UNLABELLED: Artificial intelligence applications in biomedicine face major challenges from data privacy requirements. To address this issue for clinically annotated tissue proteomic data, we developed a federated deep learning approach (ProCanFDL), t...
Studies in health technology and informatics
Sep 3, 2025
INTRODUCTION: Whole Exome Sequencing (WES) promises to open a new range of personalized treatments due to breaking the limits of former panel-based methods of molecular analysis. While the methodology is well established and already included in clini...
Journal of applied clinical medical physics
Sep 1, 2025
BACKGROUND: Efficient workflows are essential for timely, high-quality radiotherapy. In 2020, an internal audit identified key workflow bottlenecks, including long patient wait times, suboptimal treatment planning, and inadequate quality control. Acc...
METHODS: In this review, the historical development of tumor response criteria is examined and an interview was conducted with one of the original researchers behind the original study. This study, published nearly five decades ago, assessed tumor si...
BACKGROUND: Patient-specific quality assurance (PSQA) is essential to guarantee the requested accuracy and safety of high-precision radiotherapy treatments. With the widespread adoption of modulated-intensity techniques, there is a growing need for i...
The integration of artificial intelligence (AI) into [F]FDG PET/CT imaging continues to expand, offering new opportunities for more precise, consistent, and personalized oncologic evaluations. Building on the foundation established in Part I, this se...
The identification of key features and reliable phenotype classification remains pivotal in cancer research, with direct implications for early diagnosis, prognosis, treatment optimization, and cost reduction in healthcare. This study introduces a hy...
Anticancer peptide (ACP) has emerged as potent therapeutic agents owing to its ability to selectively target cancer cells while minimising toxicity to healthy cells. However, the accurate computational prediction of ACP remains challenging because of...
Predicting drug responses using genetic and transcriptomic features is crucial for enhancing personalized medicine. In this study, we implemented an ensemble of machine learning algorithms to analyze the correlation between genetic and transcriptomic...
Deep learning-based semantic segmentation approaches provide an efficient and automated means for cancer diagnosis and monitoring, which is important in clinical applications. However, implementing these approaches outside the experimental environmen...
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