Large Language Models Empower Precision Oncology: A New Paradigm for Radiomics Analysis

The CS85 project explores the application of large language models (LLMs) in radiomics analysis, providing an AI-driven new solution for medical image interpretation in precision oncology. The original author/maintainer is ZLY1oading, the source platform is GitHub, the original title is CS85-Harnessing-Large-Language-Models-for-Radiomics-Analysis-in-Precision-Oncology, the link is https://github.com/ZLY1oading/CS85-Harnessing-Large-Language-Models-for-Radiomics-Analysis-in-Precision-Oncology, and the release date is 2026-06-14. This project demonstrates the innovative application potential of LLMs in the healthcare field.

The core goal of precision oncology is to develop personalized treatment plans based on individual patient characteristics. Medical images (CT, MRI, PET, etc.) provide key biological information. Radiomics extracts quantitative features through high-throughput computing to characterize tumor phenotypes, but traditional analysis faces many challenges: feature engineering relies on expert experience, insufficient feature interpretability, difficulty integrating with clinical data, complex multi-modal data fusion, etc.

The potential of LLMs in the field of medical imaging includes:

1. Multi-modal understanding ability: Models like GPT-4V and Gemini Pro Vision can process text and images simultaneously, understand image features and explain clinical significance;
2. Knowledge integration ability: Pre-trained on massive medical knowledge, they can link image features with clinical knowledge, generate standardized reports, and provide evidence-based suggestions;
3. Reasoning and interpretability: They can show the reasoning process, explain judgments in natural language, and improve the interpretability of medical AI and doctors' trust.

The CS85 project may involve the following technical directions:

1. Natural language description of image features: Convert quantitative features into doctor-friendly text (e.g., tumor inhomogeneity suggests necrosis);
2. Clinical decision support: Integrate multi-source information to predict molecular subtypes, evaluate treatment response, and prompt attention-worthy indicators;
3. Automated report generation: Automatically identify key findings, organize reports according to templates, and prompt changes compared to previous records;
4. Multi-modal data fusion: Link image phenotypes with genotypes, integrate longitudinal data, and support cross-modal retrieval and Q&A.

Applying LLMs to radiomics requires solving the following issues:

1. Specificity of medical images: High professionalism, high precision requirements, and handling standardized formats such as DICOM;
2. Data privacy and security: Patient information desensitization, encrypted storage and transmission, access control, and audit logs;
3. Model hallucination: Solve through retrieval-augmented generation (RAG), multi-model cross-validation, confidence assessment, and human-machine review;
4. Regulatory compliance: Need clinical validation, standardized performance evaluation, quality management systems, and post-market monitoring.

The prospects of LLMs empowering radiomics include:

1. Improve diagnostic efficiency: Reduce doctors' burden, prioritize rapid screening, and reduce missed and misdiagnoses;
2. Promote standardization of diagnosis and treatment: Unify report templates and terminology, based on guideline recommendations, and reduce subjective differences;
3. Support scientific research: Large-scale automatic image analysis, discovery of new biomarkers, and integration of multi-center studies;
4. Empower primary care: Provide expert-level interpretation, support teleconsultation, and balance the distribution of medical resources.

AI applications in healthcare need to focus on:

• Responsibility attribution: Definition of liability when AI-assisted diagnosis is incorrect;
• Algorithm bias: Poor performance on specific populations due to biased training data;
• Doctor-patient relationship: Impact of AI intervention on doctor-patient trust;
• Data rights: Patients' informed consent and right to choose regarding the use of their medical data. These issues need to be addressed through regulations and ethical guidelines.

The CS85 project represents the cutting-edge exploration of AI in the healthcare field. Combining LLMs with radiomics is expected to break through the bottlenecks of precision oncology. Although facing technical, regulatory, and ethical challenges, the prospect is worth looking forward to. With the progress of multi-modal models and the improvement of medical data infrastructure, AI-empowered precision oncology will have a broader space.