Automated 3D Ιnterstitial Lung Disease Εxtent Quantification: Performance Evaluation and Correlation to PFTs

In this study, the performance of a recently proposed computer-aided diagnosis (CAD) scheme in detection and 3D quantification of reticular and ground glass pattern extent in chest computed tomography of interstitial lung disease (ILD) patients is evaluated. CAD scheme performance was evaluated on a dataset of 37 volumetric chest scans, considering five representative axial anatomical levels per scan. CAD scheme reliability analysis was performed by estimating agreement (intraclass correlation coefficient, ICC) of automatically derived ILD pattern extent to semi-quantitative disease extent assessment in terms of 29-point rating scale provided by two expert radiologists. Receiver operating characteristic (ROC) analysis was employed to assess CAD scheme accuracy in ILD pattern detection in terms of area under ROC curve (A_z). Correlation of reticular and ground glass volumetric pattern extent to pulmonary function tests (PFTs) was also investigated. CAD scheme reliability was substantial for ILD extent (ICC = 0.809) and distinct reticular pattern extent (0.806) and moderate for distinct ground glass pattern extent (0.543), performing within inter-observer agreement. CAD scheme demonstrated high accuracy in detecting total ILD (A_z = 0.950 ± 0.018), while accuracy in detecting distinct reticular and ground glass patterns was 0.920 ± 0.023 and 0.883 ± 0.024, respectively. Moderate and statistically significant negative correlation was found between reticular volumetric pattern extent and diffusing capacity, forced expiratory volume in 1 s, forced vital capacity, and total lung capacity (R = −0.581, −0.513, −0.494, and −0.446, respectively), similar to correlations found between radiologists’ semi-quantitative ratings with PFTs. CAD-based quantification of disease extent is in agreement with radiologists’ semi-quantitative assessment and correlates to specific PFTs, suggesting a potential imaging biomarker for ILD staging and management.     

Reliability of MRSI brain temperature mapping at 1.5 and 3 T

MRSI permits the non‐invasive mapping of brain temperature in vivo, but information regarding its reliability is lacking. We obtained MRSI data from 31 healthy male volunteers [age range, 22–40 years; mean ± standard deviation (SD), 30.5 ± 5.0 years]. Eleven subjects (age range, 23–40 years; mean ± SD, 30.5 ± 5.2 years) were invited to receive four point‐resolved spectroscopy MRSI scans on each of 3 days in both 1.5‐T (TR/TE = 1000/144 ms) and 3‐T (TR/TE = 1700/144 ms) clinical scanners; a further 20 subjects (age range, 22–40 years; mean ± SD, 30.5 ± 4.9 years) were scanned on a single occasion at 3 T. Data were fitted in the time domain to determine the water–N‐acetylaspartate chemical shift difference, from which the temperature was estimated. Temperature data were analysed using a linear mixed effects model to determine variance components and systematic temperature changes during the scanning sessions. To characterise the effects of instrumental drift on apparent MRSI brain temperature, a temperature‐controlled phantom was constructed and scanned on multiple occasions. Components of apparent in vivo temperature variability at 1.5 T/3 T caused by inter‐subject (0.18/0.17 °C), inter‐session (0.18/0.15 °C) and within‐session (0.36/0.14 °C) effects, as well as voxel‐to‐voxel variation (0.59/0.54 °C), were determined. There was a brain cooling effect during in vivo MRSI of 0.10 °C [95% confidence interval (CI): –0.110, –0.094 °C; p < 0.001] and 0.051 °C (95% CI: –0.054, –0.048 °C; p < 0.001) per scan at 1.5 T and 3 T, respectively, whereas phantom measurements revealed minimal drift in apparent MRSI temperature relative to fibre‐optic temperature measurements. The mean brain temperature at 3 T was weakly associated with aural (R = 0.55, p = 0.002) and oral (R = 0.62, p < 0.001) measurements of head temperature. In conclusion, the variability associated with MRSI brain temperature mapping was quantified. Repeatability was somewhat higher at 3 T than at 1.5 T, although subtle spatial and temporal variations in apparent temperature were demonstrated at both field strengths. Such data should assist in the efficient design of future clinical studies. © 2013 The Authors. NMR in Biomedicine published by John Wiley & Sons, Ltd.     

Clinical performance of the novel DiaSorin LIAISON® XL murex: HBsAg Quant,HCV-Ab,HIV-Ab/Ag assays

BackgroundThe fully automated and closed LIAISON^®XL platform was developed for reliable detection of infection markers like hepatitis B virus (HBV) surface antigen (HBsAg), hepatitis C virus (HCV) antibodies (Ab) or human immunodeficiency virus (HIV)-Ag/Ab. To date, less is known about the diagnostic performance of this system in direct comparison to the common Abbott ARCHITECT^® platform.ObjectivesWe compared the diagnostic performance and usability of the DiaSorin LIAISON^®XL with the commonly used Abbott ARCHITECT^® system.Study designThe qualitative performance of the above mentioned assays was compared in about 500 sera. Quantitative tests were performed for HBsAg-positive samples from patients under therapy (n = 289) and in vitro expressed mutants (n = 37). For HCV-Ab, a total number of 155 selected samples from patients chronically infected with different HCV genotypes were tested.ResultsThe concordance between both systems was 99.4% for HBsAg, 98.81% for HCV-Ab, and 99.6% for HIV-Ab/Ag. The quantitative LIAISON^®XL murex HBsAg assay detected all mutants in comparable amounts to the HBsAg wild type and yielded highly reliable HBsAg kinetics in patients treated with antiviral drugs. Dilution experiments using the 2nd International Standard for HBsAg (WHO) showed a high accuracy of this test. HCV-Ab from patients infected with genotypes 1–3 were equally detected in both systems. Interestingly, S/CO levels of HCV-Ab from patients infected with genotype 3 seem to be relatively low using both systems.ConclusionsThe LIAISON^®XL platform proved to be an excellent system for diagnostics of HBV, HCV, and HIV with equal performance compared to the ARCHITECT^® system.     

2011～2013年北京市部分二级以上医院收治患者的乙肝病毒感染情况分析

目的 了解北京市部分二级及以上医院住院及门诊收治病人的乙肝病毒感染情况,为制订乙肝防治策略提供科学数据.方法 以2011年1月至2013年12月期间在北京市部分二级及以上医院收治的用于检测HBV-DNA定量的3351例患者为研究对象,利用荧光定量PCR方法对研究对象的血清（血浆）标本进行HBV-DNA定量检测,并以结果大于5×10^2 IU/mL作为阳性判断标准,利用Excel的CHITEST函数分析资料完整样本的感染数据.结果 2011年至2013年,在北京部分二级及以上医院收治患者中,乙肝病毒的感染率为33.42％左右.男性感染者数量明显多于女性（P＜0.01）.感染者主要来自21 ～40岁年龄组（55.35％）;规范儿童疫苗接种后感染者有所减少,0～20岁年龄组阳性率为5.77％等.结论 从本文的检测结果看,HBV阳性率较高,对阳性人群应加强管理并用药控制病毒载量,对易感人群应加强保护预防感染.     

Quantification of glutamate and glutamine using constant‐time point‐resolved spectroscopy at 3 T

Separate quantification of glutamate (Glu) and glutamine (Gln) using conventional MRS on clinical scanners is challenging. In previous work, constant‐time point‐resolved spectroscopy (CT‐PRESS) was optimized at 3 T to detect Glu, but did not resolve Gln. To quantify Glu and Gln, a time‐domain basis set was constructed taking into account metabolite T₂ relaxation times and dephasing from B₀ inhomogeneity. Metabolite concentrations were estimated by fitting the basis one‐dimensional CT‐PRESS diagonal magnitude spectra to the measured spectrum. This method was first validated using seven custom‐built phantoms containing variable metabolite concentrations, and then applied to in vivo data acquired in rats exposed to vaporized ethanol and controls. Separate metabolite quantification revealed increased Gln after 16 weeks and increased Glu after 24 weeks of vaporized ethanol exposure in ethanol‐treated compared with control rats. Without separate quantification, the signal from the combined resonances of Glu and Gln (Glx) showed an increase at both 16 and 24 weeks in ethanol‐exposed rats, precluding the determination of the independent and differential contribution of each metabolite at each time. Copyright © 2012 John Wiley & Sons, Ltd.     

Thiol‐water proton exchange of glutathione,cysteine, and N‐acetylcysteine: Implications for CEST MRI

Amide‐, amine‐, and hydroxyl‐water proton exchange can generate MRI contrast through chemical exchange saturation transfer (CEST). In this study, we show that thiol‐water proton exchange can also generate quantifiable CEST effects under near‐physiological conditions (pH = 7.2 and 37°C) through the characterization of the pH dependence of thiol proton exchange in phosphate‐buffered solutions of glutathione, cysteine, and N‐acetylcysteine. The spontaneous, base‐catalyzed, and buffer‐catalyzed exchange contributions to the thiol exchange were analyzed. The thiol‐water proton exchange of glutathione and cysteine was found to be too fast to generate a CEST effect around neutral pH due to significant base catalysis. The thiol‐water proton exchange of N‐acetylcysteine was found to be much slower, yet still in the fast‐exchange regime with significant base and buffer catalysis, resulting in a 9.5% attenuation of the water signal at pH 7.2 in a slice‐selective CEST NMR experiment. Furthermore, the N‐acetylcysteine thiol CEST was also detectable in human serum albumin and agarose phantoms.     

Antilogic,a new supervised machine learning software for the automatic interpretation of antibiotic susceptibility testing in clinical microbiology: proof-of-concept on three frequently isolated bacterial species

ObjectiveAntibiotic susceptibility testing (AST) is necessary in order to adjust empirical antibiotic treatment, but the interpretation of results requires experience and knowledge. We have developed a machine learning software that is capable of reading AST images without any human intervention and that automatically interprets the AST, based on a database of antibiograms that have been clinically validated with European Committee on Antimicrobial Susceptibility Testing rules.MethodsWe built a database of antibiograms that were labelled by senior microbiologists for three species: Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. We then developed Antilogic, a Python software based on an original image segmentation module and supervised learning models that we trained against the database. Finally, we blind tested Antilogic against a validation set of 5100 photos of antibiograms.ResultsWe trained Antilogic against a database of 18072 pictures of antibiograms. Overall agreement against the validation set reached 97% (16 855/17 281) regarding phenotypes. The severity rate of errors was also evaluated: 1.66% (287/17 281) were major errors and 0.80% (136/17 281) were very major errors. After implementation of uncertainty quantifications, the rate of errors decreased to 0.80% (114/13 451) and 0.42% (51/13 451) for major and very major errors respectively.DiscussionAntilogic is the first machine learning software that has been developed for AST interpretation. It is based on a novel approach that differs from the typical diameter measurement and expert system approach. Antilogic is a proof of concept that artificial intelligence can contribute to faster and easier diagnostic methods in the field of clinical microbiology.     

Quantification of metabolites in breast cancer patients with different clinical prognosis using HR MAS MR spectroscopy

Absolute quantitative measures of breast cancer tissue metabolites can increase our understanding of biological processes. Electronic REference To access In vivo Concentrations (ERETIC) was applied to high resolution magic angle spinning MR spectroscopy (HR MAS MRS) to quantify metabolites in intact breast cancer samples. The ERETIC signal was calibrated using solutions of creatine and TSP. The largest relative errors of the ERETIC method were 8.4%, compared to 4.4% for the HR MAS MRS method using TSP as a standard. The same MR experimental procedure was applied to intact tissue samples from breast cancer patients with clinically defined good (n = 13) and poor (n = 16) prognosis. All samples were examined by histopathology for relative content of different tissue types and proliferation index (MIB‐1) after MR analysis. The resulting spectra were analyzed by quantification of tissue metabolites (β‐glucose, lactate, glycine, myo‐inositol, taurine, glycerophosphocholine, phosphocholine, choline and creatine), by peak area ratios and by principal component analysis. We found a trend toward lower concentrations of glycine in patients with good prognosis (1.1 µmol/g) compared to patients with poor prognosis (1.9 µmol/g, p = 0.067). Tissue metabolite concentrations (except for β‐glucose) were also found to correlate to the fraction of tumor, connective, fat or glandular tissue by Pearson correlation analysis. Tissue concentrations of β‐glucose correlated to proliferation index (MIB‐1) with a negative correlation factor (?0.45, p = 0.015), consistent with increased energy demand in proliferating tumor cells. By analyzing several metabolites simultaneously, either in ratios or by metabolic profiles analyzed by PCA, we found that tissue metabolites correlate to patients' prognoses and health status five years after surgery. This study shows that the diagnostic and prognostic potential in MR metabolite analysis of breast cancer tissue is greater when combining multiple metabolites (MR Metabolomics). Copyright © 2010 John Wiley & Sons, Ltd.