The structure is the message: Preserving experimental context through tensor decomposition.

Recent biological studies have been revolutionized in scale and granularity by multiplex and high-throughput assays. Profiling cell responses across several experimental parameters, such as perturbations, time, and genetic contexts, leads to richer and more generalizable findings. However, these multidimensional datasets necessitate a reevaluation of the conventional methods for their representation and analysis. Traditionally, experimental parameters are merged to flatten the data into a two-dimensional matrix, sacrificing crucial experiment context reflected by the structure. As Marshall McLuhan famously stated, "the medium is the message." In this work, we propose that the experiment structure is the medium in which subsequent analysis is performed, and the optimal choice of data representation must reflect the experiment structure. We review how tensor-structured analyses and decompositions can preserve this information. We contend that tensor methods are poised to become integral to the biomedical data sciences toolkit.

Current Auditory Hallucinations Are Not Associated With Specific White Matter Diffusion Alterations in Schizophrenia.

Background and Hypothesis: Studies have linked auditory hallucinations (AH) in schizophrenia spectrum disorders (SCZ) to altered cerebral white matter microstructure within the language and auditory processing circuitry (LAPC). However, the specificity to the LAPC remains unclear. Here, we investigated the relationship between AH and DTI among patients with SCZ using diffusion tensor imaging (DTI). Study Design: We included patients with SCZ with (AH+; n = 59) and without (AH-; n = 81) current AH, and 140 age- and sex-matched controls. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were extracted from 39 fiber tracts. We used principal component analysis (PCA) to identify general factors of variation across fiber tracts and DTI metrics. Regression models adjusted for sex, age, and age2 were used to compare tract-wise DTI metrics and PCA factors between AH+, AH-, and healthy controls and to assess associations with clinical characteristics. Study Results: Widespread differences relative to controls were observed for MD and RD in patients without current AH. Only limited differences in 2 fiber tracts were observed between AH+ and controls. Unimodal PCA factors based on MD, RD, and AD, as well as multimodal PCA factors, differed significantly relative to controls for AH-, but not AH+. We did not find any significant associations between PCA factors and clinical characteristics. Conclusions: Contrary to previous studies, DTI metrics differed mainly in patients without current AH compared to controls, indicating a widespread neuroanatomical distribution. This challenges the notion that altered DTI metrics within the LAPC is a specific feature underlying AH.

Improved efficacy behavioral modeling of microwave circuits through dimensionality reduction and fast global sensitivity analysis.

Behavioral models have garnered significant interest in the realm of high-frequency electronics. Their primary function is to substitute costly computational tools, notably electromagnetic (EM) analysis, for repetitive evaluations of the structure under consideration. These evaluations are often necessary for tasks like parameter tuning, statistical analysis, or multi-criterial design. However, constructing reliable surrogate models faces several challenges, including the nonlinearity of circuit characteristics and the vast size of the parameter space, encompassing both dimensionality and design variable ranges. Additionally, ensuring the validity of the model across broad geometry/material parameter and frequency ranges is crucial for its utility in design. The purpose of this paper is to introduce an innovative approach to cost-effective and dependable behavioral modeling of microwave passives. Central to our method is a fast global sensitivity analysis (FGSA) procedure, which is devised to identify correlations between design parameters and quantify their impacts on circuit characteristics. The most significant directions identified through FGSA are utilized to establish a reduced-dimensionality domain. Within this domain, the model may be constructed using a limited amount of data samples while capturing a significant portion of the circuit response variability, rendering it suitable for design purposes. The outstanding predictive capability of the proposed model, its superiority over traditional techniques, and its readiness for design applications are demonstrated through the analysis of three microstrip circuits of diverse characteristics.

Association of adipocytokine pathway gene polymorphisms with NAFLD in obese children. / è„‚è‚ªç»†èƒžå› å­é€šè·¯åŸºå› å¤šæ€æ€§ä¸Žè‚¥èƒ–å„¿ç«¥éžé ’ç²¾æ€§è„‚è‚ªæ€§è‚ç— çš„å ³è”.

OBJECTIVES: Non-alcoholic fatty liver disease (NAFLD) has significant genetic susceptibility. Adipocytokines play a crucial role in NAFLD development by participating in insulin resistance and hepatic steatosis. However, the association between adipocytokine pathway genes and NAFLD remains unclear. This study aims to explore the association of gene polymorphisms in the adipocytokine pathway and their interactions with NAFLD in obese children. METHODS: A case-control study was conducted, dividing obese children into NAFLD and control groups. Peripheral venous blood (2 mL) was collected from each participant for DNA extraction. A total of 14 single nucleotide polymorphisms (SNP) in the adipocytokine pathway were genotyped using multiplex PCR and high-throughput sequencing. Univariate and multivariate Logistic regression analyses were used to assess the association between SNP and NAFLD in obese children. Dominant models were used to analyze additive and multiplicative interactions via crossover analysis and Logistic regression. Generalized multifactor dimensionality reduction (GMDR) was used to detect gene-gene interactions among the 14 SNPs and their association with NAFLD in obese children. RESULTS: A total of 1 022 children were included, with 511 in the NAFLD group and 511 in the control group. After adjusting for age, gender, and BMI, multivariate Logistic regression showed that PPARG rs1801282 was associated with NAFLD in the obese children in 3 genetic models: heterozygote model (CG vs CC, OR=0.58, 95% CI 0.36 to 0.95, P=0.029), dominant model (GG+CG vs CC, OR=0.62, 95% CI 0.38 to 1.00, P=0.049), and overdominant model (CC+GG vs CG, OR=1.72, 95% CI 1.06 to 2.80, P=0.028). PRKAG2 rs12703159 was associated with NAFLD in 4 genetic models: heterozygous model (CT vs CC, OR=1.51, 95% CI 1.10 to 2.07, P=0.011), dominant model (CT+TT vs CC, OR=1.50, 95% CI 1.10 to 2.03, P=0.010), overdominant model (CC+TT vs CT, OR=0.67, 95% CI 0.49 to 0.92, P=0.012), and additive model (CC vs CT vs TT, OR=1.40, 95% CI 1.07 to 1.83, P=0.015). No significant multiplicative or additive interaction between PPARG rs1801282 and PRKAG2 rs12703159 was found in association with NAFLD. GMDR analysis, adjusted for age, gender, and BMI, revealed no statistically significant interactions among the 14 SNPs (all P>0.05). CONCLUSIONS: Mutations in PPARG rs1801282 and PRKAG2 rs12703159 are associated with NAFLD in obese children. However, no gene-gene interactions among the SNP are found to be associated with NAFLD in obese children.

An enhanced algorithm for semantic-based feature reduction in spam filtering.

With the advent and improvement of ontological dictionaries (WordNet, Babelnet), the use of synsets-based text representations is gaining popularity in classification tasks. More recently, ontological dictionaries were used for reducing dimensionality in this kind of representation (e.g., Semantic Dimensionality Reduction System (SDRS) (Vélez de Mendizabal et al., 2020)). These approaches are based on the combination of semantically related columns by taking advantage of semantic information extracted from ontological dictionaries. Their main advantage is that they not only eliminate features but can also combine them, minimizing (low-loss) or avoiding (lossless) the loss of information. The most recent (and accurate) techniques included in this group are based on using evolutionary algorithms to find how many features can be grouped to reduce false positive (FP) and false negative (FN) errors obtained. The main limitation of these evolutionary-based schemes is the computational requirements derived from the use of optimization algorithms. The contribution of this study is a new lossless feature reduction scheme exploiting information from ontological dictionaries, which achieves slightly better accuracy (specially in FP errors) than optimization-based approaches but using far fewer computational resources. Instead of using computationally expensive evolutionary algorithms, our proposal determines whether two columns (synsets) can be combined by observing whether the instances included in a dataset (e.g., training dataset) containing these synsets are mostly of the same class. The study includes experiments using three datasets and a detailed comparison with two previous optimization-based approaches.

The Dimensions of dimensionality.

Cognitive scientists often infer multidimensional representations from data. Whether the data involve text, neuroimaging, neural networks, or human judgments, researchers frequently infer and analyze latent representational spaces (i.e., embeddings). However, the properties of a latent representation (e.g., prediction performance, interpretability, compactness) depend on the inference procedure, which can vary widely across endeavors. For example, dimensions are not always globally interpretable and the dimensionality of different embeddings may not be readily comparable. Moreover, the dichotomy between multidimensional spaces and purportedly richer representational formats, such as graph representations, is misleading. We review what the different notions of dimension in cognitive science imply for how these latent representations should be used and interpreted.

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.

Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results. Feature selection and feature extraction are two pivotal techniques employed to address these challenges by reducing the dimensionality of the data, thereby enhancing model performance, interpretability, and computational efficiency. Feature selection and feature extraction can be categorized into statistical and machine learning methods. The present study conducts a comprehensive and comparative review of various statistical, machine learning, and deep learning-based feature selection and extraction techniques specifically tailored for NGS and microarray data interpretation of humankind. A thorough literature search was performed to gather information on these techniques, focusing on array-based and NGS data analysis. Various techniques, including deep learning architectures, machine learning algorithms, and statistical methods, have been explored for microarray, bulk RNA-Seq, and single-cell, single-cell RNA-Seq (scRNA-Seq) technology-based datasets surveyed here. The study provides an overview of these techniques, highlighting their applications, advantages, and limitations in the context of high-dimensional NGS data. This review provides better insights for readers to apply feature selection and feature extraction techniques to enhance the performance of predictive models, uncover underlying biological patterns, and gain deeper insights into massive and complex NGS and microarray data.

Reduced-rank clustered coefficient regression for addressing multicollinearity in heterogeneous coefficient estimation.

Clustered coefficient regression (CCR) extends the classical regression model by allowing regression coefficients varying across observations and forming clusters of observations. It has become an increasingly useful tool for modeling the heterogeneous relationship between the predictor and response variables. A typical issue of existing CCR methods is that the estimation and clustering results can be unstable in the presence of multicollinearity. To address the instability issue, this paper introduces a low-rank structure of the CCR coefficient matrix and proposes a penalized non-convex optimization problem with an adaptive group fusion-type penalty tailor-made for this structure. An iterative algorithm is developed to solve this non-convex optimization problem with guaranteed convergence. An upper bound for the coefficient estimation error is also obtained to show the statistical property of the estimator. Empirical studies on both simulated datasets and a COVID-19 mortality rate dataset demonstrate the superiority of the proposed method to existing methods.

The affective grounds of the mind. The Affective Pertinentization (APER) model.

The paper presents the Affective Pertinentization model (APER), a theory of the affect and its role it plays in meaning-making. APER views the affect as the basic form of making sense of reality. It consists of a global, bipolar pattern of neurophysiological activity through which the organism maps the instant-by-instant variation of its environment. Such a pattern of neuropsychological activity is constituted by a plurality of bipolar affective dimensions, each of which maps a component of the environmental variability. The affect has a pluri-componential structure defining a multidimensional affective landscape that foregrounds (i.e., makes pertinent) a certain pattern of facets of the environment (e.g., its pleasantness/unpleasantness) relevant to survival, while backgrounding the others. Doing so, the affect grounds the following cognitive processes. Accordingly, meaning-making can be modeled as a function of the dimensionality of the affective landscape. The greater the dimensionality of the affective landscape, the more differentiated the system of meaning is. Following a brief review of current theories pertaining to the affect, the paper proceeds discussing the APER's core tenets - the multidimensional view of the affect, its semiotic function, and the concepts of Affective Landscape and Phase Space of Meaning. The paper then proceeds deepening the relationship between the APER model and other theories, highlighting how the APER succeeds in framing original conceptualizations of several challenging issues - the intertwinement between affect and sensory modalities, the manner in which the mind constitutes the content of the experience, the determinants of psychopathology, the intertwinement of mind and culture, and the spreading of affective forms of thinking and behaving in society. Finally, the unsolved issues and future developments of the model are briefly envisaged.

Prediction of laser printers and cartridges based on three-dimensional profiles via discrimination analysis.

Printer source prediction is an important task when examining questioned documents. While some research has provided methods to predict the source printer of documents, with the advent of compatible consumables, printer prediction could become more complex and difficult. Predicting the source printer after replacing cartridges and identifying the source of printer cartridges are unresolved issues that are rarely addressed in current research. Herein, we introduce a novel technique to predict the manufacturer, model, and cartridges of laser printers (i.e., compatible, and original cartridges) used to produce a given document. Document samples produced using eight laser printers and 247 cartridges were collected to establish a dataset. Common manufacturers included HP, Canon, Lenovo, and Epson. After obtaining white-light images and three-dimensional profile images of printed characters, a morphological analysis was conducted by questioned document examiners (QDEs) using microscopy. Microscopic image features across a series of images were also extracted and analyzed using algorithms. Then, six high-dimensional reduction algorithms were used to obtain between- and within-printer variations as well as between- and within-cartridge variations. Finally, we conducted principal component analysis (PCA) and discriminant analysis. For 40 % of the samples, mixed discrimination analysis (MDA) and fixed discrimination analysis (FDA) were employed to predict the manufacturer, model and cartridge of laser printers used to produce the questioned printed document; the remaining 60 % samples comprised the training dataset. In the prediction of manufacturer, model and cartridge, our method achieved mean accuracies of 95.5 %, 97.5 %, and 90.2 %, respectively. Hence, this technique could reasonably aid in predicting the manufacturer, model, and cartridge of a laser printer, even if different cartridges are loaded into printers.

Dimensionality and cross-language invariance of Pittsburgh Sleep Quality Index among Indian adolescents and adults.

Sleep quality, key to physical and mental health, requires regular assessment in clinical and non-clinical settings. Despite widespread use, the dimensionality of the Pittsburgh Sleep Quality Index (PSQI) is debated, and its Hindi version's factor structure remains unexplored. Our study evaluates the PSQI's dimensionality among Indian adolescents and adults aiming to demonstrate cross-language (Hindi and English) invariance of its factor structure. The PSQI showed satisfactory item reliability, and a best-fitting two-factor model: "sleep efficiency" (comprising sleep duration and habitual sleep efficiency), and "perceived sleep quality" (comprising remaining five PSQI components). This model showed configural invariance across age groups, sexes, and languages. Metric invariance was noted across age groups, but a partial metric non-invariance was observed across languages and sexes as reflected by differences in factor loadings. The second-order factor structure model had an excellent fit indicating the usefulness of aggregate scores of the two factors as a single index of sleep quality. Our findings better support a two-factor structure of sleep quality (both for English and Hindi versions of PSQI) in India. However, further validation in diverse clinical and non-clinical samples is warranted.

FlowAtlas: an interactive tool for high-dimensional immunophenotyping analysis bridging FlowJo with computational tools in Julia.

As the dimensionality, throughput and complexity of cytometry data increases, so does the demand for user-friendly, interactive analysis tools that leverage high-performance machine learning frameworks. Here we introduce FlowAtlas: an interactive web application that enables dimensionality reduction of cytometry data without down-sampling and that is compatible with datasets stained with non-identical panels. FlowAtlas bridges the user-friendly environment of FlowJo and computational tools in Julia developed by the scientific machine learning community, eliminating the need for coding and bioinformatics expertise. New population discovery and detection of rare populations in FlowAtlas is intuitive and rapid. We demonstrate the capabilities of FlowAtlas using a human multi-tissue, multi-donor immune cell dataset, highlighting key immunological findings. FlowAtlas is available at https://github.com/gszep/FlowAtlas.jl.git.

Design principles of multi-map variation in biological systems.

Complexity in biology is often described using a multi-map hierarchical architecture, where the genotype, representing the encoded information, is mapped to the functional level, known as the phenotype, which is then connected to a latent phenotype we refer to as fitness. This underlying architecture governs the processes driving evolution. Furthermore, natural selection, along with other neutral forces, can, in turn, modify these maps. At each level, variation is observed. Here, I propose the need to establish principles that can aid in understanding the transformation of variation within this multi-map architecture. Specifically, I will introduce three, related to the presence of modulators, constraints, and the modular channeling of variation. By comprehending these design principles in various biological systems, we can gain better insights into the mechanisms underlying these maps and how they ultimately contribute to evolutionary dynamics.

T-distributed Stochastic Neighbor Network for unsupervised representation learning.

Unsupervised representation learning (URL) is still lack of a reasonable operator (e.g. convolution kernel) for exploring meaningful structural information from generic data including vector, image and tabular data. In this paper, we propose a simple end-to-end T-distributed Stochastic Neighbor Network (TsNet) for URL with clustering downstream task. Concretely, our TsNet model has three major components: (1) an adaptive connectivity distribution learning module is presented to construct a pairwise graph for preserving the local structure of generic data; (2) a T-distributed stochastic neighbor embedding based loss function is designed to learn a transformation between embeddings and original data, which improves the discrimination of representations; (3) a nonlinear parametric mapping is learned via our TsNet on an unsupervised generalized manner, which can address the "out-of-sample" issue. By combining these components, our method is able to considerably outperform previous related unsupervised learning approaches on visualization and clustering of generic data. A simple deep neural network equipped on our model respectively achieves 74.90%, 76.56% ACC and NMI, which is 8% relative improvement over previous state-of-the-art on real single-cell RNA-sequencing (scRNA-seq) datasets clustering.

What does it mean for consciousness to be multidimensional? A narrative review.

A recent development in the psychological and neuroscientific study of consciousness has been the tendency to conceptualize consciousness as a multidimensional phenomenon. This narrative review elucidates the notion of dimensionality of consciousness and outlines the key concepts and disagreements on this topic through the viewpoints of several theoretical proposals. The reviewed literature is critically evaluated, and the main issues to be resolved by future theoretical and empirical work are identified: the problems of dimension selection and dimension aggregation, as well as some ethical considerations. This narrative review is seemingly the first to comprehensively overview this specific aspect of consciousness science.

Reliability of variability and complexity measures for task and task-free BOLD fMRI.

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.

Differences in cortical microstructure according to body mass index in neurologically healthy populations using structural magnetic resonance imaging.

Associations between brain structure and body mass index (BMI) are increasingly gaining attention. Although BMI-related regional alterations in brain morphology have been previously reported, the effect of BMI on the microstructural profiles, which provide information on the proxy of neuronal density within the cortex, is unexplored. In this study, we investigated the links between cortical layer-specific microstructural profiles and BMI in 302 neurologically healthy young adults. Using the microstructure-sensitive proxy based on the T1-and T2-weighted ratio, we estimated microstructural profile covariance (MPC) by calculating linear correlations of cortical depth-wise intensity profiles between different brain regions. Then, low-dimensional gradients of the MPC matrix were estimated using dimensionality reduction techniques, and the gradients were associated with BMI. Significant effects in the heteromodal association areas were observed. The BMI-gradient association map was related to the geodesic distance along the cortical surface, curvature, and sulcal depth, suggesting that the microstructural alterations occurred along the cortical topology. The BMI-gradient association map was further linked to cognitive states related to negative emotions. Our findings may provide insights into understanding the atypical cortical microstructure associated with BMI.

Advancing laser ablation assessment in hyperspectral imaging through machine learning.

Hyperspectral imaging (HSI) is gaining increasing relevance in medicine, with an innovative application being the intraoperative assessment of the outcome of laser ablation treatment used for minimally invasive tumor removal. However, the high dimensionality and complexity of HSI data create a need for end-to-end image processing workflows specifically tailored to handle these data. This study addresses this challenge by proposing a multi-stage workflow for the analysis of hyperspectral data and allows investigating the performance of different components and modalities for ablation detection and segmentation. To address dimensionality reduction, we integrated principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to capture dominant variations and reveal intricate structures, respectively. Additionally, we employed the Faster Region-based Convolutional Neural Network (Faster R-CNN) to accurately localize ablation areas. The two-stage detection process of Faster R-CNN, along with the choice of dimensionality reduction technique and data modality, significantly influenced the performance in detecting ablation areas. The evaluation of the ablation detection on an independent test set demonstrated a mean average precision of approximately 0.74, which validates the generalization ability of the models. In the segmentation component, the Mean Shift algorithm showed high quality segmentation without manual cluster definition. Our results prove that the integration of PCA, t-SNE, and Faster R-CNN enables improved interpretation of hyperspectral data, leading to the development of reliable ablation detection and segmentation systems.

Interaction of Th17 differentiations-related gene polymorphisms and environmental factors contributing to the disease classification, complications, and surgical risks of Crohn's disease in the Chinese Han population.

OBJECTIVES: Few studies have been conducted on gene-environment interactions in the Chinese population with Crohn's disease (CD). We aimed to investigate the association between single nucleotide polymorphisms (SNPs) on the T helper 17 (Th17) cell and CD susceptibility/performance in Chinese individuals. METHODS: We conducted a case-control and case-only study at the Peking Union Medical College Hospital. Four SNPs related to the Th17 cell pathway genes were prioritized, including rs2284553 (interferon gamma receptor 2), rs7517847 (interleukin 23 receptor), rs7773324 (interferon regulatory factor 4), and rs4263839 (tumor necrosis factor superfamily 15). SNP frequency was calculated, and gene-environment interaction was assessed by multifactor dimensionality reduction analysis. RESULTS: Altogether 159 CD patients and 316 healthy controls were included. All analyzed SNPs were found in Hardy-Weinberg equilibrium (P > 0.05). The frequency of rs2284553-A allele and rs4263839-A allele were lower in CD patients compared with controls (P < 0.05). While the rs4263839-A allele was more prevalent in ileocolonic CD patients than in those with isolated small intestinal or colonic disease (P = 0.035). Gene-environment interactions revealed associations between rs2284553 and breastfeeding, sunshine exposure, and fridge-stored food, affecting age at diagnosis, intestinal involvement, and intestinal stricture. Interaction of rs4263839 and breastfeeding influenced small intestinal lesions and intestinal stricture in CD. CONCLUSIONS: This study provided information on the genetic background in Chinese CD patients. Incorporating these SNPs into predictive models may improve risk assessment and outcome prediction. Gene-environment interaction contributes to the understanding of CD pathogenesis.

Bridging Histopathology and Radiomics Toward Prognosis of Metastasis in Early Breast Cancer.

Tumor histomorphology is crucial for the prognostication of breast cancer outcomes because it contains histological, cellular, and molecular tumor heterogeneity related to metastatic potential. To enhance breast cancer prognosis, we aimed to apply radiomics analysis-traditionally used in 3D scans-to 2D histopathology slides. This study tested radiomics analysis in a cohort of 92 breast tumor specimens for outcome prognosis, addressing -omics dimensionality by comparing models with moderate and high feature counts, using least absolute shrinkage and selection operator for feature selection and machine learning for prognostic modeling. In the test folds, models with radiomics features [area under the curves (AUCs) range 0.799-0.823] significantly outperformed the benchmark model, which only included clinicopathological (CP) parameters (AUC = 0.584). The moderate-dimensionality model with 11 CP + 93 radiomics features matched the performance of the highly dimensional models with 1,208 radiomics or 11 CP + 1,208 radiomics features, showing average AUCs of 0.823, 0.799, and 0.807 and accuracies of 79.8, 79.3, and 76.6%, respectively. In conclusion, our application of deep texture radiomics analysis to 2D histopathology showed strong prognostic performance with a moderate-dimensionality model, surpassing a benchmark based on standard CP parameters, indicating that this deep texture histomics approach could potentially become a valuable prognostic tool.