Quantitative Features Research Articles

The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study

PurposeRadiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC).Materials and methodsRetrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models.ResultsWe collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively.ConclusionOur clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.

Multivariable Mendelian randomization with incomplete measurements on the exposure variables in the Hispanic Community Health Study/Study of Latinos

Multivariable Mendelian randomization allows simultaneous estimation of direct causal effects of multiple exposure variables on an outcome. When the exposure variables of interest are quantitative omic features, obtaining complete data can be economically and technically challenging: the measurement cost is high, and the measurement devices may have inherent detection limits. In this paper, we propose a valid and efficient method to handle unmeasured and undetectable values of the exposure variables in a one-sample multivariable Mendelian randomization analysis with individual-level data. We estimate the direct causal effects with maximum likelihood estimation and develop an expectation-maximization algorithm to compute the estimators. We show the advantages of the proposed method through simulation studies and provide an application to the Hispanic Community Health Study/Study of Latinos, which has a large amount of unmeasured exposure data.

Quantitative CT Imaging Features Associated with Stable PRISm using Machine Learning

Rationale and ObjectivesThe structural lung features that characterize individuals with preserved ratio impaired spirometry (PRISm) that remain stable overtime are unknown. The objective of this study was to use machine learning models with computed tomography (CT) imaging to classify stable PRISm from stable controls and stable COPD and identify discriminative features. Materials and MethodsA total of 596 participants that did not transition between control, PRISm and COPD groups at baseline and 3-year follow-up were evaluated: n=274 with normal lung function (stable control), n=22 stable PRISm, and n=300 stable COPD. Investigated features included: quantitative CT (QCT) features (n=34), such as total lung volume (%TLCCT) and percentage of ground glass and reticulation (%GG+Reticulationtexture), as well as Radiomic (n=102) features, including varied intensity zone distribution grainy texture (GLDZMZDV). Logistic regression machine learning models were trained using various feature combinations (Base, Base+QCT, Base+Radiomic, Base+QCT+Radiomic). Model performances were evaluated using area under receiver operator curve (AUC) and comparisons between models were made using DeLong test; feature importance was ranked using Shapley Additive Explanations values. ResultsMachine learning models for all feature combinations achieved AUCs between 0.63-0.84 for stable PRISm vs. stable control, and 0.65-0.92 for stable PRISm vs. stable COPD classification. Models incorporating imaging features outperformed those trained solely on base features (p>0.05). Compared to stable control and COPD, those with stable PRISm exhibited decreased %TLCCT and increased %GG+Reticulationtexture and GLDZMZDV. ConclusionThese findings suggest that reduced lung volumes, and elevated high-density and ground glass/reticulation patterns on CT imaging are associated with stable PRISm.

Research on the shared function of central neurons and breast cancer based on gene expression profile data mining: The role of EMID1 protein antibody expression

In recent years, the incidence of breast cancer has gradually increased, and the research on it has become a hot spot in the scientific community. Central neurons play an important role in breast cancer. This study aims to explore the application of gene expression profile data mining in the study of shared function between central neurons and breast cancer, and focuses on the expression of EMID1 protein antibody. The study collected biomedical images and gene expression profile data of breast cancer patients. Then, we use image processing and analysis technology to extract and analyze features of biomedical images to obtain quantitative features of breast cancer. Gene expression profile data were preprocessed and analyzed to obtain information about breast cancer related genes. Integrating and fusing biomedical images and gene expression profile data, and exploring the sharing function between central neurons and breast cancer through data mining algorithms and statistical analysis methods. The results showed that the expression of EMID1 protein was high in breast cancer tissues, and the expression pattern was similar to that of central neurons. Further functional studies have shown that EMID1 protein is involved in the regulation of proliferation and invasion of breast cancer cells. By regulating the expression level of EMID1 protein, we observed that the proliferation and invasion ability of breast cancer cells were significantly affected. The research results show that through the comprehensive analysis of biomedical images and gene expression profile data, we found the sharing function between central neurons and breast cancer. The central neuronal cell marker genes EMID1 and GREB1L may be used as key biomarkers to regulate the pathogenesis of breast cancer and affect the occurrence and development of breast cancer.

Investigating quantitative histological characteristics in renal pathology using HistoLens

HistoLens is an open-source graphical user interface developed using MATLAB AppDesigner for visual and quantitative analysis of histological datasets. HistoLens enables users to interrogate sets of digitally annotated whole slide images to efficiently characterize histological differences between disease and experimental groups. Users can dynamically visualize the distribution of 448 hand-engineered features quantifying color, texture, morphology, and distribution across microanatomic sub-compartments. Additionally, users can map differentially detected image features within the images by highlighting affected regions. We demonstrate the utility of HistoLens to identify hand-engineered features that correlate with pathognomonic renal glomerular characteristics distinguishing diabetic nephropathy and amyloid nephropathy from the histologically unremarkable glomeruli in minimal change disease. Additionally, we examine the use of HistoLens for glomerular feature discovery in the Tg26 mouse model of HIV-associated nephropathy. We identify numerous quantitative glomerular features distinguishing Tg26 transgenic mice from wild-type mice, corresponding to a progressive renal disease phenotype. Thus, we demonstrate an off-the-shelf and ready-to-use toolkit for quantitative renal pathology applications.

Ultrasomics in liver cancer: Developing a radiomics model for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma using contrast-enhanced ultrasound.

Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) represent the predominant histological types of primary liver cancer, comprising over 99% of cases. Given their differing biological behaviors, prognoses, and treatment strategies, accurately differentiating between HCC and ICC is crucial for effective clinical management. Radiomics, an emerging image processing technology, can automatically extract various quantitative image features that may elude the human eye. Reports on the application of ultrasound (US)-based radiomics methods in distinguishing HCC from ICC are limited. To develop and validate an ultrasomics model to accurately differentiate between HCC and ICC. In our retrospective study, we included a total of 280 patients who were diagnosed with ICC (n = 140) and HCC (n = 140) between 1999 and 2019. These patients were divided into training (n = 224) and testing (n = 56) groups for analysis. US images and relevant clinical characteristics were collected. We utilized the XGBoost method to extract and select radiomics features and further employed a random forest algorithm to establish ultrasomics models. We compared the diagnostic performances of these ultrasomics models with that of radiologists. Four distinct ultrasomics models were constructed, with the number of selected features varying between models: 13 features for the US model; 15 for the contrast-enhanced ultrasound (CEUS) model; 13 for the combined US + CEUS model; and 21 for the US + CEUS + clinical data model. The US + CEUS + clinical data model yielded the highest area under the receiver operating characteristic curve (AUC) among all models, achieving an AUC of 0.973 in the validation cohort and 0.971 in the test cohort. This performance exceeded even the most experienced radiologist (AUC = 0.964). The AUC for the US + CEUS model (training cohort AUC = 0.964, test cohort AUC = 0.955) was significantly higher than that of the US model alone (training cohort AUC = 0.822, test cohort AUC = 0.816). This finding underscored the significant benefit of incorporating CEUS information in accurately distinguishing ICC from HCC. We developed a radiomics diagnostic model based on CEUS images capable of quickly distinguishing HCC from ICC, which outperformed experienced radiologists.

Assessing the stability and discriminative ability of radiomics features in the tumor microenvironment: Leveraging peri-tumoral regions in vestibular schwannoma

PurposeThe tumor microenvironment (TME) plays a crucial role in tumor progression and treatment response. Radiomics offers a non-invasive approach to studying the TME by extracting quantitative features from medical images. In this study, we present a novel approach to assess the stability and discriminative ability of radiomics features in the TME of vestibular schwannoma (VS). MethodsMagnetic Resonance Imaging (MRI) data from 242 VS patients were analyzed, including contrast-enhanced T1-weighted (ceT1) and high-resolution T2-weighted (hrT2) sequences. Radiomics features were extracted from concentric peri-tumoral regions of varying sizes. The intraclass correlation coefficient (ICC) was used to assess feature stability and discriminative ability, establishing quantile thresholds for ICCmin and ICCmax. ResultsThe identified thresholds for ICCmin and ICCmax were 0.45 and 0.72, respectively. Features were classified into four categories: stable and discriminative (S-D), stable and non-discriminative (S-ND), unstable and discriminative (US-D), and unstable and non-discriminative (US-ND). Different feature groups exhibited varying proportions of S-D features across ceT1 and hrT2 sequences. The similarity of S-D features between ceT1 and hrT2 sequences was evaluated using Jaccard’s index, with a value of 0.78 for all feature groups which is ranging from 0.68 (intensity features) to 1.00 (Neighbouring Gray Tone Difference Matrix (NGTDM) features). ConclusionsThis study provides a framework for identifying stable and discriminative radiomics features in the TME, which could serve as potential biomarkers or predictors of patient outcomes, ultimately improving the management of VS patients.

An Application of the Scaled–Pixel–Counting Protocol to Quantify the Radiological Features of Anatomical Structures of the Normal Tarsal Joint in Horses

Abstract Background: As high-performance human and equine athletes train and compete at the highest level of effort, the prevention of high-performance-cased diseases, such as osteoarthritis (OA), requires knowledge of the anatomy and physiology of the subjected bones. Objective: Implementation of the scaled–pixel–counting protocol to quantify the radiological features of anatomical structures of the normal equine tarsal joint as the first step in the prevention of the tarsal joints OA in high-performance sport horses. Methods: A radiographic examination was performed in six cadaverous equine pelvic limbs. The dorso–plantar projection of the tarsal joint was performed using density standard (DS) attached to the radiographic cassette, standard X-ray equipment and standard diagnostic imaging protocol. On each of the radiographs, pixel brightness (PB) was extracted for each of the 10 steps (S1–S10) of DS. On each of the radiographs, seven regions of interest (ROIs) were annotated representing four bones (II tarsal bone [II TB], III tarsal bone [III TB], IV tarsal bone [IV TB] and central tarsal bone [CTB]) and three joints (proximal intertarsal joint [PIJ], distal intertarsal joint [DIJ] and tarsometatarsal joint [TMJ]), respectively. For each ROI, the percentage (%) of number of pixels (NP) from each range was calculated. Results: The % of NP was lower in bones than in joint spaces for S1–S6 and was higher in bones than in joint spaces for S8–S10. The % of NP was higher in PIJ than TMJ for S1 and higher in PIJ than DIJ for S4. No differences were found between consecutive bones for all examined steps of DS. Conclusions: An application of the scaled–pixel–counting protocol provides the quantitative radiological features of normal bone and joint structures of the tarsal joint in horses, making possible differentiation of the lucency of joint space and opacity of bone structure.

Lipids, lipid-modified drug target genes, and the risk of male infertility: a Mendelian randomization study.

Previous observational studies have reported a possible association between circulating lipids and lipid-lowering drugs and male infertility (MIF), as well as the mediating role of circulating vitamin D. Then, due to issues such as bias, reverse causality, and residual confounding, inferring causal relationships from these studies may be challenging. Therefore, this study aims to explore the effects of circulating lipids and lipid-lowering drugs on MIF through Mendelian randomization (MR) analysis and evaluate the mediating role of vitamin D. Genetic variations related to lipid traits and the lipid-lowering effect of lipid modification targets are extracted from the Global Alliance for Lipid Genetics Genome-Wide Association Study. The summary statistics for MIF are from the FinnGen 9th edition. Using quantitative expression feature loci data from relevant organizations to obtain genetic variations related to gene expression level, further to explore the relationship between these target gene expression levels and MIF risk. Two-step MR analysis is used to explore the mediating role of vitamin D. Multiple sensitivity analysis methods (co-localization analysis, Egger intercept test, Cochrane's Q test, pleiotropy residuals and outliers (MR-PRESSO), and the leave-one-out method) are used to demonstrate the reliability of our results. In our study, we observed that lipid modification of four lipid-lowering drug targets was associated with MIF risk, the LDLR activator (equivalent to a 1-SD decrease in LDL-C) (OR=1.94, 95% CI 1.14-3.28, FDR=0.040), LPL activator (equivalent to a 1-SD decrease in TG) (OR=1.86, 95% CI 1.25-2.76, FDR=0.022), and CETP inhibitor (equivalent to a 1-SD increase in HDL-C) (OR=1.28, 95% CI 1.07-1.53, FDR=0.035) were associated with a higher risk of MIF. The HMGCR inhibitor (equivalent to a 1-SD decrease in LDL-C) was associated with a lower risk of MIF (OR=0.38, 95% CI 0.17-0.83, FDR=0.39). Lipid-modifying effects of three targets were partially mediated by serum vitamin D levels. Mediation was 0.035 (LDLR activator), 0.012 (LPL activator), and 0.030 (CETP inhibitor), with mediation ratios of 5.34% (LDLR activator), 1.94% (LPL activator), and 12.2% (CETP inhibitor), respectively. In addition, there was no evidence that lipid properties and lipid modification effects of six other lipid-lowering drug targets were associated with MIF risk. Multiple sensitivity analysis methods revealed insignificant evidence of bias arising from pleiotropy or genetic confounding. This study did not support lipid traits (LDL-C, HDL-C, TG, Apo-A1, and Apo-B) as pathogenic risk factors for MIF. It emphasized that LPL, LDLR, CETP, and HMGCR were promising drug targets for improving male fertility.

Comparative analysis of the capabilities of WoS and eLibrary for analyzing bibliographic networks

This article presents a comparative analysis of two major scientific publication databases: Web of Science Core Collection and eLibrary – to identify their differences and unique opportunities for exploration of bibliographic networks of Russian scientific authors. Current shortage of tools and approaches for collection, processing and analysis of bibliographic data in the Russian language constitutes the relevance of this study. Empirical analysis is based on comparison of respective arrays of scientific publications in the field of sociology over the period of 2010-2021. We propose a set of comparison criteria including those related to the procedure of data access, quality of data management, quantitative and qualitative features of the data. Inspection of the databases based on the proposed criteria aids in identification of intersections between both the collections and the respective qualitative observations about them. We make conclusions regarding the comparative advantages and weaknesses of both databases in regards to their potential as the sole data source for bibliographic studies, and make recommendations for their effective use in research on Russian science.

Voltage relaxation characterization methods in lithium-ion batteries

This study evaluated three approaches for characterizing voltage relaxation in lithium-ion batteries: voltage vs. time, the derivative of voltage vs. time, and the second derivative of voltage vs. time. The first two are well-established approaches, whereas the third was never investigated to our knowledge. To assess the potential of each approach, characterizations were performed on data with various depth-of-discharges, regimes, state of healths, temperatures, and chemistries. Findings indicate that the established approaches do not comprehensively characterize voltage relaxation whereas the novel approach demonstrated promise in providing a quantitative feature to compare relaxation behaviors. However, it was found to have severe limitations in its application due to its lack of consistency between chemistry, rates, and temperatures, reliance on heavy filtering, and inability to identify trends in capacity loss, all preventing any potential for widespread application.

Growth performance, carcass composition, and qualitative meat features of broiler chickens after galactooligosaccharides and sodium butyrate in ovo administration

The study aimed to analyze the growth performance, feed indicators, and quantitative and qualitative physicochemical features of carcass and meat from broiler chickens after rearing, stimulated in ovo on d 12 of incubation with various substances. In the experiment, 1,200 hatching eggs from meat-type hen Ross 308 parental flock were incubated. On d 12, the injection was performed. Group CON-0 was noninjected. Group CON-S was injected with saline. In the GOS group, the procedure was performed in ovo with galactooligosaccharides (dissolved 3.5 mg of GOS in 0.2 mL of NaCl). In the SB group, butyric acid sodium salt was administered in a 0.3% SB dissolved in 0.2 mL NaCl. After hatching, 336, 1-day-old chicks per group were transferred to the broiler house and kept in 7 pens with 12 chickens per group for 42 d. The body weight and feed intake indicators were calculated. Next, 40 birds were selected (n = 10 per group) and taken to analyze carcass composition and meat quality (pH, color, WHC, drip loss, chemical composition). Compared to the experimental groups, the highest body weight indicators were found in groups CON-0 and CON-S. The feed conversion ratio was the lowest in the SB group on d 36 to 42 (P < 0.05). The European Efficiency Production Factor in groups GOS and SB was lower than in group CON-S (P = 0.005). The GOS group showed higher pH24hours in the pectoral muscles than the CON-S group (P = 0.011). The leg muscles showed better WHC in the CON-S, GOS, and SB groups than in the CON-0 group (P < 0.001). A lower intramuscular fat of the pectoral and leg muscle content was demonstrated, especially in the SB group. Injection of galactooligosaccharides and sodium butyrate in ovo adversely affected broiler production but did not alter carcass composition. It varied pectoral muscles' pH and chemical composition and improved water holding capacity and chemical composition in leg muscles.

A Classification Method for Incomplete Mixed Data Using Imputation and Feature Selection

Data missing is a ubiquitous problem in real-world systems that adversely affects the performance of machine learning algorithms. Although many useful imputation methods are available to address this issue, they often fail to consider the information provided by both features and labels. As a result, the performance of these methods might be constrained. Furthermore, feature selection as a data quality improvement technique has been widely used and has demonstrated its efficiency. To overcome the limitation of imputation methods, we propose a novel algorithm that combines data imputation and feature selection to tackle classification problems for mixed data. Based on the mean and standard deviation of quantitative features and the selecting probabilities of unique values of categorical features, our algorithm constructs different imputation models for quantitative and categorical features. Particle swarm optimization is used to optimize the parameters of the imputation models and select feature subsets simultaneously. Additionally, we introduce a legacy learning mechanism to enhance the optimization capability of our method. To evaluate the performance of the proposed method, seven algorithms and twelve datasets are used for comparison. The results show that our algorithm outperforms other algorithms in terms of accuracy and F1 score and has reasonable time overhead.

Assessing the Heterogeneity of Response of [68Ga] Ga-PSMA-11 PET/CT Lesions in Patients With Biochemical Recurrence of Prostate Cancer

IntroductionTreatment of men with metastatic prostate cancer can be difficult due to the heterogeneity of response of lesions. [68Ga]Ga-PSMA-11 (PSMA) PET/CT assists with monitoring and directing clinical intervention; however, the impact of response heterogeneity has yet to be related to outcome measures. The aim of this study was to assess the impact of quantitative imaging information on the value of PSMA PET/CT to assess patient outcomes in response evaluation. Patients and MethodsBaseline and follow-up (6 months) PSMA PET/CT of 162 men with oligometastatic PC treated with standard clinical care were acquired between 2015 and 2016 for analysis. An augmentative software medical device was used to track lesions between scans and quantify lesion change to categorize them as either new, increasing, stable, decreasing, or disappeared. Quantitative imaging features describing the size, intensity, extent, change, and heterogeneity of change (based on percent change in SUVtotal) among lesions were extracted and evaluated for association with overall survival (OS) using Cox regression models. Model performance was evaluated using the c-index. ResultsForty-one (25%) of subjects demonstrated heterogeneous response at follow-up, defined as having at least 1 new or increasing lesion and at least 1 decreasing or disappeared lesion. Subjects with heterogeneous response demonstrated significantly shorter OS than subjects without (median OS = 76.6 months vs. median OS not reached, P < .05, c-index = 0.61). In univariate analyses, SUVtotal at follow-up was most strongly associated with OS (HR = 1.29 [1.19, 1.40], P < .001, c-index = 0.73). Multivariable models applied using heterogeneity of change features demonstrated higher performance (c-index = 0.79) than models without (c-index = 0.71-0.76, P < .05). ConclusionAugmentative software tools enhance the evaluation change on serial PSMA PET scans and can facilitate lesional evaluation between timepoints. This study demonstrates that a heterogeneous response at a lesional level may impact adversely on patient outcomes and supports further investigation to evaluate the role of imaging to guide individualized patient management to improve clinical outcomes.

Comparative Analysis of Tool Functionality in Descriptive Statistics of Language R

The article provides methods and procedures of getting numerical statistical characteristics of qualitative and quantitative features by means of language R in the process of preliminary data analysis. The most expedient, in view of simplicity of use and information value of result, specification of functions of language descriptive statistics was shown. The author studied comparative advantages of tools of descriptive statistics of the language with the open software code R in comparison with similar units Python and Ms Excel. A conclusion was drawn about the convenience and appeal of using language R for estimating descriptive statistics for beginners, who have not got specific knowledge in programming. For example, it was shown that in order to get min 12 characteristics of descriptive data statistics grouped by categories only one command will be enough. As for estimating one statistical characteristic for segment of data produced by several grouping features, again only one command is necessary. Findings of the research can be useful for investigators of different fields, both beginners and experts, who work with methods of statistical data processing in academic and practical spheres.

Operator impact assessment on qualitative and quantitative parameters of canine platelet-rich plasma.

Platelet-rich plasma (PRP) has emerged as a cornerstone in veterinary regenerative medicine. The present study evaluated the impact of the operator on the qualitative and quantitative features of non-activated PRP derived from canine whole blood. Blood was collected in anticoagulant acid citrate dextrose from twelve healthy adult dogs and PRP was prepared according to the double-spin method. Both operators followed an identical protocol and utilized the same equipment for PRP preparation from the pooled blood samples. The resulting PRP underwent characterization, classification and coding based on minimum reporting standards. The consistency and internal reliability of different parameters were also assessed using the intraclass correlation coefficient and Cronbach's alpha values. Variables such as white blood cell (WBC) concentration, relative WBC composition and mean platelet volume (MPV) showed poor reliability, and WBC concentration and MPV also had unacceptable internalconsistency. Significant differences were observed in several qualitative and quantitative parameters of the prepared PRP, highlighting the influence of the operator even when the same protocol and equipment were used. Our study has direct implications to regenerative medicine, reinforcing the urgency to set minimum requirements for reporting PRP in research studies.

Security monitoring via sound analysis and voice identification with artificial intelligence

The article demonstrates the possibility of monitoring user access through authentication based on voice profiles using the means of Artificial Intelligence. A two-stage approach is proposed for sound analysis and voice recognition using Feed-Forward Neural Networks (FFNNs) and Cascade-Forward Neural Networks (CFNNs). Seven test voice profiles were pre-processed to extract quantitative sound features. The procedure involves registration of a set of sound parameters concerning three categories, respectively, for all audio and acoustic measurements in the entire sound spectrum, measurements up to and above 100 dBA. The neural architectures were trained with Scaled Gradient Descent (SCG) and Levenberg Marquardt (LM) algorithms, using different transfer functions in the output structural layers. In the initial phase of neural training, the entire sound spectrum of registered indicators was used, and high levels of Accuracy around 90.0% were reached. Subsequently, steps were taken to reduce the informative features when searching for similar levels of accuracy in order to limit the necessary computational procedures in neural training, but maintain the threshold of successful user authentication. In the analysis of neural performance, in addition to accuracy, additional criteria were used, namely Mean-Squared Error (MSE) and Root Mean Squared Error (RMSE). About the achieved and analyzed results, a synthesis was conducted of a set of four informative features with the highest significance, respectively LAE (A-weighted, sound exposure level), Laeq (A-weighted, equivalent sound level), LAF (A-weighted, fast time-constant, sound level) and LAS (A-weighted, slow time constant response, sound level). In the course of subsequent neural training processes, unsuitability was found when using the Log-sigmoid activation type with greatly underestimated accuracy readings and errors below 58.0% and above thresholds of 0.2300 and 0.4800. Positive performance indicators of voice recognition were achieved with Softmax and Hyperbolic tangent sigmoid activations in SCG and LM training procedures in levels of accuracy of 98.7 % and 96.1 % at FFNN models. Successful correct recognition of the test voice profiles on access and security personalization with a quantitative equivalent of 100.0 % accuracy was achieved in the Linear transfer function for Cascade-Forward Neural Networks. The proposed method and the synthesized neural models in the research can be used as units and modules in access control systems with biometric diagnostics and intelligent recognition of employees in company departments to electronically store classified information and physical access control.

Magnetic resonance imaging-based prognostic model for subsequent distant metastasis in patients with ipsilateral breast tumor recurrence following breast-conserving surgery.

Ipsilateral breast tumor recurrence (IBTR) following breast-conserving surgery (BCS) has been considered a risk factor for distant metastasis (DM). Limited data are available regarding the subsequent outcomes after IBTR. Therefore, this study aimed to determine the clinical course after IBTR and develop a magnetic resonance imaging (MRI)-based predictive model for subsequent DM. We retrospectively extracted quantitative features from MRI to construct a radiomics cohort, with all eligible patients undergoing preoperative MRI at time of primary tumor and IBTR between 2010 and 2018. Multivariate Cox analysis was performed to identify factors associated with DM. Three models were constructed using different sets of clinicopathological, qualitative, and quantitative MRI features and compared. Additionally, Kaplan-Meier analysis was performed to assess the prognostic value of the optimal model. Among the 183 patients who experienced IBTR, 47 who underwent MRI for both primary and recurrent tumors were enrolled. Multivariate analysis demonstrated that the independent prognostic factors were human epidermal growth factor receptor 2 (HER2) status [hazard ratio (HR) =5.40] and background parenchymal enhancement (BPE) (HR =7.94) (all P values <0.01). Furthermore, four quantitative MRI features of recurrent tumors were selected through the least absolute shrinkage and selection operator (LASSO) method. The combined model exhibited superior performance [concordance index (C-index) 0.77] compared to the clinicoradiological model (C-index 0.71; P=0.006) and radiomics model (C-index 0.70; and P=0.01). Furthermore, the combined model successfully categorized patients into low- and high-risk subgroups with distinct prognoses (P<0.001). The clinicopathological and MRI features of IBTR were associated with secondary events following surgery. Additionally, the MRI-based combined model exhibited the highest predictive efficacy. These findings could be helpful in risk stratification and tailoring follow-up strategies in patients with IBTR.

Differentiation of benign and malignant lesions in Bethesda III and IV thyroid nodules via dual-energy computed tomography quantitative parameters and morphologic features.

Thyroid nodules (TNs) cytologically defined as category Bethesda III and IV pose a major diagnostic challenge before surgery, demanding new methods to reduce unnecessary diagnostic thyroid lobectomies for patients with benign TNs. This study aimed to assess whether a model combining dual-energy computed tomography (DECT) quantitative parameters with morphologic features could reliably differentiate between benign and malignant lesions in Bethesda III and IV TNs. Data from 77 patients scheduled for thyroid surgery for Bethesda III and IV TNs (malignant =48; benign =29) who underwent DECT scans were reviewed. DECT quantitative parameters including normalized iodine concentration (NIC), attenuation on the slope of spectral Hounsfield unit (HU) curve, and normalized effective atomic number (Zeff) were measured in the arterial phase (AP) and venous phase (VP). DECT quantitative parameters and morphologic features were compared between the malignant and benign cohorts. The receiver operating characteristic curve was performed to compare the performances of significant DECT quantitative parameters, morphologic features, or the models combining the DECT parameters, respectively, with morphologic features. A nomogram was constructed from the optimal performance model, and the performance was evaluated via the calibration curve and decision curve analysis. The areas under the receiver operating characteristic curve with 95% confidence interval (CI) of the NIC in the AP (AP-NIC), slope of spectral HU curve in the AP, and NZeff in the AP were 0.749 (95% CI: 0.641-0.857), 0.654 (95% CI: 0.530-0.778), and 0.722 (95% CI: 0.602-0.842), respectively. The model combining AP-NIC with enhanced blurring showed the highest diagnostic performance, with an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.808, 0.854, and 0.655, respectively; it was then used to construct a nomogram. The calibration curve showed that the discrepancy between the prediction of the nomogram and actual observations was less than 5%. The decision curve analysis indicated the nomogram had a positive net benefit in threshold risk ranges of 14% to 58% or 60% to 91% for malignant Bethesda III and IV TNs. The model combining AP-NIC with enhanced blurring could reliably differentiate between benign and malignant lesions in Bethesda III and IV TNs.

Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories.

To investigate the fundamental question of how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we focused on nuclear growth in hiPS cell colonies as a model system. We generated a 3D timelapse dataset of thousands of nuclei over multiple days, and developed open-source tools for image and data analysis and an interactive timelapse viewer for exploring quantitative features of nuclear size and shape. We performed a data-driven analysis of nuclear growth variations across timescales. We found that individual nuclear volume growth trajectories arise from short timescale variations attributable to their spatiotemporal context within the colony. We identified a strikingly time-invariant volume compensation relationship between nuclear growth duration and starting volume across the population. Notably, we discovered that inheritance plays a crucial role in determining these two key nuclear growth features while other growth features are determined by their spatiotemporal context and are not inherited.