Cancer data, particularly cancer incidence and mortality, are fundamental to understand the cancer burden, to set targets for cancer control and to evaluate the evolution of the implementation of a cancer control policy. However, the complexity of data collection, classification, validation and processing result in cancer incidence figures often lagging two to three years behind the calendar year. In response, national or regional population-based cancer registries (PBCRs) are increasingly interested in methods for forecasting cancer incidence. However, in many countries there is an additional difficulty in projecting cancer incidence as regional registries are usually not established in the same year and therefore cancer incidence data series between different regions of a country are not harmonised over time. This study addresses the challenge of forecasting cancer incidence with incomplete data at both regional and national levels. To achieve this, we propose the use of multivariate spatio-temporal shared component models that jointly model mortality data and available cancer incidence data. We evaluate the performance of these multivariate models using lung cancer incidence dat
Metastatic prostate cancer is one of the most common cancers in men. In the advanced stages of prostate cancer, tumours can metastasise to other tissues in the body, which is fatal. In this thesis, we performed a genetic analysis of prostate cancer tumours at different metastatic sites using data science, machine learning and topological network analysis methods. We presented a general procedure for pre-processing gene expression datasets and pre-filtering significant genes by analytical methods. We then used machine learning models for further key gene filtering and secondary site tumour classification. Finally, we performed gene co-expression network analysis and community detection on samples from different prostate cancer secondary site types. In this work, 13 of the 14,379 genes were selected as the most metastatic prostate cancer related genes, achieving approximately 92% accuracy under cross-validation. In addition, we provide preliminary insights into the co-expression patterns of genes in gene co-expression networks. Project code is available at https://github.com/zcablii/Master_cancer_project.
Embryology has long played a foundational role in shaping our scientific understanding of animal evolution. In recent decades, growing evidence has also highlighted its role in cancer. Despite the indisputable similarities between embryonic development and cancer, there has been limited discussion on the profound embryological implications for the disease. This article explores the understanding of cancer as an embryological and evolutionary phenomenon, offering a fresh perspective on the disease and discussing immediate consequences in the search for therapeutic approaches
GREX-PLUS (Galaxy Reionization EXplorer and PLanetary Universe Spectrometer) is a mission candidate for a JAXA strategic L-class mission to be launched in the 2030s. Its primary science goals are two-fold: galaxy formation and evolution, and planetary system formation and evolution. The GREX-PLUS spacecraft will carry a telescope with a 1 m primary mirror aperture cooled down to 50 K. The two science instruments will be onboard: a wide-field camera in the 2--8 $μ$m wavelength band and a high-resolution spectrometer with a wavelength resolution of 30,000 in the 10--18 $μ$m band. The GREX-PLUS wide-field camera aims to detect the first generation of galaxies at redshift $z>15$. The GREX-PLUS high-resolution spectrometer aims to identify the location of the water ``snowline'' in protoplanetary disks. Both instruments will provide unique datasets for a broad range of scientific topics, including galaxy mass assembly, the origin of supermassive blackholes, infrared background radiation, molecular spectroscopy in the interstellar medium, transit spectroscopy of exoplanet atmospheres, planetary atmospheres in the Solar System, and so on. This document is the second version of a collect
In recent years, cancer genome sequencing and other high-throughput studies of cancer genomes have generated many notable discoveries. In this review, Novel genomic alteration mechanisms, such as chromothripsis (chromosomal crisis) and kataegis (mutation storms), and their implications for cancer are discussed. Genomic alterations spur cancer genome evolution. Thus, the relationship between cancer clonal evolution and cancer stems cells is commented. The key question in cancer biology concerns how these genomic alterations support cancer development and metastasis in the context of biological functioning. Thus far, efforts such as pathway analysis have improved the understanding of the functional contributions of genetic mutations and DNA copy number variations to cancer development, progression and metastasis. However, the known pathways correspond to a small fraction, plausibly 5-10%, of somatic mutations and genes with an altered copy number. To develop a comprehensive understanding of the function of these genomic alterations in cancer, an integrative network framework is proposed and discussed. Finally, the challenges and the directions of studying cancer omic data using an in
Breast cancer screening plays a pivotal role in early detection and subsequent effective management of the disease, impacting patient outcomes and survival rates. This study aims to assess breast cancer screening rates nationwide in the United States and investigate the impact of social determinants of health on these screening rates. Data on mammography screening at the census tract level for 2018 and 2020 were collected from the Behavioral Risk Factor Surveillance System. We developed a large dataset of social determinants of health, comprising 13 variables for 72337 census tracts. Spatial analysis employing Getis-Ord Gi statistics was used to identify clusters of high and low breast cancer screening rates. To evaluate the influence of these social determinants, we implemented a random forest model, with the aim of comparing its performance to linear regression and support vector machine models. The models were evaluated using R2 and root mean squared error metrics. Shapley Additive Explanations values were subsequently used to assess the significance of variables and direction of their influence. Geospatial analysis revealed elevated screening rates in the eastern and northern U
Effective communication about breast and cervical cancers remains a persistent health challenge, with significant gaps in public understanding of cancer prevention, screening, and treatment, potentially leading to delayed diagnoses and inadequate treatments. This study evaluates the capabilities and limitations of Large Language Models (LLMs) in generating accurate, safe, and accessible cancer-related information to support patient understanding. We evaluated five general-purpose and three medical LLMs using a mixed-methods evaluation framework across linguistic quality, safety and trustworthiness, and communication accessibility and affectiveness. Our approach utilized quantitative metrics, qualitative expert ratings, and statistical analysis using Welch's ANOVA, Games-Howell, and Hedges' g. Our results show that general-purpose LLMs produced outputs of higher linguistic quality and affectiveness, while medical LLMs demonstrate greater communication accessibility. However, medical LLMs tend to exhibit higher levels of potential harm, toxicity, and bias, reducing their performance in safety and trustworthiness. Our findings indicate a duality between domain-specific knowledge and s
The large instantaneous sensitivity, a wide frequency coverage and flexible observation modes with large number of beams in the sky are the main features of the SKA observatory's two telescopes, the SKA-Low and the SKA-Mid, which are located on two different continents. Owing to these capabilities, the SKAO telescopes are going to be a game-changer for radio astronomy in general and pulsar astronomy in particular. The eleven articles in this special issue on pulsar science with the SKA Observatory describe its impact on different areas of pulsar science. In this lead article, a brief description of the two telescopes highlighting the relevant features for pulsar science is presented followed by an overview of each accompanying article, exploring the inter-relationship between different pulsar science use cases.
Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29\% of all diagnoses and 35,000 total deaths in 2024. Traditional screening methods such as prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) have been pivotal in diagnosis, but have faced limitations in specificity and generalizability. In this paper, we explore the potential of enhancing PCa gland segmentation using a novel MRI modality called synthetic correlated diffusion imaging (CDI$^s$). We employ several state-of-the-art deep learning models, including U-Net, SegResNet, Swin UNETR, Attention U-Net, and LightM-UNet, to segment prostate glands from a 200 CDI$^s$ patient cohort. We find that SegResNet achieved superior segmentation performance with a Dice-Sorensen coefficient (DSC) of $76.68 \pm 0.8$. Notably, the Attention U-Net, while slightly less accurate (DSC $74.82 \pm 2.0$), offered a favorable balance between accuracy and computational efficiency. Our findings demonstrate the potential of deep learning models in improving prostate gland segmentation using CDI$^s$ to enhance PCa management and clinical support.
Cancer is increasingly perceived as a systems-level, network phenomenon. The major trend of malignant transformation can be described as a two-phase process, where an initial increase of network plasticity is followed by a decrease of plasticity at late stages of tumor development. The fluctuating intensity of stress factors, like hypoxia, inflammation and the either cooperative or hostile interactions of tumor inter-cellular networks, all increase the adaptation potential of cancer cells. This may lead to the bypass of cellular senescence, and to the development of cancer stem cells. We propose that the central tenet of cancer stem cell definition lies exactly in the indefinability of cancer stem cells. Actual properties of cancer stem cells depend on the individual "stress-history" of the given tumor. Cancer stem cells are characterized by an extremely large evolvability (i.e. a capacity to generate heritable phenotypic variation), which corresponds well with the defining hallmarks of cancer stem cells: the possession of the capacity to self-renew and to repeatedly re-build the heterogeneous lineages of cancer cells that comprise a tumor in new environments. Cancer stem cells rep
We present a general computational theory of cancer and its developmental dynamics. The theory is based on a theory of the architecture and function of developmental control networks which guide the formation of multicellular organisms. Cancer networks are special cases of developmental control networks. Cancer results from transformations of normal developmental networks. Our theory generates a natural classification of all possible cancers based on their network architecture. Each cancer network has a unique topology and semantics and developmental dynamics that result in distinct clinical tumor phenotypes. We apply this new theory with a series of proof of concept cases for all the basic cancer types. These cases have been computationally modeled, their behavior simulated and mathematically described using a multicellular systems biology approach. There are fascinating correspondences between the dynamic developmental phenotype of computationally modeled {\em in silico} cancers and natural {\em in vivo} cancers. The theory lays the foundation for a new research paradigm for understanding and investigating cancer. The theory of cancer networks implies that new diagnostic methods
Nanorobots are a promising development in targeted drug delivery and the treatment of neurological disorders, with potential for crossing the blood-brain barrier (BBB). These small devices leverage advancements in nanotechnology and bioengineering for precise navigation and targeted payload delivery, particularly for conditions like brain tumors, Alzheimer's disease, and Parkinson's disease. Recent progress in artificial intelligence (AI) and machine learning (ML) has improved the navigation and effectiveness of nanorobots, allowing them to detect and interact with cancer cells through biomarker analysis. This study presents a new reinforcement learning (RL) framework for optimizing nanorobot navigation in complex biological environments, focusing on cancer cell detection by analyzing the concentration gradients of surrounding biomarkers. We utilize a computer simulation model to explore the behavior of nanorobots in a three-dimensional space with cancer cells and biological barriers. The proposed method uses Q-learning to refine movement strategies based on real-time biomarker concentration data, enabling nanorobots to autonomously navigate to cancerous tissues for targeted drug d
Mauve is a low-cost small satellite developed and operated by Blue Skies Space Ltd. The payload features a 13 cm telescope connected with a fibre that feeds into a UV-Vis spectrometer. The detector covers the 200-700 nm range in a single shot, obtaining low resolution spectra at R~20-65. Mauve has launched on 28th November 2025, reaching a 510 km Low-Earth Sun-synchronous orbit. The satellite will enable UV and visible observations of a variety of stellar objects in our Galaxy, filling the gaps in the ultraviolet space-based data. The researchers that have already joined the mission have defined the science themes, observational strategy and targets that Mauve will observe in the first year of operations. To date 10 science themes have been developed by the Mauve science collaboration for year 1, with observational strategies that include both long duration monitoring and short cadence snapshots. Here, we describe these themes and the science that Mauve will undertake in its first year of operations.
With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-making by such algorithms is not trustworthy for clinicians and is considered a black-box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer-reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model-agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP mo
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future.
Lung cancer is the primary cause of cancer-related mortality, claiming approximately 1.79 million lives globally in 2020, with an estimated 2.21 million new cases diagnosed within the same period. Among these, Non-Small Cell Lung Cancer (NSCLC) is the predominant subtype, characterized by a notably bleak prognosis and low overall survival rate of approximately 25% over five years across all disease stages. However, survival outcomes vary considerably based on the stage at diagnosis and the therapeutic interventions administered. Recent advancements in artificial intelligence (AI) have revolutionized the landscape of lung cancer prognosis. AI-driven methodologies, including machine learning and deep learning algorithms, have shown promise in enhancing survival prediction accuracy by efficiently analyzing complex multi-omics data and integrating diverse clinical variables. By leveraging AI techniques, clinicians can harness comprehensive prognostic insights to tailor personalized treatment strategies, ultimately improving patient outcomes in NSCLC. Overviewing AI-driven data processing can significantly help bolster the understanding and provide better directions for using such syste
Tumor protein P53 is believed to be involved in over half of human cancers cases, the prediction of malignancies plays essential roles not only in advance detection for cancer, but also in discovering effective prevention and treatment of cancer, till now there isn't approach be able in prediction the mutated in tumor protein P53 which is caused high ratio of human cancers like breast, Blood, skin, liver, lung, bladder etc. This research proposed a new approach for prediction pre-cancer via detection malignant mutations in tumor protein P53 using bioinformatics tools like FASTA, BLAST, CLUSTALW and TP53 databases worldwide. Implement and apply this new approach of prediction pre-cancer through mutations at tumor protein P53 shows an effective result when used more specific parameters/features to extract the prediction result that means when the user increase the number of filters of the results which obtained from the database gives more specific diagnosis and classify, addition that the detecting pre-cancer via prediction mutated tumor protein P53 will reduces a person's cancers in the future by avoiding exposure to toxins, radiation or monitoring themselves at older ages by chang
Although cancer is known to be characterized by several unifying biological hallmarks, systems biology has had limited success in identifying molecular signatures present in in all types of cancer. The current availability of rich data sets from many different cancer types provides an opportunity for thorough computational data mining in search of such common patterns. Here we report the identification of 18 "pan-cancer" molecular signatures resulting from analysis of data sets containing values from mRNA expression, microRNA expression, DNA methylation, and protein activity, from twelve different cancer types. The membership of many of these signatures points to particular biological mechanisms related to cancer progression, suggesting that they represent important attributes of cancer in need of being elucidated for potential applications in diagnostic, prognostic and therapeutic products applicable to multiple cancer types.
Background Precise prediction of cancer types is vital for cancer diagnosis and therapy. Important cancer marker genes can be inferred through predictive model. Several studies have attempted to build machine learning models for this task however none has taken into consideration the effects of tissue of origin that can potentially bias the identification of cancer markers. Results In this paper, we introduced several Convolutional Neural Network (CNN) models that take unstructured gene expression inputs to classify tumor and non-tumor samples into their designated cancer types or as normal. Based on different designs of gene embeddings and convolution schemes, we implemented three CNN models: 1D-CNN, 2D-Vanilla-CNN, and 2D-Hybrid-CNN. The models were trained and tested on combined 10,340 samples of 33 cancer types and 731 matched normal tissues of The Cancer Genome Atlas (TCGA). Our models achieved excellent prediction accuracies (93.9-95.0%) among 34 classes (33 cancers and normal). Furthermore, we interpreted one of the models, known as 1D-CNN model, with a guided saliency technique and identified a total of 2,090 cancer markers (108 per class). The concordance of differential e
Breast cancer is a leading cause of cancer-related deaths, but current programs are expensive and prone to false positives, leading to unnecessary follow-up and patient anxiety. This paper proposes a solution to automated breast cancer detection, to improve the efficiency and accuracy of screening programs. Different methodologies were tested against the RSNA dataset of radiographic breast images of roughly 20,000 female patients and yielded an average validation case pF1 score of 0.56 across methods.