❌

Reading view

Recapitulating lung cancer metastasis in vitro: Advances in organoid models and challenges in clinical translation (Review)

Oncol Rep. 2026 Mar;55(3):49. doi: 10.3892/or.2026.9054. Epub 2026 Jan 23.

ABSTRACT

Lung cancer remains a significant global health challenge, with metastatic progression being the leading driver of mortality. Organoid technology provides a tractable, physiologically relevant platform to model key aspects of lung cancer metastasis in vitro. The present review summarized methodologies for constructing and interrogating these models, covering tissue sources, culture modalities, gene editing and in vivo transplantation; applications in studying metastatic mechanisms, drug screening and capturing intra‑ and intertumoral heterogeneity are also highlighted. Persistent challenges include standardizing derivation and culture conditions, improving preservation of tumor‑microenvironmental interactions, expanding immune‑competent and vascularized models, and addressing scalability, cost, and regulatory and ethical considerations for clinical translation. Future directions include integrating multi‑omics approaches and spatial profiling, leveraging artificial intelligence for image and response analytics, advancing immune‑organoid models and establishing shared standards, reference materials and reporting guidelines to enhance reproducibility and clinical impact.

PMID:41574717 | DOI:10.3892/or.2026.9054

  •  

MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Models

arXiv:2506.07400v3 Announce Type: replace-cross Abstract: The integration of deep learning-based glaucoma detection with large language models (LLMs) presents an automated strategy to mitigate ophthalmologist shortages and improve clinical reporting efficiency. However, applying general LLMs to medical imaging remains challenging due to hallucinations, limited interpretability, and insufficient domain-specific medical knowledge, which can potentially reduce clinical accuracy. Although recent approaches combining imaging models with LLM reasoning have improved reporting, they typically rely on a single generalist agent, restricting their capacity to emulate the diverse and complex reasoning found in multidisciplinary medical teams. To address these limitations, we propose MedChat, a multi-agent diagnostic framework and platform that combines specialized vision models with multiple role-specific LLM agents, all coordinated by a director agent. This design enhances reliability, reduces hallucination risk, and enables interactive diagnostic reporting through an interface tailored for clinical review and educational use. Code available at https://github.com/Purdue-M2/MedChat.
  •  

ScRNA-seq of gastric cancer tissues reveals differences in the immune microenvironment of primary tumors and metastases

Oncogene, Published online: 30 March 2024; doi:10.1038/s41388-024-03012-5

ScRNA-seq of gastric cancer tissues reveals differences in the immune microenvironment of primary tumors and metastases
  •  
❌