G. Elisabeta Marai

We present ConGAT, CONtext-aware Graph ATtention Network, a visual analysis system for identifying and analyzing cell-cell interactions in large tissue volumes, which leverages graph attention networks and self-supervised learning. Advanced multichannel microscopy techniques, such as Cyclic Immunofluorescence (CycIF), enable detailed, biomarker-based single-cell mapping, but yield large, densely packed volumes that are difficult to explore. ConGAT implements a machine learning-assisted visual analysis approach for these data. First, ConGAT constructs 3D spatial graphs from volumetric image data and applies a 3D graph attention network to model cell-cell relationships directly in 3D space. Next, we introduce a self-supervised feature refinement strategy that learns discriminative region-of-interest representations without labeled data. Through an interactive visual interface, ConGAT allows experts to define arbitrary combinations of biomarkers, enabling flexible ROIs identification and iterative exploration of spatial patterns. We assess ConGAT on synthetic ground truth and on real-world data. An evaluation with domain expert validation demonstrates that Con-GAT identifies spatial patterns and candidate ROIs that show substantial agreement with expert assessments of biological relevance.

We present ConGAT, a visual analysis system for identifying and analyzing cell–cell interactions in large tissue volumes, which leverages graph attention networks and self-supervised learning. Advanced multichannel microscopy techniques, such as Cyclic Immunofluorescence (CycIF), enable detailed, biomarker-based single-cell mapping, but yield large, densely packed volumes that are difficult to explore. ConGAT implements a machine learning-assisted visual analysis approach for these data. First, ConGAT constructs 3D spatial graphs from volumetric image data and applies a 3D graph attention network to model cell–cell relationships directly in 3D space. Next, we introduce a self-supervised feature refinement strategy that learns discriminative region-of-interest representations without labeled data. Through an interactive front-end, ConGAT allows experts to define arbitrary combinations of biomarkers, enabling flexible ROIs identification and iterative exploration of spatial patterns. We assess ConGAT on synthetic ground truth and on real-world data. An evaluation with domain expert validation demonstrates that ConGAT can automatically identify biologically meaningful interaction patterns that are difficult to detect with existing approaches.

Objective:Modeling normal tissue complication probability (NTCP) from dose–volume histogram (DVH) data, a 3D planning graph used in radiation therapy to summarize the radiation dose distribution within specific anatomical structures, remains challenging due to the high dimensionality of DVHs and the complex, multi-organ interactions underlying radiation-induced toxicities. Conventional approaches often rely on simplified DVH summaries or single-organ features, limiting their ability to capture full dose–volume relationships and inter-organ effects. This study proposes a self-supervised framework to learn discriminative representations from multi-organ DVH data for improved toxicity prediction. Methods:We developed a self-supervised contrastive clustering framework that learns compact, discriminative representations directly from full multi-organ DVH data. Structure-specific convolutional encoders independently process each organ’s DVH, and the resulting embeddings are jointly refined through a deep embedded clustering objective that promotes well-separated latent representations. The framework was applied to a cohort of head and neck cancer (HNC) patients treated at The University of Texas MD Anderson Cancer Center to predict patient-reported xerostomia severity at six months post-radiotherapy. Results:The framework identified three distinct dosimetric subgroups with a silhouette score of 0.60. Gradient-based attribution analysis revealed that the parotid glands and hard palate, particularly within low-to-intermediate dose ranges, were the primary contributors to cluster differentiation, consistent with established dose–response relationships for xerostomia. Incorporating cluster assignments into a logistic regression model improved prediction performance with an AUC of 0.74 compared to 0.66 using clinical variables alone, with cluster membership emerging as the strongest predictor.Conclusion:Self-supervised dosimetric representation learning can uncover clinically meaningful subgroups and enhance toxicity prediction. The proposed framework provides a principled and scalable alternative to handcrafted feature engineering for NTCP modeling.

Evolving measuring and computing capabilities, along with increasingly complex problems or models, are resulting in a new type of dataset: second‐generation ensemble data. Like first‐generation ensemble data, these data consist of large‐scale, repeated measurements of the same process or phenomenon and often have a spatial component. Unlike older datasets, they typically require the extraction and aggregation of novel complex features, which may be generated through direct measurements rather than simulations, and appear in a wider range of application domains, including neuroscience. We describe an interactive visual analysis solution for this type of second‐generation ensemble data, related to the study and planning of surgical interventions in epilepsy treatment. As part of this solution, we introduce a dynamic community abstraction in conjunction with analysis algorithms for feature extraction and aggregation, registration techniques to correlate and project sample data and custom visual encodings to support the analysis of conserved network patterns. A quantitative and qualitative evaluation with domain experts at four sites demonstrates the effectiveness of this solution. We discuss this approach and solution in the context of second‐generation ensemble data analysis, along with the challenges of working with this type of data.

This work presents the design of a simulator-driven imitation learning approach for sequential treatment decisions in head and neck cancer, built around Superhuman Policy Gradient Optimization (SPGO). Rather than simply replicating physicians’ actions, the method leverages a clinical simulator to generate complete patient trajectories and incorporates an inverse-reinforcement-learning–inspired loss that rewards policies for outperforming experts on key clinical outcomes, including relapse rates and long-term toxicities.

Normal tissue complication probability (NTCP) modeling using dose–volume histograms (DVHs) is fundamentally challenged by high dimensionality, severe multicollinearity, and substantial overlap between DVH profiles of patients with differing toxicity outcomes, limiting the effectiveness of classical classification approaches. We introduce SC–NTCP, a supervised contrastive learning framework that transforms DVH data into a compact, separable latent representation optimized for predicting osteoradionecrosis (ORN) in head and neck cancer patients. Rather than relying on raw, high-dimensional DVH features, SC–NTCP explicitly maximizes intra-class similarity and inter-class separability within the embedding space, enabling more accurate downstream classification. Using a cohort of head and neck cancer patients, we benchmarked SC–NTCP against logistic regression, support vector machines, multilayer perceptrons, and convolutional neural networks. SC–NTCP demonstrated superior discrimination (AUC = 0.77), improved calibration, and enhanced interpretability via gradient-based feature attribution, while the integration of clinical covariates further augmented predictive performance. By addressing the inherent limitations of DVH data, SC–NTCP offers a principled and interpretable approach for robust radiation toxicity prediction, with the potential to inform personalized treatment planning and improve clinical outcomes.

The visualization and analysis of street and pedestrian networks are important to various domain experts, including urban planners, climate researchers, and health experts. This has led to the development of new techniques for street and pedestrian network visualization, expanding possibilities for effective data presentation and interpretation. Despite their increasing adoption, there is no established design framework to guide the creation of these visualizations while addressing the diverse requirements of various domains. When exploring a feature of interest, domain experts often need to transform, integrate, and visualize a combination of thematic data (e.g., demographic, socioeconomic, pollution) and physical data (e.g., zip codes, street networks), often spanning multiple spatial and temporal scales. This not only complicates the process of visual data exploration and system implementation for developers but also creates significant entry barriers for experts who lack a background in programming. With this in mind, in this paper, we reviewed 45 studies utilizing street-overlaid visualizations to understand how they are applied in practice. Through qualitative coding of these visualizations, we analyzed three key aspects of street and pedestrian network visualization usage: their analytical purposes, the visualization approaches employed, and the data sources used in their creation. Building on this design space, we introduce StreetWeave, a declarative grammar for designing custom visualizations of multivariate spatial network data across multiple resolutions. We demonstrate how StreetWeave can be used to create various street-overlaid visualizations, enabling effective exploration and analysis of spatial data. StreetWeave is available at urbantk.org/streetweave

The rapidly expanding data generated by advanced methods in biology are creating enormous challenges at the interface between humans and biological data that can be represented and analyzed visually. To meet these challenges, a blend of methodology from the visualization, bioinformatics, and biology domains is required. This methodology encompasses perception and design knowledge, algorithm design, techniques for analyzing and visualizing Big Data, statistics, and specific domain knowledge for different application problems. Powerful and integrative visualization methods are necessary, as well as methods for visually communicating the outcome of computational analytical methods and findings. This special issue aims to highlight new challenges in biological data visualization (BioVis) and State-of-the-Art solutions created by the vibrant BioVis community.

Osteoradionecrosis of the jaw (ORNJ) is a radiation-induced late toxicity that can dramatically decrease patients’ quality of life. Recent increases in survival rates of head and neck cancers associated with human papillomavirus (HPV) infection have resulted in a higher frequency of radiation-induced toxicities, particularly ORNJ. Recent work with Normal Tissue Complication Probability (NTCP) models and a Weibull Accelerated Failure Time (WAFT) model have further developed our understanding of ORNJ clinical/dosimetric risk factors and longitudinal features, respectively. In this data descriptor, 1129 head and neck cancer (HNC) patients received curative intent radiotherapy (RT) at MD Anderson Cancer Center and were follow-up with clinical and radiological assessments at 3-6, 12, 18, 24 months, and then annually following the conclusion of RT for development of ORNJ. This data, in addition to the patients’ demographic, supplementary clinical, and dosimetric information was recorded in a comma-separated value file embedded within this data descriptor. This large, longitudinal dataset is a significant resource for further systematic analysis of post-RT normal tissue outcomes in HNC.