A hatter and a railway clerk's 1925 medical breakthrough became one of the most profound events in medical history. Breakthroughs, discoveries, and DIY tips sent every weekday. In 1925,, one of the world's most prestigious medical journals, published a blockbuster finding so significant that its editors offered a rare prelude: "The two communications which follow mark an event in the history of medicine . They form a detailed description of a prolonged and intensive research into the origin of malignant new growths, and they may present a solution of the central problem of cancer." On the day the studies were scheduled to be released, word began to spread beyond the scientific community.

Charges against an American influencer and teen pilot who has been stranded on a remote island in the Antarctic since June have been dropped. Ethan Guo, 19, is alleged to have illegally landed his plane in Chilean territory after embarking on a solo trip to all seven continents to raise money for cancer research, according to local authorities. They accused him of providing false flight plan information to officials who detained him and opened an investigation. A judge has ordered him to leave the area, pay a $30,000 (£22,332) donation to a children's cancer foundation and is banned from re-entering Chilean territory for three years. Mr Guo made headlines last year when he began an attempt to become the youngest person to fly solo to all seven continents and collect donations for research into childhood cancer.

The task of data integration for multi-omics data has emerged as a powerful strategy to unravel the complex biological underpinnings of cancer. Recent advancements in graph neural networks (GNNs) offer an effective framework to model heterogeneous and structured omics data, enabling precise representation of molecular interactions and regulatory networks. This systematic review explores several recent studies that leverage GNN-based architectures in multi-omics cancer research. We classify the approaches based on their targeted omics layers, graph neural network structures, and biological tasks such as subtype classification, prognosis prediction, and biomarker discovery. The analysis reveals a growing trend toward hybrid and interpretable models, alongside increasing adoption of attention mechanisms and contrastive learning. Furthermore, we highlight the use of patient-specific graphs and knowledge-driven priors as emerging directions. This survey serves as a comprehensive resource for researchers aiming to design effective GNN-based pipelines for integrative cancer analysis, offering insights into current practices, limitations, and potential future directions.

Feedback is New Scientist's popular sideways look at the latest science and technology news. You can submit items you believe may amuse readers to Feedback by emailing feedback@newscientist.com Saturday 17 May will see the final of this year's Eurovision Song Contest, which will be the most over-the-top evening of television since, well, the previous Eurovision. Feedback is deeply relieved that Feedback Jr appears not to be interested this year, so we might escape having to sit up and watch the entire thing. While we are deeply supportive of the contest's kind and welcoming vibe, most of the songs make our ears bleed.

Background: Structured information extraction from unstructured histopathology reports facilitates data accessibility for clinical research. Manual extraction by experts is time-consuming and expensive, limiting scalability. Large language models (LLMs) offer efficient automated extraction through zero-shot prompting, requiring only natural language instructions without labeled data or training. We evaluate LLMs' accuracy in extracting structured information from breast cancer histopathology reports, compared to manual extraction by a trained human annotator. Methods: We developed the Medical Report Information Extractor, a web application leveraging LLMs for automated extraction. We developed a gold standard extraction dataset to evaluate the human annotator alongside five LLMs including GPT-4o, a leading proprietary model, and the Llama 3 model family, which allows self-hosting for data privacy. Our assessment involved 111 histopathology reports from the Breast Cancer Now (BCN) Generations Study, extracting 51 pathology features specified in the study's data dictionary. Results: Evaluation against the gold standard dataset showed that both Llama 3.1 405B (94.7% accuracy) and GPT-4o (96.1%) achieved extraction accuracy comparable to the human annotator (95.4%; p = 0.146 and p = 0.106, respectively). While Llama 3.1 70B (91.6%) performed below human accuracy (p <0.001), its reduced computational requirements make it a viable option for self-hosting. Conclusion: We developed an open-source tool for structured information extraction that can be customized by non-programmers using natural language. Its modular design enables reuse for various extraction tasks, producing standardized, structured data from unstructured text reports to facilitate analytics through improved accessibility and interoperability.

In 2021, cancer was the second leading cause of death in the European Union. Notably, while Europe constitutes only a tenth of the global population, it accounts for almost a quarter of the world's cancer cases, bearing an economic impact of approximately 100 billion annually. Belgian statistics further highlight that every step forward in cancer treatment and care could significantly alleviate the immense personal and societal burden. Globally, extensive efforts are made through a variety of innovative approaches with the ultimate objectives of better prevention, earlier detection, and improved patient outcomes and care. Personalized medicine is considered the holy grail of cancer care in most of these initiatives. Tailoring interventions to the unique characteristics of individual patients promises to revolutionize cancer care. Artificial intelligence (AI) plays a pivotal role in this transformative journey towards precision medicine, aiding researchers and healthcare professionals in accurately predicting cancer risks, enabling earlier diagnoses, and customizing treatment plans to meet individual needs.

Objective: This review aims to analyze the application of natural language processing (NLP) techniques in cancer research using electronic health records (EHRs) and clinical notes. This review addresses gaps in the existing literature by providing a broader perspective than previous studies focused on specific cancer types or applications. Methods: A comprehensive literature search was conducted using the Scopus database, identifying 94 relevant studies published between 2019 and 2024. Data extraction included study characteristics, cancer types, NLP methodologies, dataset information, performance metrics, challenges, and future directions. Studies were categorized based on cancer types and NLP applications. Results: The results showed a growing trend in NLP applications for cancer research, with breast, lung, and colorectal cancers being the most studied. Information extraction and text classification emerged as predominant NLP tasks. A shift from rule-based to advanced machine learning techniques, particularly transformer-based models, was observed. The Dataset sizes used in existing studies varied widely. Key challenges included the limited generalizability of proposed solutions and the need for improved integration into clinical workflows. Conclusion: NLP techniques show significant potential in analyzing EHRs and clinical notes for cancer research. However, future work should focus on improving model generalizability, enhancing robustness in handling complex clinical language, and expanding applications to understudied cancer types. Integration of NLP tools into clinical practice and addressing ethical considerations remain crucial for utilizing the full potential of NLP in enhancing cancer diagnosis, treatment, and patient outcomes.

Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data. This systematic review synthesises current knowledge on the state-of-the-art FL in oncology, focusing on breast, lung, and prostate cancer. Distinct from previous surveys, our comprehensive review critically evaluates the real-world implementation and impact of FL on cancer care, demonstrating its effectiveness in enhancing ML generalisability, performance and data privacy in clinical settings and data. We evaluated state-of-the-art advances in FL, demonstrating its growing adoption amid tightening data privacy regulations. FL outperformed centralised ML in 15 out of the 25 studies reviewed, spanning diverse ML models and clinical applications, and facilitating integration of multi-modal information for precision medicine. Despite the current challenges identified in reproducibility, standardisation and methodology across studies, the demonstrable benefits of FL in harnessing real-world data and addressing clinical needs highlight its significant potential for advancing cancer research. We propose that future research should focus on addressing these limitations and investigating further advanced FL methods, to fully harness data diversity and realise the transformative power of cutting-edge FL in cancer care.

The future of cancer research is in good hands. Six high-school students in the U.S. are dedicated to making progress toward improving the diagnostics and treatment of the disease. The students were finalists in this year's Regeneron Science Talent Search, which is the country's oldest and most prestigious science and mathematics competition hosted by the Society for Science in Washington, D.C. "We are thrilled to honor these bright minds dedicated to making strides in cancer research," said Maya Ajmera, president and CEO of the Society for Science, a partner with Regeneron in the Science Talent Search. "These high-school students are not only advancing our understanding of the way cancer presents in the human body, but are paving the way for potential future therapies and helping unlock new possibilities in the fight against this formidable disease." Four of the six student finalists who specialized in cancer research are shown here.

Artificial intelligence could be twice as effective at diagnosing rare cancers as biopsies, a study found. British scientists developed a computer algorithm which correctly diagnosed the severity of sarcoma tumours in 82 per cent of cases, compared with 44 per cent of biopsies. Experts said the technique could eventually become standard practice for all cancers - saving thousands of patients from undergoing the invasive procedure every year. Such programmes will also help doctors diagnose subtypes of the disease faster and tailor treatment more effectively, they believe. Researchers used CT scans of 170 patients from the Royal Marsden, London, with sarcoma tumours, an aggressive type of cancer that develops in the body's connective tissues, such as fat, muscle and nerves.