The lancet oncology
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The lancet oncology · Dec 2024
Observational StudyPreferences for speed of access versus certainty of the survival benefit of new cancer drugs: a discrete choice experiment.
The extent to which patients with cancer are willing to accept uncertainty about the clinical benefit of new cancer drugs in exchange for faster access is not known. This study aims to examine preferences for access versus certainty, and to understand factors that influence these preferences. ⋯ The London School of Economics and Political Science Phelan United States Centre.
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The lancet oncology · Dec 2024
ReviewNavigating the oncology drug discovery and development process with programmes supported by the National Institutes of Health.
The translation of basic drug discoveries from laboratories to clinical use presents substantial challenges. Factors such as insufficient funding, misdirected project focus, and inability to understand a drug's limitations or strengths contribute to the difficulty of this process. To address these issues, the National Institutes of Health (NIH) has established various resources dedicated to streamlining drug development. ⋯ The NIH also provides access to key resources through various programmes, such as the Developmental Therapeutics Program, focusing on preclinical drug discovery and the Cancer Therapy Evaluation Program, which oversees clinical trial efforts for investigational agents. These resources might include funding opportunities, access to a network of scientific experts, and services to address gaps in scientific work. This Review explores the diverse platforms and resources available at the NIH and outlines how researchers can leverage them to expedite the drug development process.
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The lancet oncology · Dec 2024
ReviewArtificial intelligence-aided data mining of medical records for cancer detection and screening.
The application of artificial intelligence methods to electronic patient records paves the way for large-scale analysis of multimodal data. Such population-wide data describing deep phenotypes composed of thousands of features are now being leveraged to create data-driven algorithms, which in turn has led to improved methods for early cancer detection and screening. Remaining challenges include establishment of infrastructures for prospective testing of such methods, ways to assess biases given the data, and gathering of sufficiently large and diverse datasets that reflect disease heterogeneities across populations. This Review provides an overview of artificial intelligence methods designed to detect cancer early, including key aspects of concern (eg, the problem of data drift-when the underlying health-care data change over time), ethical aspects, and discrepancies between access to cancer screening in high-income countries versus low-income and middle-income countries.