Integration Of Features Research Articles

Emerging and Biological Concepts in Pediatric High-Grade Gliomas.

Primary central nervous system tumors are the most frequent solid tumors in children, accounting for over 40% of all childhood brain tumor deaths, specifically high-grade gliomas. Compared with pediatric low-grade gliomas (pLGGs), pediatric high-grade gliomas (pHGGs) have an abysmal survival rate. The WHO CNS classification identifies four subtypes of pHGGs, including Grade 4 Diffuse midline glioma H3K27-altered, Grade 4 Diffuse hemispheric gliomas H3-G34-mutant, Grade 4 pediatric-type high-grade glioma H3-wildtype and IDH-wildtype, and infant-type hemispheric gliomas. In recent years, we have seen promising advancements in treatment strategies for pediatric high-grade gliomas, including immunotherapy, CAR-T cell therapy, and vaccine approaches, which are currently undergoing clinical trials. These therapies are underscored by the integration of molecular features that further stratify HGG subtypes. Herein, we will discuss the molecular features of pediatric high-grade gliomas and the evolving landscape for treating these challenging tumors.

Dual cross-enhancement network for highly accurate dichotomous image segmentation

The existing image segmentation tasks mainly focus on segmenting objects with specific characteristics, such as salient, camouflaged, and meticulous objects, etc. However, the research of highly accurate Dichotomous Image Segmentation (DIS) combining these tasks has just started and still faces problems such as insufficient information interaction between layers and incomplete integration of high-level semantic information and low-level detailed features. In this paper, a new dual cross-enhancement network (DCENet) for highly accurate DIS is proposed, which mainly consists of two new modules: a cross-scaling guidance (CSG) module and a semantic cross-transplantation (SCT) module. Specifically, the CSG module adopts the adjacent-layer cross-scaling guidance method, which can efficiently interact with the multi-scale features of the adjacent layers extracted; the SCT module uses dual-branch features to complement each other. Moreover, in the way of transplantation, the high-level semantic information of the low-resolution branch is used to guide the low-level detail features of the high-resolution branch, and the features of different resolution branches are effectively fused. Finally, experimental results on the challenging DIS5K benchmark dataset show that the proposed network outperforms the 9 state-of-the-art (SOTA) networks in 5 widely used evaluation metrics. In addition, the ablation experiments also demonstrate the effectiveness of the cross-scaling guidance module and the semantic cross-transplantation module.

Dynamic category-sensitive hypergraph inferring and homo-heterogeneous neighbor feature learning for drug-related microbe prediction.

The microbes in human body play a crucial role in influencing the functions of drugs, as they can regulate the activities and toxicities of drugs. Most recent methods for predicting drug-microbe associations are based on graph learning. However, the relationships among multiple drugs and microbes are complex, diverse, and heterogeneous. Existing methods often fail to fully model the relationships. In addition, the attributes of drug-microbe pairs exhibit long-distance spatial correlations, which previous methods have not integrated effectively. We propose a new prediction method named DHDMP which is designed to encode the relationships among multiple drugs and microbes and integrate the attributes of various neighbor nodes along with the pairwise long-distance correlations. First, we construct a hypergraph with dynamic topology, where each hyperedge represents a specific relationship among multiple drug nodes and microbe nodes. Considering the heterogeneity of node attributes across different categories, we developed a node category-sensitive hypergraph convolution network to encode these diverse relationships. Second, we construct homogeneous graphs for drugs and microbes respectively, as well as drug-microbe heterogeneous graph, facilitating the integration of features from both homogeneous and heterogeneous neighbors of each target node. Third, we introduce a graph convolutional network with cross-graph feature propagation ability to transfer node features from homogeneous to heterogeneous graphs for enhanced neighbor feature representation learning. The propagation strategy aids in the deep fusion of features from both types of neighbors. Finally, we design spatial cross-attention to encode the attributes of drug-microbe pairs, revealing long-distance correlations among multiple pairwise attribute patches. The comprehensive comparison experiments showed our method outperformed state-of-the-art methods for drug-microbe association prediction. The ablation studies demonstrated the effectiveness of node category-sensitive hypergraph convolution network, graph convolutional network with cross-graph feature propagation, and spatial cross-attention. Case studies on three drugs further showed DHDMP's potential application in discovering the reliable candidate microbes for the interested drugs. Source codes and supplementary materials are available at https://github.com/pingxuan-hlju/DHDMP.

SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch Embedding.

We propose a cotton pest and disease recognition method, SpemNet, based on efficient multi-scale attention and stacking patch embedding. By introducing the SPE module and the EMA module, we successfully solve the problems of local feature learning difficulty and insufficient multi-scale feature integration in the traditional Vision Transformer model, which significantly improve the performance and efficiency of the model. In our experiments, we comprehensively validate the SpemNet model on the CottonInsect dataset, and the results show that SpemNet performs well in the cotton pest recognition task, with significant effectiveness and superiority. The SpemNet model excels in key metrics such as precision and F1 score, demonstrating significant potential and superiority in the cotton pest and disease recognition task. This study provides an efficient and reliable solution in the field of cotton pest and disease identification, which is of great theoretical and applied significance.

3D mineral prospectivity modeling at the Axi epithermal gold deposit, NW China by using a feature adaptive fusion strategy

Mineralization distribution is always intricately affected by multiple ore-controlling geological units that play different roles in a mineral system (e.g., driver, trap, and throttle). How to effectively balance and integrate ore-controlling features from various 3D geological models during 3D mineral prospectivity modeling (MPM) is still a challenging task. In this paper, we introduce a novel approach, the feature adaptive fusion convolutional neural networks (CNN), which is designed to learn multiple 3D geological models with ore-controlling functions. The method is validated in the Axi epithermal gold deposit, northwestern China that mineralization distribution is jointly controlled by fault, volcanic phase, and phyllic alteration. The geology units are firstly constructed by explicit-implicit modeling and their ore-controlling features are subsequently described by high-frequency Laplace-Beltrami eigenfunctions and reassembled into multi-channel images as input to CNN. To learn the differences in ore-controlling effects among various geological units, we designed a fully connected layer to achieve adaptive quantification and weighted integration of the ore-controlling features by automatically optimizing weight allocation parameters and bias vectors using the neural network intelligence. Comparison results between the proposed method and other prospectivity methods suggest that the feature adaptive fusion CNN produces more reliable predictions, characterized by: (1) high consistency with known mineralization, (2) the highest AUC value and success rate, and (3) accurate prediction of deep voxels explored by drilling. Therefore, the proposed method effectively integrates the ore-controlling effects of multiple geological units and is suitable for complex scenarios of 3D MPM. Utilizing the prospectivity results generated by our method, we identified five potential mineralization in the Axi gold deposit, laying a robust foundation for future gold exploration.

Visual Recognition Method for Lateral Swing of the Tail Rope

The large lateral displacement of tail ropes increases the risk of their wear and fracture, posing hidden dangers to the safety of the hoisting system. However, no suitable method is available to recognize the lateral swing of tail ropes online. A target-free visual measurement method, which includes the dual-branch SiamSeg, was proposed in this study. Considering the slender characteristics of tail ropes, the receptive field of the feature extraction network was enhanced via the Receptive Field Module (RFM), thereby strengthening the discriminability and integrity of tail rope features. The consistency loss constraints were added to the segmentation loss function to maximize the time sequence information of the video and further improve the accuracy of pixel-level displacement. Compared with other methods, the proposed approach achieved better segmentation effects. Comparison results synchronously measured by sensors revealed the effectiveness of this method and its potential for practical underground applications.

Ethnic integration and evolution of the phonological features of Northern Standard Chinese Language

ABSTRACT: Ethnic integration is often accompanied by language contact and language shift. The process of "ethnic integration" is the process of "remolding" northern standard Chinese language into an inter-ethnic language and ultimately into a common national language. Only by reevaluating the phonological rules and structure of the languages (mainly Altaic) of northern non-Han ethnic groups and investigating the interference with and influence on Chinese during the language shift by the non-Han ethnic groups, can we comprehend that the common national language is a language community jointly shaped by the Han people and other ethnic groups during ethnic integration. The speakers of ethnic languages are, therefore, not only the transmitters but also the co-modelers in forming the common national language. 摘要: 民族融合往往伴随着语言接触和语言转换。"民族融合"的过程就是 将北方标准汉语"重塑"为族际共同语进而成为民族共同语的过程。 只有基于以阿尔泰语为主的北方诸民族语音系规则和音韵结构特征重 新思考并深入研究以阿尔泰为主的北方诸民族在转用汉语过程中对汉 语形成的干扰和影响，才能理解民族共同语是在民族融合过程中汉族 与其他民族共同塑造的语言共同体。在民族共同语形成的过程中，民 族语者不仅是民族共同语的传播者，也是民族共同语的共同塑造者。

Apple recognition in complex environments based on FC-DETR

This study aims to address the challenges of apple recognition in complex environments by proposing a solution based on the FC-DETR model. Apple cultivation is an important part of agriculture in Xinjiang, but the increasing shortage of labor has driven the demand for automated harvesting technologies. Therefore, research and development of apple recognition technology in complex environments have become crucial. While CNN architecture models can accurately identify apples in conventional settings, their inherent receptive field limitations prevent them from fully capturing global features in noisy and complex environments, making it difficult to achieve the accuracy and robustness required for practical applications. To solve this problem, this study proposes a real-time object detection model, FC-DETR. This model incorporates the innovative FEMA-BasicBlock residual module, the CAFM cross-scale adaptive feature fusion module, and the novel Inner-WIoU loss function, thereby enhancing feature processing, multi-scale feature selection and integration, and detection accuracy. Experimental results show that the FC-DETR model achieves an apple recognition accuracy of 87 % and a recall rate of 82 % in complex backgrounds while maintaining a lightweight design. This study not only makes significant advances in automated apple harvesting technology but also contributes to improving the efficiency and sustainability of the apple industry in Xinjiang and globally.

Using Paradata for Imputation of Missing Values in Sociological Survey Data: Results of Statistical Modeling (Case of Croatia and Slovakia)

Missing values are a common issue in quantitative social researches. One of the ways to handle missing data is by data imputation. This article outlines the challenges of traditional data imputation methods, which often introduce biases, and presents an advanced approach that features integration of paradata—auxiliary information collected during surveys—into the imputation process, using the European Social Survey (ESS) as its dataset. It is proposed that the usage of paradata could enhance predictive models used for imputation. It discusses the practical applications of data imputation, particularly through the lens of sensitive topics such as LGBT issues in socially conservative countries, where missingness could be heavily skewed due to social inacceptability of certain answers. To evaluate the effectiveness of the proposed approach towards imputation, the research employs the approach of using the 'ideal dataset', which is a subset of the original dataset with no missing vales, and then introduces artificial missing values that are not MCAR (Missing Completely at Random) to simulate the real case of missing data. Having artificial missingness allows for evaluation of the imputation procedure by comparing it with the original dataset. The study uses a novel approach towards creation of realistic missing data patterns through clustering based on response patterns. The research uses advanced statistical methods to handle missing data, and incorporates paradata from the survey process to improve the accuracy of predictive models. By comparing statistical metrics such as RMSE, MAE, and R-squared, the article evaluates the effectiveness of these methods in mimicking the original dataset's variability.

Hyperspectral image classification with spectral-spatial feature integration and ensemble learning

<p>Hyperspectral imaging (HSI) has emerged as a robust remote sensing and medical imaging tool. However, HSI classification remains a challenging problem due to the high-dimensional data and the need for efficient feature selection and enhancement techniques. The proposed work addresses the problem of spatial feature extraction in spectral-spatial HSI classification tasks. This paper introduces an innovative model addressing the intricacies of spatial feature extraction in spectral-spatial HSI classification tasks, employing a fusion of spectral and spatial features through an adaptive kernel-based Gaussian filtering mechanism to elevate the quality of HSI data and augment classification performance. The classification is executed using three distinct classifiers, whose decisions are harmoniously integrated within an ensemble learning framework to optimize outcomes. The effectiveness of the proposed system is meticulously evaluated across three diverse datasets, Indian Pine, Pavia, and Salinas. This study also compares the model's efficiency against the existing similar work presented in the literature. The results show that the proposed work outperforms existing methods with constantly showing 99% accuracy and kappa score for each dataset, demonstrating its potential applications in various domains such as remote sensing and medical imaging.</p>

BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion.

Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment decisions and improving patient outcomes. Here we present BioFusionNet, a deep learning framework that fuses image-derived features with genetic and clinical data to obtain a holistic profile and achieve survival risk stratification of ER+ breast cancer patients. We employ multiple self-supervised feature extractors (DINO and MoCoV3) pretrained on histopathological patches to capture detailed image features. These features are then fused by a variational autoencoder and fed to a self-attention network generating patient-level features. A co-dual-cross-attention mechanism combines the histopathological features with genetic data, enabling the model to capture the interplay between them. Additionally, clinical data is incorporated using a feed-forward network, further enhancing predictive performance and achieving comprehensive multimodal feature integration. Furthermore, we introduce a weighted Cox loss function, specifically designed to handle imbalanced survival data, which is a common challenge. Our model achieves a mean concordance index of 0.77 and a time-dependent area under the curve of 0.84, outperforming state-of-the-art methods. It predicts risk (high versus low) with prognostic significance for overall survival in univariate analysis (HR=2.99, 95% CI: 1.88-4.78, p 0.005), and maintains independent significance in multivariate analysis incorporating standard clinicopathological variables (HR=2.91, 95% CI: 1.80-4.68, p 0.005).

Symmetrical Siamese Network for pose-guided person synthesis

Pose-Guided Person Image Synthesis (PGPIS) aims to generate a realistic person image that preserves the appearance of the source person while adopting the target pose. Various appearances and drastic pose changes make this task highly challenging. Due to the insufficient utilization of paired data, existing models face difficulties in accurately preserving the source appearance details and high-frequency textures in the generated images. Meanwhile, although current popular AdaIN-based methods are advantageous in handling drastic pose changes, they struggle to capture diverse clothing shapes imposed by the limitation of global feature statistics. To address these issues, we propose a novel Symmetrical Siamese Network (SSNet) for PGPIS, which consists of two synergistic symmetrical generative branches that leverage prior knowledge of paired data to comprehensively exploit appearance details. For feature integration, we propose a Style Matching Module (SMM) to transfer multi-level region appearance styles and gradient information to the desired pose for enriching the high-frequency textures. Furthermore, to overcome the limitation of global feature statistics, a Spatial Attention Module (SAM) is introduced to complement the SMM for capturing clothing shapes. Extensive experiments show the effectiveness of our SSNet, achieving state-of-the-art results on public datasets. Moreover, our SSNet can also edit the source appearance attributes, making it versatile in wider application scenarios.

An automated sample generation method by integrating phenology domain optical-SAR features in rice cropping pattern mapping

Accurate spatio-temporal information on rice cropping patterns is vital for predicting grain production, managing water resource and assessing greenhouse gas emissions. However, current automated mapping of rice cropping patterns at regional scale is heavily constrained by insufficient training samples and frequent cloudy weathers in major rice-producing areas. To tackle this challenge, we proposed a Phenology domain Optical-SAR feature inTegration method to Automatically generate single (SC-Rice) and double cropping Rice (DC-Rice) sample (POSTAR) for efficient and refined rice mapping. POSTAR includes three major steps: (1) generating a potential rice map using a phenology- and object-based classification method with optical data (Sentinel-2 MSI) to select candidate rice samples; (2) employing K-means to identify SC- and DC-Rice candidate samples according to unique SAR-based (Sentinel-1 SAR) phenological features; (3) implementing a two-step refinement strategy to filter high-confidence SC- and DC-Rice samples, maintaining a balance between intraclass phenological variance and sample purity. Test areas selected for validation include the Dongting Lake plain and Poyang Lake plain in South China, as well as Fujin county located in the Sanjiang plain of North China. POSTAR proved effective in producing reliable SC- and DC-Rice samples, achieving a high spectral correlation similarity (>0.85) and low dynamic time wrapping distance (<8.5) with field samples. Applying POSTAR-derived samples to random forest classifier yielded an overall accuracy of 89.6%, with F1 score of 0.899 for SC-Rice and 0.938 for DC-Rice in the Dongting Lake plain. Owing to the incorporation of knowledge-based optical and SAR phenological features, POSTAR exhibited strong spatial transferability, achieving an overall accuracy of 96.0% in the Poyang Lake plain and 97.8% in the Fujin county. These results demonstrated the effectiveness of the POSTAR method in accurately mapping rice cropping patterns without extensive field visits, providing valuable insights for crop monitoring in large, diverse, and cloudy regions through the integration of optical and SAR data.

SwinShadow: Shifted Window for Ambiguous Adjacent Shadow Detection

Shadow detection is a fundamental and challenging task in many computer vision applications. Intuitively, most shadows come from the occlusion of light by the object itself, resulting in the object and its shadow being contiguous (referred to as the adjacent shadow in this paper). In this case, when the color of the object is similar to that of the shadow, existing methods struggle to achieve accurate detection. To address this problem, we present SwinShadow, a transformer-based architecture that fully utilizes the powerful shifted window mechanism for detecting adjacent shadows. The mechanism operates in two steps. Initially, it applies local self-attention within a single window, enabling the network to focus on local details. Subsequently, it shifts the attention windows to facilitate inter-window attention, enabling the capture of a broader range of adjacent information. These combined steps significantly improve the network's capacity to distinguish shadows from nearby objects. And the whole process can be divided into three parts: encoder, decoder, and feature integration. During encoding, we adopt Swin Transformer to acquire hierarchical features. Then during decoding, for shallow layers, we propose a deep supervision (DS) module to suppress the false positives and boost the representation capability of shadow features for subsequent processing, while for deep layers, we leverage a double attention (DA) module to integrate local and shifted window in one stage to achieve a larger receptive field and enhance the continuity of information. Ultimately, a new multi-level aggregation (MLA) mechanism is applied to fuse the decoded features for mask prediction. Extensive experiments on three shadow detection benchmark datasets, SBU, UCF, and ISTD, demonstrate that our network achieves good performance in terms of balance error rate (BER). The source code and results are now publicly available at https://github.com/harrytea/SwinShadow .

Retrieving ground-level PM2.5 concentrations in China (2013–2021) with a numerical-model-informed testbed to mitigate sample-imbalance-induced biases

Abstract. Ground-level PM2.5 data derived from satellites with machine learning are crucial for health and climate assessments. However, uncertainties persist due to the absence of spatially covered observations. To address this, we propose a novel testbed using nontraditional numerical simulations to evaluate PM2.5 estimation across the entire spatial domain. The testbed emulates the general machine-learning approach by training the model with grids corresponding to ground monitoring sites and subsequently testing its predictive accuracy for other locations. Our approach enables comprehensive evaluation of various machine-learning methods' performance in estimating PM2.5 across the spatial domain for the first time. Unexpected results are shown in the application in China, with larger absolute PM2.5 biases found in densely populated regions with abundant ground observations across all benchmark models due to the higher baseline concentration, though the relative error (approximately 20 %) is smaller compared to that in rural areas (over 50 %). The imbalance in training samples, mostly from urban areas with high emissions, is the main reason, leading to significant overestimation due to the lack of monitors in downwind areas where PM2.5 is transported from urban areas with varying vertical profiles. Our proposed testbed also provides an efficient strategy for optimizing model structure or training samples to enhance satellite-retrieval model performance. Integration of spatiotemporal features, especially with conventional neural network (CNN)-based deep-learning approaches like the residual neural network (ResNet) model, has successfully mitigated PM2.5 overestimation (by 5–30 µg m−3) and the corresponding exposure (by 3 million people ⋅ µg m−3) in the downwind area over 9 years (2013–2021) compared to the traditional approach. Furthermore, the incorporation of 600 strategically positioned ground monitoring sites identified through the testbed is essential for achieving a more balanced distribution of training samples, thereby ensuring precise PM2.5 estimation and facilitating the assessment of the associated impacts in China. In addition to presenting the retrieved surface PM2.5 concentrations in China from 2013 to 2021, this study provides a testbed dataset derived from physical modeling simulations which can serve to evaluate the performance of data-driven methodologies, such as machine learning, in estimating spatial PM2.5 concentrations for the community (Li et al., 2024a; https://doi.org/10.5281/zenodo.11122294).

A Learning Dendritic Neuron-Based Motion Direction Detective System and Its Application to Grayscale Images.

In recent research, dendritic neuron-based models have shown promise in effectively learning and recognizing object motion direction within binary images. Leveraging the dendritic neuron structure and On-Off Response mechanism within the primary cortex, this approach has notably reduced learning time and costs compared to traditional neural networks. This paper advances the existing model by integrating bio-inspired components into a learnable dendritic neuron-based artificial visual system (AVS), specifically incorporating mechanisms from horizontal and bipolar cells. This enhancement enables the model to proficiently identify object motion directions in grayscale images, aligning its threshold with human-like perception. The enhanced model demonstrates superior efficiency in motion direction recognition, requiring less data (90% less than other deep models) and less time for training. Experimental findings highlight the model's remarkable robustness, indicating significant potential for real-world applications. The integration of bio-inspired features not only enhances performance but also opens avenues for further exploration in neural network research. Notably, the application of this model to realistic object recognition yields convincing accuracy at nearly 100%, underscoring its practical utility.

Enhanced 3D object detection for autonomous driving: A spatial-temporal alignment approach in Bird's Eye View scenarios

This paper presents a novel 3D object detection algorithm designed for Bird's Eye View (BEV) scenarios, which significantly improves detection capabilities by integrating spatial and temporal features. The core of our approach is the spatial-temporal alignment module that efficiently processes information across different time steps and spatial locations, enhancing the precision and robustness of object detection. We employ a temporal self-attention mechanism to capture the motion information of objects over time, allowing the model to correlate features across various time steps for identifying and tracking moving objects. Additionally, a spatial cross-attention mechanism is utilized to focus on spatial features within regions of interest, promoting interactions between features extracted from camera views and BEV queries. Our method also implements temporal feature integration and multi-scale feature fusion to enhance detection stability and accuracy for fast-moving objects and to capture multi-scale context information, respectively. The model employs an enriched feature set post alignment for 3D bounding box prediction, ascertaining the position, dimensions, and orientation of objects. We conducted experiments on two public datasets for autonomous driving nuScenes and Waymo Open Dataset, demonstrating that our method outperforms previous BEVFormer and other state-of-the-art methods in terms of detection accuracy and robustness. The paper concludes with potential future directions for optimizing the BEVFormer model's performance and exploring its application in broader scenarios and tasks.

Generation (not production) improves the fidelity of visual representations in picture naming.

The production effect refers to the finding that participants better remember items read aloud than items read silently. This pattern has been attributed to aloud items being relatively more distinctive in memory than silent items, owing to the integration of additional sensorimotor features within the encoding episode that are thought to facilitate performance at test. Other theorists have instead argued that producing an item encourages additional forms of processing not limited to production itself. We tested this hypothesis using a modified production task where participants named monochromatic line drawings aloud or silently either by generating the names themselves (no label condition) or reading a provided label (label condition). During a later test, participants were presented with each line drawing a second time and required to reproduce the original color and location using a continuous slider. Production was found to improve memory for visual features, but only when participants were required to generate the label themselves. Our findings support the notion that picture naming improves memory for visual features; however, this benefit appears to be driven by factors related to response generation rather than production itself.

Catalytic Application of POSS–COF-[(Co(acetate)2] for Selective Reduction of Nitriles to Amines

We report the reticular synthesis and structural investigations through the spectroscopic analysis of a novel polyhedral oligomeric silsesquioxane (POSS)-modified framework, hereby ascribed as a catalyst for the selective reduction of aryl nitriles to amines. The integration of the unique features of the polyhedral oligomeric silsesquioxane with 2,2′-Bipyridine-4,4′-dicarboxaldehyde and subsequently coordination to cobalt acetate manifests a distinctive feature, which is a stable covalent bond between Co and the functionalized POSS, effectively preventing catalyst leaching. The cobalt acetate-modified POSS–COF, synthesized with this approach, underwent a comprehensive characterization employing various analytical techniques including FTIR, XRD, SEM, XPS, TGA, and 29Si NMR. This thorough characterization provides a detailed insight into the structural and chemical attributes of the catalyst. Our catalyst, with its exceptional catalytic efficiency in catalyzing reduction reactions compared to its homogeneous counterparts, and its distinctive three-dimensional metalated POSS system, shows outstanding catalytic performance attributed to its diverse coordination interactions with ligands. Moreover, this catalyst presents additional merits, such as facile recovery and recyclability, making it a promising candidate for sustainable and efficient catalytic processes and thus instilling hope for a greener future.

When do I ask for a DNA methylation array for primary brain tumor diagnosis?

Despite remarkable advances in molecular characterization, the diagnosis of brain tumors remains challenging, particularly in cases with ambiguous histology or contradictory molecular features. In this context, DNA methylation profiling plays an important role in improving diagnostic and prognostic accuracy. This review aims to provide diagnostic guidance regarding when DNA methylation arrays represent a useful tool for the diagnosis of primary brain tumors. Large-scale profiling has revealed that DNA methylation profiles of brain tumors are highly reproducible and stable. Therefore, DNA methylation profiling has been successfully used to classify brain tumors and identify new entities. This approach seems to be particularly promising for heterogeneous groups of tumors, such as IDH-wildtype gliomas, and glioneuronal and embryonal tumors, which include a variety of entities that are still under characterization. As underlined in the fifth edition of the WHO classification of central nervous system tumors, the diagnosis of brain tumors requires the integration of histological, molecular, clinical, and radiological features. Although advanced imaging and histological examination remain the standard diagnostic tools, DNA methylation analysis can significantly improve diagnostic accuracy, with a substantial impact on patient management.