Semi-quantification Methods Research Articles

Assessing overall oxidative metabolism of high-metabolic-rate abdominal organs in diet-induced obesity using compartmental modeling of [11C]acetate-PET

[11C]Acetate PET imaging is a powerful tool for visualizing and studying metabolic processes in various tissues and organs, particularly in the context of heart disease and prostate cancer. The tracer can reflect tissue perfusion, oxidative metabolism, and fatty acid synthesis[1]. Male Sprague Dawley rats on standard (SD n=6) and after 8-10 weeks on a high-fat diet (HFD n=6) were studied for [11C]acetate turnover. Longitudinal utilization of the tracer in the abdomen was recorded using Siemens Inveon µ-PET/CT. Under isoflurane anesthesia, rats underwent 60 min dynamic [11C]acetate-PET scans and additionally on the next week venous blood sampling for [11C]CO2-metabolite analysis (7-time points; SD n=3; HFD n=3) accordingly[2]. Delineation of kidneys, myocardium and liver; SUV calculations; 1-tissue compartmental modeling with blood-to-plasma ratio and [11C]CO2-metabolite correction were performed in PMOD3.8; clearance rate was calculated in Carimas2.1. Semi-quantification methods demonstrated contrasting outcomes between the groups. Increased renal (right p=0.04, left p=0.03) and myocardial (p=0.01) uptake (Fig.A) was observed in the HFD group, while clearance rates in kidneys (right p=0.01, left p=0.04) and liver (p=0.03) were decreased (Fig.B). Blood sampling for [11C]CO2-metabolite analysis revealed differing profiles between the groups, with a maximum peak at 30 min in SD and two peaks at 5 and 40 min in HFD animals (Fig.C). Deeper analysis using 1-tissue compartmental modeling shed light on the hepatic uptake and showed a difference in the volume of distribution (Vt), which was significantly decreased in the HFD group (p<0.001) (Fig.D). The study indicates [11C]acetate accumulation in metabolically active organs: kidneys, heart, and liver. Compartmental modeling demonstrated the necessity of metabolite correction for quantitative analysis. Previously, Sprague Dawley rats on HFD were studied for fatty acid uptake and decline of VLDL synthesis[3] that can correlate with the variation of Vt in the liver and confirm pathological changes in organ functioning. Please click on the 'PDF' for the full abstract!

Understanding nanoparticle endocytosis to improve targeting strategies in nanomedicine.

Nanoparticles (NPs) have attracted considerable attention in various fields, such as cosmetics, the food industry, material design, and nanomedicine. In particular, the fast-moving field of nanomedicine takes advantage of features of NPs for the detection and treatment of different types of cancer, fibrosis, inflammation, arthritis as well as neurodegenerative and gastrointestinal diseases. To this end, a detailed understanding of the NP uptake mechanisms by cells and intracellular localization is essential for safe and efficient therapeutic applications. In the first part of this review, we describe the several endocytic pathways involved in the internalization of NPs and we discuss the impact of the physicochemical properties of NPs on this process. In addition, the potential challenges of using various inhibitors, endocytic markers and genetic approaches to study endocytosis are addressed along with the principal (semi) quantification methods of NP uptake. The second part focuses on synthetic and bio-inspired substances, which can stimulate or decrease the cellular uptake of NPs. This approach could be interesting in nanomedicine where a high accumulation of drugs in the target cells is desirable and clearance by immune cells is to be avoided. This review contributes to an improved understanding of NP endocytic pathways and reveals potential substances, which can be used in nanomedicine to improve NP delivery.

A kinetics-based approach to amyloid PET semi-quantification.

To develop and validate a semi-quantification method (time-delayed ratio, TDr) applied to amyloid PET scans, based on tracer kinetics information. The TDr method requires two static scans per subject: one early (~ 0-10min after the injection) and one late (typically 50-70min or 90-100min after the injection, depending on the tracer). High perfusion regions are delineated on the early scan and applied onto the late scan. A SUVr-like ratio is calculated between the average intensities in the high perfusion regions and the late scan hotspot. TDr was applied to a naturalistic multicenter dataset of 143 subjects acquired with [18F]florbetapir. TDr values are compared to visual evaluation, cortical-cerebellar SUVr, and to the geometrical semi-quantification method ELBA. All three methods are gauged versus the heterogeneity of the dataset. TDr shows excellent agreement with respect to the binary visual assessment (AUC = 0.99) and significantly correlates with both validated semi-quantification methods, reaching a Pearson correlation coefficient of 0.86 with respect to ELBA. TDr is an alternative approach to previously validated ones (SUVr and ELBA). It requires minimal image processing; it is independent on predefined regions of interest and does not require MR registration. Besides, it takes advantage on the availability of early scans which are becoming common practice while imposing a negligible added patient discomfort.

Semi-quantitative Determination of Protein Expression using Immunohistochemistry Staining and Analysis: An Integrated Protocol.

Semi-quantitative IHC is a powerful method for investigating protein expression and localization within tissues. The semi-quantitative immunohistochemistry (IHC) involves using software such as free software ImageJ Fiji to conduct deconvolution and downstream analysis. Currently, there is lack of an integrated protocol that includes a detailed procedure of how to measure or compare protein expression. Publications that use semi-quantification methods to quantify protein expression often don't provide enough details in their methods section, which makes it difficult for the reader to reproduce their data. The current protocol for the first time provides a detailed, step-by-step instruction of conducting semi-quantitative analysis of IHC images using ImageJ Fiji software so that researchers would be able to follow this single protocol to conduct their research. The protocol uses semi-quantitative IHC of organic anion transporting polypeptide (OATP1B1) as an example, and gives detailed steps on how to deconvolute IHC images stained with hematoxylin and 3, 3 - diaminobenzidine (DAB) and how to quantify their expression using ImageJ Fiji. The protocol includes clear steps for a reader so that this method can be applied to many different proteins. We anticipate this method will provide a practical guidance to the reader and make semi-quantification of proteins an easier task to include in publications.

Semi-quantification and grading of amyloid PET: A project of the European Alzheimer's Disease Consortium (EADC).

Backgroundamyloid-PET reading has been classically implemented as a binary assessment, although the clinical experience has shown that the number of borderline cases is non negligible not only in epidemiological studies of asymptomatic subjects but also in naturalistic groups of symptomatic patients attending memory clinics. In this work we develop a model to compare and integrate visual reading with two independent semi-quantification methods in order to obtain a tracer-independent multi-parametric evaluation.MethodsWe retrospectively enrolled three cohorts of cognitively impaired patients submitted to 18F-florbetaben (53 subjects), 18F-flutemetamol (62 subjects), 18F-florbetapir (60 subjects) PET/CT respectively, in 6 European centres belonging to the EADC. The 175 scans were visually classified as positive/negative following approved criteria and further classified with a 5-step grading as negative, mild negative, borderline, mild positive, positive by 5 independent readers, blind to clinical data. Scan quality was also visually assessed and recorded. Semi-quantification was based on two quantifiers: the standardized uptake value (SUVr) and the ELBA method. We used a sigmoid model to relate the grading with the quantifiers.We measured the readers accord and inconsistencies in the visual assessment as well as the relationship between discrepancies on the grading and semi-quantifications.ConclusionIt is possible to construct a map between different tracers and different quantification methods without resorting to ad-hoc acquired cases. We used a 5-level visual scale which, together with a mathematical model, delivered cut-offs and transition regions on tracers that are (largely) independent from the population. All fluorinated tracers appeared to have the same contrast and discrimination ability with respect to the negative-to-positive grading. We validated the integration of both visual reading and different quantifiers in a more robust framework thus bridging the gap between a binary and a user-independent continuous scale.

A comparison of semi-quantitative methods suitable for establishing volatile profiles

BackgroundFull scent profiles emitted by living tissues can be screened by using total ion chromatograms generated in full scan mode and gas chromatography–mass spectrometry technique using Headspace Sorptive Extraction. This allows the identification of specific compounds and their absolute quantification or relative abundance. Quantifications ideally should be based on calibration curves using standards for each compound. However, the unpredictable composition of Volatile Organic Compounds (VOCs) and lack of standards make this approach difficult. Researchers studying scent profiles therefore concentrate on identifying specific scent footprints i.e. relative abundance rather than absolute quantities. We compared several semi-quantitative methods: external calibration curves generated in the sampling system and by liquid addition of standards to stir bars, total integrated peak area per fresh weight (FW), normalized peak area per FW, semi-quantification based on internal standard abundance, semi-quantification based on the nearest n-alkane and percentage of emission. Furthermore, we explored the usage of nearest components and single calibrators for semi-quantifications.ResultsAny of the semi-quantification methods based on a standard produced similar or even identical results compared to quantification by a true-standard for a compound, except for the method based on standard addition. Each method beholds advantages and disadvantages regarding level of accuracy, experimental variability, acceptance and retrieved quantities.ConclusionsOur data shows that, except for the method of standard addition to the biological sample, the rest of the semi-quantification methods studied give highly similar statistical results. Any of the methodologies presented here can therefore be considered as valid for scent profiling. Regarding relative proportions of VOCs, the generation of calibration curves for each compound analysed is not necessary.

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

BackgroundSemi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified.This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features:Voxel intensitiesPrincipal components of image voxel intensitiesStriatal binding radios from the putamen and caudate.Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods:Minimum of age-matched controlsMean minus 1/1.5/2 standard deviations from age-matched controlsLinear regression of normal patient data against age (minus 1/1.5/2 standard errors)Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training dataEach machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times.ResultsThe mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively.ConclusionsClassification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context.

Semi-quantification image methods for assessing severity of scleroderma-associated interstitial lung disease according to computed tomography data

The aim of this study was to compare three semi-quantification scales for prospective assessment of scleroderma-associated interstitial lung disease (SS-ILD) severity. Methods. From 110 prospectively followed patients with SS-ILD, we selected 12 patients (mean age, 42 ± 13 years, 11 females) with obvious improvement (n = 6) or worsening (n = 6) of lung lesions on high resolution computed tomography (HRCT) during a year. The patients had diffuse (n = 7) or limited (n = 7) SS with mean length of the disease of 8.5 ± 6.7 years (range, 1 to 23 years). HRCT was done at baseline (inclusion in the study) and in a year. CT scans were quantitatively assessed by four radiologists including one experienced radiologist. A blinded analysis of HRCT scans was done using three scales: J.H.Warrick et al. (1991), A.U.Wells et al. (1997), and E.A.Kazerooni et al. (1997). The intraclass correlation coefficient (ICC) was calculated to evaluate the assessment reliability. T-test for independent samples was used to evaluate reproducibility of the assessments. Agreement between independent experts' opinions was evaluated using Kendall's rank correlation coefficient. Results. The measurements were significantly divergent between the radiologists, both for the baseline and the follow-up HRCT scans. ICCs for investigated radiological parameters were 0.56 to 0.76. The highest ICC (0.76) was obtained for A.U.Wells' scale. All scales used to assess HRCT scans had lower interoperator reproducibility. Conclusion. Combined use of currently available semi-quantification methods for follow-up assessment of HRCT in SS-ILD patients allowed thorough qualitative evaluation of lung lesions, but the reliability of the radiological parameters in detecting 1-year fibrosis progression in SS patients was low. The risk of significant interoperator bias limited the use of the radiological parameters in clinical trials of SS-ILD patients.

Metabolic spatial connectivity in amyotrophic lateral sclerosis as revealed by independent component analysis.

Positron emission tomography (PET) and volume of interest (VOI) analysis have recently shown in amyotrophic lateral sclerosis (ALS) an accuracy of 93% in differentiating patients from controls. The aim of this study was to disclose by spatial independent component analysis (ICA) the brain networks involved in ALS pathological processes and evaluate their discriminative value in separating patients from controls. Two hundred fifty-nine ALS patients and 40 age- and sex-matched control subjects underwent brain 18F-2-fluoro-2-deoxy-D-glucose PET (FDG-PET). Spatial ICA of the preprocessed FDG-PET images was performed. Intensity values were converted to z-scores and binary masks were used as data-driven VOIs. The accuracy of this classifier was tested versus a validated system processing intensity signals in 27 brain meta-VOIs. A support vector machine was independently applied to both datasets and the 'leave-one-out' technique verified the general validity of results. The 8 components selected as pathophysiologically meaningful discriminated patients from controls with 99.0% accuracy, the discriminating value of bilateral cerebellum/midbrain alone representing 96.3%. Among the meta-VOIs, right temporal lobe alone reached an accuracy of 93.7%. Spatial ICA identified in a very large cohort of ALS patients distinct spatial networks showing a high discriminatory value, improving substantially on the previously obtained accuracy. The cerebellar/midbrain component accounted for the highest accuracy in separating ALS patients from controls. Spatial ICA and multivariate analysis perform better than univariate semi-quantification methods in identifying the neurodegenerative features of ALS and pave the way for inclusion of PET in clinical trials and early diagnosis.

Radius dependence of FP-CIT quantification: a Monte Carlo-based simulation study

Dopamine transporter imaging with SPECT is a valuable tool for both clinical routine and research studies. Semi-quantitative analysis plays a key role in interpreting the scans, but is dependent on numerous factors, rotational radius being one of them. This study systematically evaluates the potential influence of radius of rotation on apparent tracer binding and describes methods for correction. Monte Carlo simulation scans of a digital brain phantom with various disease states and various radii of rotation ranging from 13 to 30 cm were analyzed using 4 different methods of semi-quantification. Different volumes of interest as well as a method with partial volume correction were applied. For conventional 3D semi-quantification methods the decrease of measured striatal binding per cm additional radius rotation lied in the range between 2.5 and 3.1 %, whereas effects were negligible when applying recovery-corrected quantification. Effects were independent of disease state. Partial volume effects with increasing radius of rotation can lead to considerable decrease of measured binding ratios, particularly when applying dopamine transporter imaging in a research setting. Standardization of acquisition radius can avoid the effect; correction seems feasible, but the correction factors depend on the quantification approach applied.

SPECT imaging evaluation in movement disorders: far beyond visual assessment

Single photon emission computed tomography (SPECT) imaging with (123)I-FP-CIT is of great value in differentiating patients suffering from Parkinson's disease (PD) from those suffering from essential tremor (ET). Moreover, SPECT with (123)I-IBZM can differentiate PD from Parkinson's "plus" syndromes. Diagnosis is still mainly based on experienced observers' visual assessment of the resulting images while many quantitative methods have been developed in order to assist diagnosis since the early days of neuroimaging. The aim of this work is to attempt to categorize, briefly present and comment on a number of semi-quantification methods used in nuclear medicine neuroimaging. Various arithmetic indices have been introduced with region of interest (ROI) manual drawing methods giving their place to automated procedures, while advancing computer technology has allowed automated image registration, fusion and segmentation to bring quantification closer to the final diagnosis based on the whole of the patient's examinations results, clinical condition and response to therapy. The search for absolute quantification has passed through neuroreceptor quantification models, which are invasive methods that involve tracer kinetic modelling and arterial blood sampling, a practice that is not commonly used in a clinical environment. On the other hand, semi-quantification methods relying on computers and dedicated software try to elicit numerical information out of SPECT images. The application of semi-quantification methods aims at separating the different patient categories solving the main problem of finding the uptake in the structures of interest. The semi-quantification methods which were studied fall roughly into three categories, which are described as classic methods, advanced automated methods and pixel-based statistical analysis methods. All these methods can be further divided into various subcategories. The plethora of the existing semi-quantitative methods reinforces the feeling that visual assessment is still the base of image interpretation and that the unambiguous numerical results that will allow the absolute differentiation between the known diseases have not been standardized yet. Switching to a commonly agreed-ideally PC-based-automated software that may take raw or mildly processed data (checked for consistency and maybe corrected for attenuation and/or scatter and septal penetration) as input, work with basic operator's inference and produce validated numerical results that will support the diagnosis is in our view the aim towards which efforts should be directed. After all, semi-quantification can improve sensitivity, strengthen diagnosis, aid patient's follow-up and assess the response to therapy. Objective diagnosis, altered diagnosis in marginal cases and a common approach to multicentre trials are other benefits and future applications of semi-quantification.

Identification of N-homocysteinylated apolipoprotein AI in normal human serum

In human serum, a portion of homocysteine (Hcy) exists as an N-linked form to the epsilon-amino group of protein lysine residues. N-homocysteinylated proteins differ structurally and functionally from native proteins. The present study strives to develop detection and potential semi-quantification methods for N-homocysteinylated apolipoprotein AI (N-Hcy-apoAI) in human serum. Serum treated with or without cysteamine was supplied to isoelectric focusing (IEF) followed by an immunoblot using an anti-apoAI antibody. Cysteamine treatment increased the isoelectric point for N-Hcy-apoAI, but not for unmodified apoAI, due to the presence of -SH group(s) derived from Hcy and the absence of a cysteine residue in the apoAI molecule. N-Hcy-apoAI was semi-quantified from the scanned immunoblot pattern via a computer. After cysteamine treatment, N-Hcy-apoAI in the serum was identified by IEF at the position with a higher pI value compared with intact apoAI. The reproducibility (between assays) of the semi-quantification method was 19.1% CV (coefficient of variation) for an average ratio 5.9% of N-Hcy-apoAI to the whole apoAI in the serum. Approximately 1.0-7.4% of apoAI was N-homocysteinylated in the serum obtained from 27 healthy subjects. Neither the ratio of N-Hcy-apoAI nor its concentration, calculated by total apoAI concentration, indicated correlation with the so-called total (free and S-linked) Hcy concentration. We directly found that a portion of apoAI in the serum undergoes homocysteinylation in an N-linkage manner, and used this to develop a potential semi-quantification method for N-Hcy-apoAI.