Quantitative Extraction Research Articles

Comprehensive analysis of phenolic compounds from natural products: Integrating sample preparation and analysis

The comprehensive analysis of phenolic compounds from natural products comprises critical steps, including quantitative extraction, extract preparation, and chromatographic procedure. Performing these steps off-line requires a long time to obtain results, besides being laborious and more error-prone. This work discusses the concept and presents the details of assembling and validating a new system to comprehensively analyze phenolic compounds in natural products. The system is based on a bidimensional separation through the combination of pressurized liquid extraction with in-line solid-phase extraction coupled online with HPLC-PDA. The system proved to be able to perform a bidimensional separation to characterize the sample and ensure quantitative extraction of all detected components using the most appropriate extraction solvent gradient depending on the raw sample analyzed. The 1st dimension separation is achieved by PLE-SPE with a solvent gradient and differential interactions of extracted compounds with the adsorbent. The 2nd dimension presents the HPLC-PDA separation. The extraction/separation process can be monitored in real-time, and kinetic extraction curves for individual compounds can also be obtained to ensure quantitative extraction. Thus, the 2D PLE-SPE × HPLC-PDA may provide fast and precise comprehensive analyses of a large plethora of phenolic compounds, finding relevant applications in the chemical, food, pharmaceutical, and agricultural fields.

Emerging applications of radiomics in rectal cancer: State of the art and future perspectives

Rectal cancer (RC) is the third most commonly diagnosed cancer and has a high risk of mortality, although overall survival rates have improved. Preoperative assessments and predictions, including risk stratification, responses to therapy, long-term clinical outcomes, and gene mutation status, are crucial to guide the optimization of personalized treatment strategies. Radiomics is a novel approach that enables the evaluation of the heterogeneity and biological behavior of tumors by quantitative extraction of features from medical imaging. As these extracted features cannot be captured by visual inspection, the field holds significant promise. Recent studies have proved the rapid development of radiomics and validated its diagnostic and predictive efficacy. Nonetheless, existing radiomics research on RC is highly heterogeneous due to challenges in workflow standardization and limitations of objective cohort conditions. Here, we present a summary of existing research based on computed tomography and magnetic resonance imaging. We highlight the most salient issues in the field of radiomics and analyze the most urgent problems that require resolution. Our review provides a cutting-edge view of the use of radiomics to detect and evaluate RC, and will benefit researchers dedicated to using this state-of-the-art technology in the era of precision medicine.

Polydopamine-functionalised biosilica derived from diatomite as a porous composite for effective solid phase extraction of heavy metal ions from aqueous samples

ABSTRACT This study aimed to synthesise and apply polydopamine-functionalised biosilica(BS@PDA), derived from diatomite, as a highly porous and biodegradable composite for efficient adsorption of Cu(II), Co(II), Pb(II), and Zn(II) ions from different types of aqueous matrices. The self-polymerisation of dopamine monomers on the treated diatomite’s surface and internal pores was conducted in an aqueous medium at pH = 8.5. The adsorbent’s structural characterisation was evaluated using FT-IR, SEM, EDX, TEM, BET, and BJH confirming a successful synthesis. The effects of critical parameters influencing the quantitative extraction and determination of analytes were optimised. The effects of interfering ions commonly found in industrial and environmental waters were also examined, indicating no severe interference in the extraction process. The calibration curves in the range of 10–500 ng/mL (r ≥ 0.9960) were linear. The relative standard deviations (n = 5) at two different concentration levels were in the range of 2.1–6.2%. The limits of detection of analytes ranged from 0.24 to 1.25 ng/mL. The bio-adsorbent ability to extract the studied analytes was tested in the industrial wastewater, river water, and seawater samples. The recoveries of the spiked analytes in the real samples were above 94%. The results indicated the comparable capacity of the proposed method for both removal and determination of heavy metals in industrial and environmental water samples.

A new method for highly efficient separation and determination of arsenic species in natural water using silica modified with polyamines

A simple and highly efficient method for the determination of highly toxic arsenic species using non-covalently aminated silica is proposed. The polyamines including poly(hexamethyleneguanidine), poly(4,9-dioxadodecane-1,12-guanidine), hexadimethrine, and poly(diallyldimethylammonium) were tested as silica modifiers. The prepared adsorbents allow effective preconcentration of anionic species of arsenic from aqueous solutions. It was found that As(V) can be quantitatively extracted from solutions at pH 4.5–7.0 by the anion exchange mechanism in less than 5 min, while neutral at this pH As(III) was not adsorbed at these conditions. A reaction with 2,3-dimercapto-1-propanesulphonic acid, which resulted in the formation of the negatively charged complex of As(III) with adsorbents was used for its quantitative extraction from solutions with a pH of 3.5–6.5. A system of two cartridges filled with poly(diallyldimethylammonium) modified silica and the on-line reaction of As(III) with 2,3-dimercapto-1-propanesulphonic acid proceeding between the cartridges was used for separate preconcentration and determination of As(V) and As(III) at pH 5. The proposed method was used for four-year monitoring of natural water pollution by arsenic in the area of residence of the indigenous peoples of Tyva Republic (Russia).

Allosteric activation of Tcell antigen receptor signaling by quaternary structure relaxation.

SummaryThe mechanism of T cell antigen receptor (TCR-CD3) signaling remains elusive. Here, we identify mutations in the transmembrane region of TCRβ or CD3ζ that augment peptide T cell antigen receptor (pMHC)-induced signaling not explicable by enhanced ligand binding, lateral diffusion, clustering, or co-receptor function. Using a biochemical assay and molecular dynamics simulation, we demonstrate that the gain-of-function mutations loosen the interaction between TCRαβ and CD3ζ. Similar to the activating mutations, pMHC binding reduces TCRαβ cohesion with CD3ζ. This event occurs prior to CD3ζ phosphorylation and at 0°C. Moreover, we demonstrate that soluble monovalent pMHC alone induces signaling and reduces TCRαβ cohesion with CD3ζ in membrane-bound or solubilised TCR-CD3. Our data provide compelling evidence that pMHC binding suffices to activate allosteric changes propagating from TCRαβ to the CD3 subunits, reconfiguring interchain transmembrane region interactions. These dynamic modifications could change the arrangement of TCR-CD3 boundary lipids to license CD3ζ phosphorylation and initiate signal propagation.

Attention Multi-Scale Network for Automatic Layer Extraction of Ice Radar Topological Sequences

Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly provide quantitative, objective and reliable extraction of information from radargrams. Most traditional handcrafted methods, designed to detect ice-surface and ice-bed layers from ice sheet radargrams, require complex human involvement and are difficult to apply to large datasets, while deep learning methods can obtain better results in a generalized way. In this study, an end-to-end multi-scale attention network (MsANet) is proposed to realize the estimation and reconstruction of layers in sequences of ice sheet radar tomographic images. First, we use an improved 3D convolutional network, C3D-M, whose first full connection layer is replaced by a convolution unit to better maintain the spatial relativity of ice layer features, as the backbone. Then, an adjustable multi-scale module uses different scale filters to learn scale information to enhance the feature extraction capabilities of the network. Finally, an attention module extended to 3D space removes a redundant bottleneck unit to better fuse and refine ice layer features. Radar sequential images collected by the Center of Remote Sensing of Ice Sheets in 2014 are used as training and testing data. Compared with state-of-the-art deep learning methods, the MsANet shows a 10% reduction (2.14 pixels) on the measurement of average mean absolute column-wise error for detecting the ice-surface and ice-bottom layers, runs faster and uses approximately 12 million fewer parameters.

Structural designs, principles, and applications of thin-walled membrane and plate-type acoustic/elastic metamaterials

Many advanced physical properties can be realized by using well-designed acoustic metamaterial (AM) structures, which have significant application value in engineering. In particular, thin-walled membrane, plate, and shell-type structures with deep subwavelength thicknesses that can meet light weight requirements have attracted the attention of many researchers and engineers from various specialized fields. This Tutorial systematically introduced the structural design methods, acoustic/elastic wave attenuation and regulation principles, and engineering applications of thin-walled AMs for low-frequency sound insulation, sound absorption, and vibration reduction. In particular, the design methods and sound insulation/absorption properties of thin-walled AMs for realizing narrow-band and broadband sound attenuation were explored. Furthermore, the local resonance bandgap characteristics, quantitative extraction method for the bending wave bandgap, vibration suppression properties, and the design method for local resonance vibration dampers for elastic wave regulation by thin-walled elastic metamaterials were summarized successively. Moreover, other thin-walled AM applications, such as the wavefront steering performance of thin-walled acoustic/elastic metasurfaces, and the active thin-walled AMs, were introduced as well.

Extraction of medicinal compounds of the benzodiazepines class from aqueous solutions and their following gas chromatographic determination

In extraction systems hexane–water, hexane–aqueous solutions of inorganic salts (sodium chloride, dipotassium phosphate and potassium carbonate), chloroform–water using the method of gas chromatography–mass spectrometry, the distribution constants of psychotropic substances, limited to movement through the customs border of the Republic of Belarus – alprazolam, lorazepam, temazepam, and as well as clozapine, a medicinal product permitted for movement, were determined. The greatest dynamics of the growth of the distribution constant versus salt concentration was observed in alprazolam. It was found that replacing hexane with chloroform leads to increasing the distribution constants and the quantitative extraction of all substances. However, for the selective extraction of substances from various objects and the simultaneous removal of matrix components, it is necessary to use the systems as hexane–aqueous solutions of salts or hexane–water. Based on the obtained values of the distribution constants of substances, extraction-chromatographic technique for the determination of organic non­electrolytes (benzodiazepines) in various objects is proposed. Metrological characteristics of the techniques are: relative standard deviation does not exceed 2 %, the range of determined concentrations in solutions is 0,05– 1,00 g/dm3.

Optimizing sulfate pyrolysis triple oxygen isotope analysis for samples from desert environments.

The triple oxygen isotope composition of sulfate may reveal the formation pathway and depositional sources and may indicate slow biologic cycling in the environment. Pyrolysis mass spectrometry is better suited for large sample workloads during environmental profiling but sufficient precision and a thorough verification of accuracy are required for comparison with higher precision laser fluorination data. Quantitative sulfate extraction from soil samples at neutral pH, purification, conversation into Ag-sulfate, and pyrolysis mass spectrometry were modified for high sample throughput. Samples were analyzed after pyrolysis in quartz cups and gold capsules in a modified EuroVector model 3000 elemental analyzer. Sample O2 was measured in continuous He-flow after purification by cryo-trapping and chromatography on a Thermo Finnigan MAT253 isotope ratio mass spectrometer. A protocol for routine quality control and data normalization ensures long-term accuracy of the pyrolysis method. The 1σ SD external reproducibility is 0.12‰ for Δ17 OSO4 values on 30 μmol samples. Careful normalization for a daily analytical session accounts for changing pyrolysis conditions over the course of multiple sessions. The precision and accuracy obtained with quartz cups are comparable with those obtained with gold capsules. Pyrolysis and fluorination data for in-house standards from four laboratories and from an Atacama Desert gypsum-soil profile are identical and demonstrate the accuracy of our simplified method. Pyrolysis of sulfate in quartz cups and a modified simple elemental analyzer setup allows for accurate, precise, fast, cost-efficient, and non-hazardous mass spectrometric analysis. Exchangeability of data from pyrolysis and laser fluorination methods was demonstrated by repeat analysis of standards and natural samples despite high contents of interfering, easily soluble nitrates and chlorides.

AutoSmarTrace: Automated chain tracing and flexibility analysis of biological filaments

Single-molecule imaging is widely used to determine statistical distributions of molecular properties. One such characteristic is the bending flexibility of biological filaments, which can be parameterized via the persistence length. Quantitative extraction of persistence length from images of individual filaments requires both the ability to trace the backbone of the chains in the images and sufficient chain statistics to accurately assess the persistence length. Chain tracing can be a tedious task, performed manually or using algorithms that require user input and/or supervision. Such interventions have the potential to introduce user-dependent bias into the chain selection and tracing. Here, we introduce a fully automated algorithm for chain tracing and determination of persistence lengths. Dubbed “AutoSmarTrace,” the algorithm is built off a neural network, trained via machine learning to identify filaments within images recorded using atomic force microscopy. We validate the performance of AutoSmarTrace on simulated images with widely varying levels of noise, demonstrating its ability to return persistence lengths in agreement with input simulation parameters. Persistence lengths returned from analysis of experimental images of collagen and DNA agree with previous values obtained from these images with different chain-tracing approaches. Although trained on atomic-force-microscopy-like images, the algorithm also shows promise to identify chains in other single-molecule imaging approaches, such as rotary-shadowing electron microscopy and fluorescence imaging.

Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations.

Simple SummaryDifferentiated thyroid cancer (DTC) is the most common endocrine malignancy with a high incidence rate in females. The COVID-19 epidemic posed an increased risk of treatment delay causing increased DTC morbidity and mortality rate of DTC. Several imaging techniques, including ultrasound (US), magnetic resonance imaging (MRI), and computer tomography (CT), have been applied in the early screening and diagnosis of DTC. However, these traditional methods have limited sensitivity and specificity due to dependence on the experience and skill of the radiologists.Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.

A novel approach to discriminate transgenic soybean seeds based on terahertz spectroscopy

In qualitative and quantitative terahertz(THz)spectroscopic analyses, reduction and feature extraction of original spectral data are important steps. Due to the parameters, sample preparation, and experimental conditions used in THz time-domain spectroscopy (THz-TDS), the sample absorption lines present different degrees of oscillation and contain certain background noise; therefore spectral data dimension reduction is necessary. Since the existing traditional algorithms, such as principal component analysis, cannot extract useful information from signals, an improved spectral feature extraction method is proposed based on geodesic distance nonlinear reduction and partial least squares regression. Three kinds of transmission spectra of transgenic soybeans are obtained in this experiment. To extract the useful information from the spectral data, principal component analysis (PCA), a locally linear embedding (LLE) algorithm and Floyd's improved LLE algorithm (FLLE) are applied. Multiple linear regression analysis (MLR) and partial least squares regression analysis (PLSR) are performed on the reduced dimensional spectral data. The root mean square error (RMSE) of the FLLE-PLSR algorithm is 0.0079, and the determination coefficient(R) is 0.9966, which are obviously better than those of the PCA-MLR, LLE-MLR, FLLE-MLR, PCA-PLSR and LLE-PLSR algorithms. The proposed method can effectively extract characteristic quantities from the THz spectrum data of transgenic soybeans that have broad application value in agricultural security and food supervision.

Modeling cyclic voltammetry during solid electrolyte interphase formation: Baseline scenario of a dynamically evolving tunneling barrier resulting from a homogeneous single-phase insulating film.

The solid electrolyte interphase (SEI) is an insulating film on anode surfaces in Li-ion batteries, which forms via the reaction of Li ions with reduced electrolyte species. The SEI leads to a reduction in the electrochemical current in heterogeneous electrochemical redox reactions at the electrode/electrolyte interface. Hence, the growth of the SEI is, in principle, self-limited. Toward our ultimate goal of an improved understanding of SEI formation, we develop a baseline quantitative model within Butler-Volmer electrode kinetics, which describes the cyclic voltammetry (CV) of a flat macroelectrode during SEI growth. Here, the SEI building up electrochemically during CV forms a homogeneous single-phase electronically insulating thin film due to the corresponding current. The model is based on a dynamically evolving electron tunneling barrier with increasing film thickness. Our objective is to provide a framework, which allows for both the qualitative, intuitive interpretation of characteristic features of CV measurements and the quantitative extraction of physicochemical parameters via model fitting. We also discuss the limitations of the baseline model and give a brief outlook for improvements. Finally, comparisons to exemplary CVs from the literature relevant to Li-ion battery science are presented.

A green miniaturized aqueous biphasic system prepared with cholinium chloride and a phosphate salt to extract and preconcentrate personal care products in wastewater samples

A miniaturized extraction/preconcentration method based on an aqueous biphasic system (μ-ABS) was developed with reagents commonly used as food additives: cholinium chloride (ChCl) as main extraction phase, K2HPO4 as salting-out agent, and water as the main component (being the sample for analyses). With the aim of obtaining high enrichment factors, miniaturization, and adequate analytical performance, a point in the biphasic region with the lowest amount of ChCl was selected, corresponding to 1.55% (w/w) of ChCl, 59.5% (w/w) of K2HPO4, and 38.95% (w/w) of water. The green μ-ABS (attending to its main elements and performance mode) was used in combination with high-performance liquid chromatography with diode-array detection (HPLC-DAD) for the determination of 9 personal care products in wastewater samples. The μ-ABS-HPLC-DAD method showed high enrichment factors (up to 100), and quantitative extraction efficiencies for those compounds containing OH groups in their structure, which can undergo hydrogen bonding with ChCl. Thus, limits of quantification down to 0.8 µg·L-1 and extraction efficiencies between 66.4 and 108% (concentration levels of 1.3 and 13 µg·L-1) were reached for the group of parabens and the UV-filter benzophenone-3. The method is characterized by the use of non-harmful reagents and the absence of organic solvents in the entire sample preparation procedure, while being simple, low-cost, easily compatible with HPLC, and highly efficient.

Improved extraction of acid-insoluble monosulfide minerals by stannous chloride reduction and its application to the separation of mono- and disulfide minerals in the presence of ferric iron

Metal sulfides, which are important indicators of sulfur cycling, are usually divided into two categories according to sulfur chemical valence: (1) monosulfides (S2−) and (2) disulfides (S22−). The two sulfur species are separated and quantified by a sequential-extraction method. Specifically, monosulfides are extracted as acid-volatile sulfide (AVS) using 6 M HCl prior to the extraction of disulfides using acidic CrCl2, which is defined as chromium-reducible sulfur (CRS). However, the conventional AVS procedure does not result in the quantitative extraction of S2− from the acid-insoluble metal monosulfide, copper sulfide (CuS). Consequently, residual sulfur in CuS (CuS-S) may be extracted as CRS resulting in the inaccurate separation of these two sulfur species. In this study, we used stannous chloride (SnCl2) to improve CuS-S recovery in the AVS procedure and permit the separate extraction of sulfur from CuS and pyrite (FeS2), the most abundant disulfide in nature. Our results show that the addition of SnCl2 increased the recovery of CuS-S as AVS from less than 36% to as high as 92% in the absence of pyrite and Fe3+ and 89% in the presence of pyrite and Fe3+. In addition, based on the observed correlation between the concentration of SnCl2 and the dissolution of FeS2, we identified the appropriate concentration of SnCl2 needed to avoid the dissolution of FeS2 in the AVS procedure. SnCl2 also minimized the oxidation of CuS-S by Fe3+ released from ferric minerals during the extraction of AVS. Based on the results of a series of sequential-extraction experiments, we show that an amendment of SnCl2 in the AVS procedure followed by CRS permits the quantitative separation of CuS-S and FeS2-S while also preventing interference by Fe3+. Our method will find application in research concerned with the fate of metals and the biogeochemistry of sulfur in the environment.

Thermodynamic Modeling of Salting Effects in Solvent Extraction of Cobalt(II) from Chloride Media by the Basic Extractant Methyltrioctylammonium Chloride.

The design and optimization of solvent extraction processes for metal separations are challenging tasks due to the large number of adjustable parameters. A quantitative predictive solvent extraction model could help to determine the optimal parameters for solvent extraction flow sheets, but such predictive models are not available yet. The main difficulties for such models are the large deviations from ideal thermodynamic behavior in both the aqueous and organic phases due to high solute concentrations. We constructed a molecular thermodynamic model for the extraction of CoCl2 from different chloride salts by 0.2 mol L–1 trioctylmethylammonium chloride in toluene using the OLI mixed-solvent electrolyte (OLI-MSE) framework. This was accomplished by analyzing the water and hydrochloric acid content of the organic phase, measuring the water activity of the system, and using metal complex speciation and solvent extraction data. The full extractant concentration range cannot be modeled by the OLI-MSE framework as this framework lacks a description for reversed micelle formation. Nevertheless, salting effects and the behavior of hydrochloric acid can be accurately described with the presented extraction model, without determining specific Co(II)–salt cation interaction parameters. The resulting model shows that the salting effects originate from indirect salt cation–solvent interactions that influence the availability of water in the aqueous and organic phases.

Polymer Adsorbents vs. Functionalized Oxides and Carbons: Particulate Morphology and Textural and SurfaceCharacteristics.

Various methods for morphological, textural, and structural characterization of polymeric, carbon, and oxide adsorbents have been developed and well described. However, there are ways to improve the quantitative information extraction from experimental data for describing complex sorbents and polymer fillers. This could be based not only on probe adsorption and electron microscopies (TEM, SEM) but also on small-angle X-ray scattering (SAXS), cryoporometry, relaxometry, thermoporometry, quasi-elastic light scattering, Raman and infrared spectroscopies, and other methods. To effectively extract information on complex materials, it is important to use appropriate methods to treat the data with adequate physicomathematical models that accurately describe the dependences of these data on pressure, concentration, temperature, and other parameters, and effective computational programs. It is shown that maximum accurate characterization of complex materials is possible if several complemented methods are used in parallel, e.g., adsorption and SAXS with self-consistent regularization procedures (giving pore size (PSD), pore wall thickness (PWTD) or chord length (CLD), and particle size (PaSD) distribution functions, the specific surface area of open and closed pores, etc.), TEM/SEM images with quantitative treatments (giving the PaSD, PSD, and PWTD functions), as well as cryo- and thermoporometry, relaxometry, X-ray diffraction, infrared and Raman spectroscopies (giving information on the behavior of the materials under different conditions).

Extraction behavior of Pu(IV), Th(IV), and fission product elements with hexapropyl and hexabutyl phosphoramides

Abstract Hexapropyl phosphoramide (HPPA) and hexabutyl phosphoramide (HBPA) were examined for the macrolevel extraction and stripping of tetravalent metal ions such as Pu(IV) and Th(IV). The solvation number of Pu(IV) with both the phosphoramides were measured. The number of stages was evaluated for the quantitative extraction and stripping of Pu(IV) and Th(IV) in cross-current mode with 1.1 M phosphoramides in xylene from the nitric acid medium. The distribution ratios for the extraction of Th(IV) were measured with 1.1 M phosphoramides in xylene as a function of equilibrium aqueous phase Th(IV) and nitric acid concentration. In addition, distribution ratios for the extraction of fission product elements such as Zr, Tc, Ru, lanthanides (La, Pr, Nd and Gd) and activation product Am were also measured with 1.1 M HPPA and HBPA in n-Dodecane (n-DD) system as a function of aqueous phase nitric acid concentration. The extraction data generated with phosphoramides were compared with a 1.1 M TBP/n-DD system.

Rapid and selective magnetic separation of uranium in seawater and groundwater using novel phosphoramidate functionalized citrate-Fe3O4@Ag nanoparticles

One-pot magnetic separation of uranium (U) in seawater and groundwater samples has been made possible by synthesizing phosphoramidate functionalized Ag coated citrate-Fe3O4 nanoparticles (NPs). The magnetic saturation value of these functionalized NPs is 27.1 emu g−1. The synergistic extraction mechanism of U(VI) ion by the surface-modified phosphoramidate and citrate molecules make these NPs highly selective towards U(VI). The adsorption kinetics follows a pseudo-second-order model and the adsorption isotherm fits successfully to the Langmuir adsorption model. The functionalized NPs show quantitative extraction efficiency in the pH range of 6.5–8 with a maximum loading capacity (Qm) of 108.7 mg g−1. The equilibration time required by these functionalized NPs to attain the Qm value is 120 s. The recycling of these NPs can be done up to 5–6 times with 1.0 mol L−1 of Na2CO3 or NH4OH for quantitative extraction of U(VI). These functionalized NPs show high resilience towards large number of naturally abundant metal ions.

Understanding Sources of Variation to Improve the Reproducibility of Radiomics.

Radiomics is the method of choice for investigating the association between cancer imaging phenotype, cancer genotype and clinical outcome prediction in the era of precision medicine. The fast dispersal of this new methodology has benefited from the existing advances of the core technologies involved in radiomics workflow: image acquisition, tumor segmentation, feature extraction and machine learning. However, despite the rapidly increasing body of publications, there is no real clinical use of a developed radiomics signature so far. Reasons are multifaceted. One of the major challenges is the lack of reproducibility and generalizability of the reported radiomics signatures (features and models). Sources of variation exist in each step of the workflow; some are controllable or can be controlled to certain degrees, while others are uncontrollable or even unknown. Insufficient transparency in reporting radiomics studies further prevents translation of the developed radiomics signatures from the bench to the bedside. This review article first addresses sources of variation, which is illustrated using demonstrative examples. Then, it reviews a number of published studies and progresses made to date in the investigation and improvement of feature reproducibility and model performance. Lastly, it discusses potential strategies and practical considerations to reduce feature variability and improve the quality of radiomics study. This review focuses on CT image acquisition, tumor segmentation, quantitative feature extraction, and the disease of lung cancer.