiologist.     

Influence of coronary stenosis location on diagnostic performance of machine learning-based fractional flow reserve from CT angiography

BackgroundCompared with invasive fractional flow reserve (FFR), coronary CT angiography (cCTA) is limited in detecting hemodynamically relevant lesions. cCTA-based FFR (CT-FFR) is an approach to overcome this insufficiency by use of computational fluid dynamics. Applying recent innovations in computer science, a machine learning (ML) method for CT-FFR derivation was introduced and showed improved diagnostic performance compared to cCTA alone. We sought to investigate the influence of stenosis location in the coronary artery system on the performance of ML-CT-FFR in a large, multicenter cohort.MethodsThree hundred and thirty patients (75.2% male, median age 63 years) with 502 coronary artery stenoses were included in this substudy of the MACHINE (Machine Learning Based CT Angiography Derived FFR: A Multi-Center Registry) registry. Correlation of ML-CT-FFR with the invasive reference standard FFR was assessed and pooled diagnostic performance of ML-CT-FFR and cCTA was determined separately for the following stenosis locations: RCA, LAD, LCX, proximal, middle, and distal vessel segments.ResultsML-CT-FFR correlated well with invasive FFR across the different stenosis locations. Per-lesion analysis revealed improved diagnostic accuracy of ML-CT-FFR compared with conventional cCTA for stenoses in the RCA (71.8% [95% confidence interval, 63.0%–79.5%] vs. 54.8% [45.7%–63.8%]), LAD (79.3 [73.9–84.0] vs. 59.6 [53.5–65.6]), LCX (84.1 [76.0–90.3] vs. 63.7 [54.1–72.6]), proximal (81.5 [74.6–87.1] vs. 63.8 [55.9–71.2]), middle (81.2 [75.7–85.9] vs. 59.4 [53.0–65.6]) and distal stenosis location (67.4 [57.0–76.6] vs. 51.6 [41.1–62.0]).ConclusionIn a multicenter cohort with high disease prevalence, ML-CT-FFR offered improved diagnostic performance over cCTA for detecting hemodynamically relevant stenoses regardless of their location.