Nature Biomedical Engineering最新文献

Advancements in medical imaging AI, particularly in 3D imaging, have been limited due to the scarcity of comprehensive datasets. We introduce CT-RATE, a public dataset that pairs 3D medical images with corresponding textual reports. CT-RATE comprises 25,692 non-contrast 3D chest CT scans from 21,304 unique patients. Each scan is accompanied by its corresponding radiology report. Leveraging CT-RATE, we develop CT-CLIP, a CT-focused contrastive language-image pretraining framework designed for broad applications without the need for task-specific training. We demonstrate how CT-CLIP can be used in multi-abnormality detection and case retrieval, and outperforms state-of-the-art fully supervised models across all key metrics. By combining CT-CLIP's vision encoder with a pretrained large language model, we create CT-CHAT, a vision-language foundational chat model for 3D chest CT volumes. Fine-tuned on over 2.7 million question-answer pairs derived from the CT-RATE dataset, CT-CHAT underscores the necessity for specialized methods in 3D medical imaging. Collectively, the open-source release of CT-RATE, CT-CLIP and CT-CHAT not only addresses critical challenges in 3D medical imaging but also lays the groundwork for future innovations in medical AI and improved patient care.

Radiotherapy is used in more than half of cancer patients, yet most radiosensitizers increase reactive oxygen species (ROS) to enhance cytotoxicity in treated cells. This approach has limited use in hypoxic tumours and may cause oxidative injury to healthy tissues. We developed a platinum(II) azido complex (Complex 1) that releases platinonitrene upon X-ray exposure. Platinonitrene reacts with nucleophilic sites on DNA bases, forming covalent adducts that disrupt DNA integrity and cause double-strand breaks, leading to tumour cell death through a mechanism distinct from classical platinum coordination. Computational modelling elucidated this pathway and supported its role in radiosensitization. Complex 1 was synthesized by sequential ligand exchange of potassium tetrachloroplatinate with cyclohexanediamine, silver nitrate and sodium azide. In murine models, complex 1 showed negligible toxicity to major organs and normal immune cells while selectively reducing regulatory T-cell infiltration in tumours. Combined with low-dose radiotherapy and programmed cell death protein 1 blockade, it achieved complete regression of bilateral tumours in 40% of mice, demonstrating a strong abscopal effect. This work establishes metallonitrene-based, ROS-independent radiosensitization for precision radiotherapy.

Neuroimaging is a ubiquitous tool for evaluating patients with neurological diseases. The global demand for magnetic resonance imaging (MRI) studies has risen steadily, placing substantial strain on health systems, prolonging turnaround times and intensifying physician burnout. These challenges disproportionately impact patients in low-resource and rural settings. Here we utilize data from a large academic health system to develop Prima, an AI foundation model for neuroimaging that supports real-world, clinical MRI studies as input. Trained on over 220,000 MRI studies, Prima uses a hierarchical vision architecture that provides general and transferable MRI features. Prima was tested in a 1-year health system-wide study that included 29,431 MRI studies. Across 52 radiologic diagnoses from major neurologic disorders, Prima achieved a mean diagnostic area under the curve (AUC) of 92.0%, outperforming other state-of-the-art general and medical AI models. Prima offers explainable differential diagnoses, worklist priority for radiologists and clinical referral recommendations. Prima demonstrates algorithmic fairness across sensitive groups. These findings highlight the transformative potential of health system-scale AI training and Prima's role in advancing AI-driven healthcare.