Visualization Tools Research Articles

Modelling teachers’ reflective practice

ABSTRACT Previous research highlights challenges in modelling teachers’ reflective practice to explore and identify their beliefs and reflections. This paper explores how methodical triangulation, combining reflective dialogues and various reflective tools, enhances teachers’ awareness of their beliefs and reflections. It facilitates a deeper exploration of teachers’ beliefs and reflections related to learning and teaching. The study is based on a descriptive analysis of reflections generated in three different individual reflective dialogues with six lower secondary teachers. The findings indicate that triangulation strengthens the teachers’ reflections, particularly through using visual reflection tools and the researcher as a “reflective catalyst’. This combination increases their awareness of their own beliefs and encourages reflection.

LOCC: a novel visualization and scoring of cutoffs for continuous variables with hepatocellular carcinoma prognosis as an example

BackgroundThe interpretation of large datasets, such as The Cancer Genome Atlas (TCGA), for scientific and research purposes, remains challenging despite their public availability. In this study, we focused on identifying gene expression profiles most relevant to patient prognosis and aimed to develop a method and database to address this issue. To achieve this, we introduced Luo’s Optimization Categorization Curve (LOCC), an innovative tool for visualizing and scoring continuous variables against dichotomous outcomes. To demonstrate the efficacy of LOCC using real-world data, we analyzed gene expression profiles and patient data from TCGA hepatocellular carcinoma samples.ResultsTo showcase LOCC, we demonstrate an optimal cutoff for E2F1 expression in hepatocellular carcinoma, which was subsequently validated in an independent cohort. Compared to ROC curves and their AUC, LOCC offered a superior description of the predictive value of E2F1 expression across various cancer types. The LOCC score, comprised of factors representing significance, range, and impact of the biomarker, facilitated the ranking of all gene expression profiles in hepatocellular carcinoma, aiding in the evaluation and understanding of previously published prognostic gene signatures. We also demonstrate that LOCC does not have the same assumptions required of Cox proportional hazards modeling for accurate analysis. Repeated sampling demonstrated that LOCC scores outperformed ROC’s AUC in discriminating predictors from non-predictors. Additionally, gene set enrichment analysis revealed significant associations between certain genes and prognosis, such as E2F target genes and G2M checkpoint with poor prognosis, and bile acid metabolism and oxidative phosphorylation with good prognosis.ConclusionIn summary, we present LOCC as a novel visualization tool for the analysis of gene expression in cancer, particularly for understanding and selecting cutoffs. Our findings suggest that LOCC scores, which effectively rank genes based on their prognostic potential, represent a more suitable approach than ROC curves and Cox proportional hazard for prognostic modeling and understanding in cancer gene expression analysis. LOCC holds promise as an invaluable tool for advancing precision medicine and furthering biomarker research. Further research regarding multivariable integration and validation will help LOCC reach its full potential and establish its utility across diverse cancer types and clinical settings.Graphical abstract

Automated image clarity detection for the improvement of colposcopy imaging with multiple devices

The proportion of women dying from cervical cancer in middle- and low-income countries is over 60%, twice that of their high-income counterparts. A primary screening strategy to eliminate this burden is cervix visualization and application of 3–5% acetic acid, inducing contrast in potential lesions. Recently, machine learning tools have emerged to aid visual diagnosis. As low-cost visualization tools expand, it is important to maximize image quality at the time of the exam or of images used in algorithms. Objective: We present the use of an object detection algorithm, the YOLOv5 model, to localize the cervix and describe blur within a multi-device image database. Methods: We took advantage of the Fourier domain to provide pseudo-labeling of training and testing images. A YOLOv5 model was trained using Pocket Colposcope, Mobile ODT EVA, and standard of care digital colposcope images. Results: When tested on all devices, this model achieved a mean average precision score, sensitivity, and specificity of 0.9, 0.89, and 0.89, respectively. Mobile ODT EVA and Pocket Colposcope hold out sets yielded mAP scores of 0.81 and 0.83, respectively, reflecting the generalizability of the algorithm. Compared to physician annotation, it yielded an accuracy of 0.72. Conclusion: This method provides an informed quantitative, generalizable analysis of captured images that is highly concordant with expert annotation. Significance: This quality control framework can assist in the standardization of colposcopy workflow, data acquisition, and image analysis and in doing so increase the availability of usable positive images for the development of deep learning algorithms.

MammOnc-DB, an integrative breast cancer data analysis platform for target discovery.

Breast cancer (BCa) is one of the most common malignancies among women worldwide. It is a complex disease that is characterized by morphological and molecular heterogeneity. In the early stages of the disease, most BCa cases are treatable, particularly hormone receptor-positive and HER2-positive tumors. Unfortunately, triple-negative BCa and metastases to distant organs are largely untreatable with current medical interventions. Recent advances in sequencing and proteomic technologies have improved our understanding of the molecular changes that occur during breast cancer initiation and progression. In this era of precision medicine, researchers and clinicians aim to identify subclass-specific BCa biomarkers and develop new targets and drugs to guide treatment. Although vast amounts of omics data including single cell sequencing data, can be accessed through public repositories, there is a lack of user-friendly platforms that integrate information from multiple studies. Thus, to meet the need for a simple yet effective and integrative BCa tool for multi-omics data analysis and visualization, we developed a comprehensive BCa data analysis platform called MammOnc-DB (http://resource.path.uab.edu/MammOnc-Home.html), comprising data from more than 20,000 BCa samples. MammOnc-DB was developed to provide a unique resource for hypothesis generation and testing, as well as for the discovery of biomarkers and therapeutic targets. The platform also provides pre- and post-treatment data, which can help users identify treatment resistance markers and patient groups that may benefit from combination therapy.

FullProfAPP: a graphical user interface for the streamlined automation of powder diffraction data analysis

FullProfAPP is a software tool for data processing, refinement and visualization of large collections of powder diffraction patterns. Featuring an intuitive graphical user interface, it seamlessly facilitates a variety of tasks. These include conducting full-profile phase searches, sequential and high-throughput Rietveld refinements, and managing background (and peak) detection. FullProfAPP also provides convenient interaction with crystallographic databases and supports the visualization and export of high-quality pixel and vector graphics depicting the refinement results, among other functionalities. FullProfAPP wraps around the refinement program FullProf [Rodríguez-Carvajal (1993), Physica B, 192, 55–69] and offers the flexibility of user-defined workflows by accessing and editing FullProf's input files and triggering its execution as necessary. FullProfAPP is distributed as open-source software and is presently available for Windows and Linux operating systems.

A Geographically Weighted Regression–Compute Unified Device Architecture Approach to Explore the Spatial Agglomeration and Heterogeneity in Arable Land Consumption in Southwest China

Arable land loss has become a critical issue in China because of rapid urbanization, industrial expansion, and unsustainable agricultural practices. While previous studies have explored the factors contributing to this loss, they often fall short in addressing the challenges of spatial heterogeneity and large-scale dataset analysis. This research introduces an innovative approach to geographically weighted regression (GWR) for assessing arable land loss in China, effectively addressing these challenges. Focusing on Chongqing, Guizhou, and Yunnan Provinces over the past two decades, it examines spatial autocorrelation with R-squared values exceeding 0.6 and residuals. Eight factors, including environmental elements (rain, evaporation, slope, digital elevation model) and human activities (distance to city, distance to roads, population, GDP), were analyzed. By visualizing and analyzing R² spatial patterns, the results reveal a clear spatial agglomeration distribution, primarily in urban areas with industries, highly urbanized cities, and flat terrains near rivers, influenced by GDP, population, rain, and slope. The novelty of this study is that it significantly enhances GWR computational capabilities for handling extensive datasets by utilizing Compute Unified Device Architecture (CUDA) on a high-performance GPU cloud server. Simultaneously, it conducts comprehensive analyses of the GWR model’s local results through visualization and spatial autocorrelation tools, enhancing the interpretability of the GWR model. Through spatial clustering analysis of local results, this study enables targeted exploration of factors influencing arable land changes in various temporal and spatial dimensions while also evaluating the reliability of the model results.

Visualizing Research Trends in Quantum Dots for Health: A Bibliometric Exploration.

Quantum dots (QDs) are semiconductor nanoparticles with immense potential accompanied by unique optoelectronic properties for revolutionizing several biomedical applications related to imaging, diagnostics, drug delivery, and therapy. A detailed bibliometric analysis has been performed in this article with regard to research relating to QDs in the health sector during 2004-2024. In spite of their promising applications, assessment of the toxicity of QDs, especially highly toxic heavy metal-based QDslike cadmium, is still the most important apprehension. This analysis identifies from Scopus data key trends, influential authors, leading sources, and significant collaborative networks in the field. On VOSviewer and Biblioshiny, visualization tools are used to show research trends and networks that help in the discovery of critical knowledge about the entire global landscape of research. The increasing scientific interest has been realized in a stable trend with peaks of notable and emerging topics attesting to its dynamism. The study epitomizes how international collaboration can advance the QD applications boundary for healthcare.

A comparison of data visualization tools: A case study in health-related research

Data visualization tools are important tools for making health decisions based on large amounts of data. However, very few comparisons of data visualization tools have been done in the context of health-related research. This paper compares commonly available visualization tools from the perspective of a dashboard developer in the context of scientific health research using real-world data and to provide general suggestions for dashboard developers’ future visualization efforts in health-related scientific discoveries. We evaluated four commonly available visualization tools (Tableau, Spotfire, PowerBI, and R Shiny). Each tool was evaluated on visualization outcomes (comparing the generated outputs of tables, scatterplots, correlation plots, treemaps, heatmaps, and geographic maps), usability, Cloud compatibility, analytic integration, and a few other factors. All tools generated comparable visualization outcomes in our tests and were equally usable with the exception of R Shiny which is not recommended for users with limited coding experience. Every tool except R Shiny supports connections to PostgreSQL, gSheets, and Athena without additional configurations. All tools support integration with R and Python; however, PowerBI requires some additional configuration. Every tool is capable of data pre-processing and supports data transformation, null detection and outlier detection. Each tool has its own unique advantages and limitations.

A Generative Adversarial Network for Upsampling of Direct Volume Rendering Images

AbstractDirect volume rendering (DVR) is an important tool for scientific and medical imaging visualization. Modern GPU acceleration has made DVR more accessible; however, the production of high‐quality rendered images with high frame rates is computationally expensive. We propose a deep learning method with a reduced computational demand. We leveraged a conditional generative adversarial network (cGAN) to upsample DVR images (a rendered scene), with a reduced sampling rate to obtain similar visual quality to that of a fully sampled method. Our dvrGAN is combined with a colour‐based loss function that is optimized for DVR images where different structures such as skin, bone, etc. are distinguished by assigning them distinct colours. The loss function highlights the structural differences between images, by examining pixel‐level colour, and thus helps identify, for instance, small bones in the limbs that may not be evident with reduced sampling rates. We evaluated our method in DVR of human computed tomography (CT) and CT angiography (CTA) volumes. Our method retained image quality and reduced computation time when compared to fully sampled methods and outperformed existing state‐of‐the‐art upsampling methods.

ProDiVis: a method to normalize fluorescence signal localization in 3D specimens.

A common problem in confocal microscopy is the decrease in intensity of excitation light and emission signal from fluorophores as they travel through 3D specimens, resulting in decreased signal detected as a function of depth. Here, we report a visualization program compatible with widely used fluorophores in cell biology to facilitate image interpretation of differential protein disposition in 3D specimens. Glioblastoma cell clusters were fluorescently labeled for mitochondrial complex I (COXI), P2X7 receptor (P2X7R), β-Actin, Ki-67, and DAPI. Each cell cluster was imaged using a laser scanning confocal microscope. We observed up to ∼70% loss in fluorescence signal across the depth in Z-stacks. This progressive underrepresentation of fluorescence intensity as the focal plane deepens hinders an accurate representation of signal location within a 3D structure. To address these challenges, we developed ProDiVis: a program that adjusts apparent fluorescent signals by normalizing one fluorescent signal to a reference signal at each focal plane. ProDiVis serves as a free and accessible, unbiased visualization tool to use in conjunction with fluorescence microscopy images and imaging software.

Human Mesofluidic Intestinal Model for Studying Transport of Drug Carriers and Bacteria Through a Live Mucosal Barrier.

The intestinal mucosal barrier forms a critical interface between lumen contents such as bacteria, drugs, and drug carriers and the underlying tissue. Current in vitro intestinal models, while recapitulating certain aspects of this barrier, generally present challenges with respect to imaging transport across mucus and uptake into enterocytes. A human mesofluidic small intestinal chip was designed to enable facile visualization of a mucosal interface created by growing primary human intestinal cells on a vertical hydrogel wall separating channels representing the intestinal lumen and circulatory flow. Type I collagen, fortified via cross-linking to prevent deformation and leaking during culture, was identified as a suitable gel wall material for supporting primary organoid-derived human duodenal epithelial cell attachment and monolayer formation. Addition of DAPT and PGE2 to culture medium paired with air-liquid interface culture increased the thickness of the mucus layer on epithelium grown within the device for 5 days from approximately 5 mm to 50 μm, making the model suitable for revealing intriguing features of interactions between luminal contents and the mucus barrier using live cell imaging. Time-lapse imaging of nanoparticle diffusion within mucus revealed a zone adjacent to the epithelium largely devoid of nanoparticles up to 4.5 hr after introduction to the lumen channel, as well as pockets of dimly lectin-stained mucus within which particles freely diffused, and apparent clumping of particles by mucus components. Multiple particle tracking conducted on the intact mucus layer in the chip revealed significant size-dependent differences in measured diffusion coefficients. E. coli introduced to the lumen channel were freely mobile within the mucus layer and appeared to intermittently contact the epithelial surface over 30 minute periods of culture. Mucus shedding into the lumen and turnover of mucus components within cells were visualized. Taken together, this system represents a powerful tool for visualization of interactions between luminal contents and an intact live mucosal barrier.

Abstract C139: Cancer related excess death and potential life years lost among Black Americans

Abstract Introduction: Cancer represents the second leading cause of death in the United States and disproportionately affects Black Americans. In recent years, cancer-related mortality rates have declined for both Black and White Americans. However, mortality rates and rate ratios alone are insufficient for characterizing the burden of cancer disparities between racial groups. Among the most prevalent cancers in the US, we evaluated trends in excess mortality and years of potential life lost between Black and White Americans. Methods: This was a serial cross-sectional study using US national level data from CDC Wonder between the years 1999-2020. We included White and non-Hispanic Black Americans with a cause of death from breast, prostate, lung, colon, bladder, or uterine cancer. We measured age-adjusted excess death as well as excess years of potential life lost for each cancer site among Black and White Americans, stratified by sex when applicable. Annual mortality rates for each cancer site were sourced from the CDC Cancer Statistic Data Visualization tool. Results: The age adjusted mortality rate for all cancers declined between 1999-2020 for Black (257.2 to 166.8 per 100,000) and White Americans (200.9 to 149.3 per 100,000). Site specific cancer mortality rates declined in Black and White Americans for breast, prostate, lung, colon, and bladder cancer, but increased for uterine cancer. Among the most prevalent cancer sites, there were a total of 115,43 excess deaths with 2,093,327 excess years potential life lost for Black men and 59,134 excess deaths with 1,508,052 excess years potential life lost for Black women over a 22-year period. The trend of annual excess death and years potential life lost has slowly increased for Black men with colon cancer as well as Black women with breast and uterine cancer. Annual excess death and years potential life lost in Black men with prostate cancer decreased until 2013 after which it has slowly increased. Conclusion: Despite declining mortality rates in most cancers in the United States, Black Americans still experience a staggering number of excess deaths and years potential life lost due to cancer. Furthermore, the discordance between declining mortality rates and increasing excess death or years potential life lost in breast, prostate, colon, and uterine cancer highlight that racial disparities in cancer care and outcomes are likely worsening among Black Americans. Action is needed to support interventions and organizations committed to addressing cancer health inequities among Black Americans. Citation Format: Daniel S. Carson, Navya Gunaje, Sarah K. Holt, Nana Frimpong, Jenney R. Lee, Liz Sage, Erika M Wolff, Yaw A. Nyame. Cancer related excess death and potential life years lost among Black Americans [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C139.

Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems

This research aims to explore more efficient machine learning (ML) algorithms with better performance for short-term forecasting. Up-to-date literature shows a lack of research on selecting practical ML algorithms for short-term forecasting in real-time industrial applications. This research uses a quantitative and qualitative mixed method combining two rounds of literature reviews, a case study, and a comparative analysis. Ten widely used ML algorithms are selected to conduct a comparative study of gas warning systems in a case study mine. We propose a new assessment visualization tool: a 2D space-based quadrant diagram can be used to visually map prediction error assessment and predictive performance assessment for tested algorithms. Overall, this visualization tool indicates that LR, RF, and SVM are more efficient ML algorithms with overall prediction performance for short-term forecasting. This research indicates ten tested algorithms can be visually mapped onto optimal (LR, RF, and SVM), efficient (ARIMA), suboptimal (BP-SOG, KNN, and Perceptron), and inefficient algorithms (RNN, BP_Resilient, and LSTM). The case study finds results that differ from previous studies regarding the ML efficiency of ARIMA, KNN, LR, LSTM, and SVM. This study finds different views on the prediction performance of a few paired algorithms compared with previous studies, including RF and LR, SVM and RF, KNN and ARIMA, KNN and SVM, RNN and ARIMA, and LSTM and SVM. This study also suggests that ARIMA, KNN, LR, and LSTM should be investigated further with additional prediction error assessments. Overall, no single algorithm can fit all applications. This study raises 20 valuable questions for further research.

Free tools for crystallographic symmetry handling and visualization.

Online courses and innovative teaching methods have triggered a trend in education, where the integration of multimedia, online resources and interactive tools is reshaping the view of both virtual and traditional classrooms. The use of interactive tools extends beyond the boundaries of the physical classroom, offering students the flexibility to access materials at their own speed and convenience and enhancing their learning experience. In the field of crystallography, there are a wide variety of free online resources such as web pages, interactive applets, databases and programs that can be implemented in fundamental crystallography courses for different academic levels and curricula. This paper discusses a variety of resources that can be helpful for crystallographic symmetry handling and visualization, discussing four specific resources in detail: the Bilbao Crystallographic Server, the Cambridge Structural Database, VESTA and Jmol. The utility of these resources is explained and shown by several illustrative examples.

Exploring Customer Insight Analytics with Descriptive Methodology: A Case Study of Adventure Hardware Group

In contemporary business, understanding consumer behavior is essential, giving firms the knowledge needed to make educated decisions and improve customer experiences. Hence, the main objective of this research is to conduct an assessment of retail outlets adaptation to changes in customer preferences to uncover profit opportunities and take advantage of customers demography visibility in the face of heightened volatility. This research delves into the dynamic landscape of Customer Insight Analytics through descriptive procedure to offer a comprehensive exploration of Adventure Hardware Group as a pivotal case study. The study employed the Power BI, a Business Intelligence and Data Visualization tool to transform data from various sources into interactive dashboards and analysis reports, facilitating insightful visualizations and data-driven decision-making. The research shows that customer demography has largely remained the same for the years under consideration, therefore, forming the basis for determining customer preferences, pursuing customer satisfaction and improved sales. In the case of Adventure Hardware Group in particular, the key influencer analysis shows that Germany has the highest influence for driving revenue, and single customers have a higher influence on improving revenue growth for the group. Thus, this information provides the background for future market segmentation for the group’s products. The research concludes that adequate knowledge of customers demography and geography will enhance informed decisions that improve customer experiences, thereby increase sales revenue.

River morphology information system: A web cyberinfrastructure for advancing river morphology research

The study of river systems is challenged by the complexity and volume of data required to understand and predict river morphology changes. The River Morphology Information System (RIMORPHIS) addresses these challenges with an open-access web-based cyberinfrastructure for advanced river morphology research. Built on the National Hydrography Dataset Plus High Resolution, RIMORPHIS integrates publicly available bathymetry data and third-party resources into a cohesive database architecture for real-time use and analysis. The platform, structured around a PostgreSQL database with PostGIS extension, offers tools for geospatial visualization using Deck.GL, data analytics with Python-based geospatial processing, and eHydro Cross Section Surveys integration via API. By providing on-demand access to relevant datasets and tools for data analytics and geospatial visualization, RIMORPHIS enhances data accessibility and interoperability, supporting informed decision-making in river basin management. This paper presents the development and functionalities of RIMORPHIS and its contributions to hydrological information systems as a self-sustained community platform.

A lightweight visualization tool for protein unfolding by collision detection and elimination

The experiments involving protein denaturation and refolding serve as the foundation for predicting the three-dimensional spatial structures of proteins based on their amino acid sequences. Despite significant progress in protein structure engineering, exemplified by AlphaFold2 and OmegaFold, there remains a gap in understanding the folding pathways of polypeptide chains leading to their final structures. We developed a lightweight tool for protein unfolding visualization called PUV whose graphics design is mainly implemented by OpenGL. PUV leverages principles from molecular biology and physics, and achieves rapid visual dynamics simulation of protein polypeptide chain unfolding through mechanical force and atom-level collision detection and elimination. After a series of experimental validations, we believe that this method can provide essential support for investigating protein folding mechanisms and pathways.

Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community

Abstract. The Community Multiscale Air Quality Model (CMAQ) is a local- to hemispheric-scale numerical air quality modeling system developed by the U.S. Environmental Protection Agency (USEPA) and supported by the Community Modeling and Analysis System (CMAS) center. CMAQ is used for regulatory purposes by the USEPA program offices and state and local air agencies and is also widely used by the broader global research community to simulate and understand complex air quality processes and for computational environmental fate and transport and climate and health impact studies. Leveraging state-of-the-science cloud computing resources for high-performance computing (HPC) applications, CMAQ is now available as a fully tested, publicly available technology stack (HPC cluster and software stack) for two major cloud service providers (CSPs). Specifically, CMAQ configurations and supporting materials have been developed for use on their HPC clusters, including extensive online documentation, tutorials and guidelines to scale and optimize air quality simulations using their services. These resources allow modelers to rapidly bring together CMAQ, cloud-hosted datasets, and visualization and evaluation tools on ephemeral clusters that can be deployed quickly and reliably worldwide. Described here are considerations in CMAQ version 5.3.3 cloud use and the supported resources for each CSP, presented through a benchmark application suite that was developed as an example of a typical simulation for testing and verifying components of the modeling system. The outcomes of this effort are to provide findings from performing CMAQ simulations on the cloud using popular vendor-provided resources, to enable the user community to adapt this for their own needs, and to identify specific areas of potential optimization with respect to storage and compute architectures.

Storying connectivity and value: the south west creative technology network’s cultural ecologies as network visualisations

ABSTRACT This paper explores the productive connectivity developed by university-industry knowledge exchange (KE) programmes for research and development (R&D) and their emergent cultural value. The South West Creative Technology Network (SWCTN) is our case study. We build on Dovey et al.'s. work to visualise the cultural ecology supported and created by cohort-led R&D programmes. Our visualisations, combined with interview data, evidence that new connections create diverse forms of value, including inspiration, mentoring, training, and recruitment opportunities, and cooperation through collaborative working. We supplement social network and visualisation tools with rich qualitative data to explore the “why,” “how” and “to what end” of new connections, contributing to the field of Mixed Methods in Social Network Analysis (MMSNA). This methodology is novel to the field of creative economy research. We argue for new forms of R&D funding for the creative sector to take a cultural ecology approach if we wish to take seriously and invest in the diverse forms of value beyond economic drivers, sometimes described as “intangible,” that play an important role in building regional capacity, innovation and productivity.

Emerging insights into thyroid cancer from immunotherapy perspective: A bibliometric analysis

ABSTRACT Thyroid cancer is a common endocrine malignancy that poses considerable therapeutic challenges in treating anaplastic carcinoma and advanced aggressive disease. Immunotherapy has become a prominent strategy for cancer treatment, and has shown remarkable advancements in recent years. In this study, we utilized visualization and bibliometric tools to analyze publications on thyroid cancer immunotherapy from the Web of Science Core Collection (WoSCC). A total of 409 articles were included, with an annual increase in both publications and citations since 2016. China leads research efforts in this area, while the University of Texas System and UTMD Anderson Cancer Center rank first in publication output. The journal Thyroid has garnered the highest citations. Notable authors contributing to this field include Antonelli Alessandro, Fallahi Poupak, and Wang Yu. Current research hotspots include immune checkpoint inhibitors, combination therapies involving immunotherapy with targeted therapy, CAR-T cell therapy, and modulation of the tumor microenvironment, all of which underscore the evolving landscape and potential for innovative treatments in thyroid cancer.