ArticleNature communications2024
LLM-driven multimodal target volume contouring in radiation oncology.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 47 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed.
- Local large language model-guided optimization for prostate radiotherapy planning.Medical physics · 2026Article
- Context-informed personalized segmentation from radiology reports: A fully automated framework for long-term liver tumor follow-up.Medical physics · 2026Article
- Radiation oncology at a new frontier: Hotspots, challenges, and emerging advances.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Evaluation of Large Language Models in Generating Physical Exercise Rehabilitation Programs for Musculoskeletal Disorders Across Multiple Clinical Scenarios.Healthcare (Basel, Switzerland) · 2026Article
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Review
- Review
- A human-AI collaborative workflow for improved gross tumor volume delineation in esophageal cancer radiotherapy.BMC medical imaging · 2026Article
- Prospective blinded clinical evaluation of a fully automated workflow for prostate radiotherapy.Physics and imaging in radiation oncology · 2026Article
- The current and future landscape of AI foundation models for cancer management.Nature communications · 2026Article
- Vision-Language Models in Medical Imaging for Cancer Diagnosis: A Bibliometric Review.Bioengineering (Basel, Switzerland) · 2026Review
- Leveraging Large Language Models for Accurate AO Fracture Classification from CT Text Reports.Journal of imaging informatics in medicine · 2026Article
- [Digital transformation in endocrinology/diabetology, pneumology, and hematology/oncology : Where do we stand today-and what lies ahead?]Innere Medizin (Heidelberg, Germany) · 2026Review
- A prospective multicenter trial of deep learning auto-segmentation for organs at risk in thoracic radiotherapy.Nature communications · 2026Observational
- Intelligent Support for Radiotherapy: A Review of Clinical Applications for Large Language Models.Journal of clinical medicine · 2026Review
- Medical large language models and systems in the clinical application of spinal diseases: Current status, challenges, and future prospects.Journal of orthopaedic translation · 2026Review
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Evaluating the capability of large language models in radiotherapy through professional certification examinations in Japan.Journal of radiation research · 2026Article
- The intersection of artificial intelligence and lung nodule research: current applications and future prospects.International journal of surgery (London, England) · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
Target volume contouring for radiation therapy is considered significantly more challenging than the normal organ segmentation tasks as it necessitates the utilization of both image and text-based clinical information. Inspired by the recent advancement of large language models (LLMs) that can facilitate the integration of the textural information and images, here we present an LLM-driven multimodal artificial intelligence (AI), namely LLMSeg, that utilizes the clinical information and is applicable to the challenging task of 3-dimensional context-aware target volume delineation for radiation oncology. We validate our proposed LLMSeg within the context of breast cancer radiotherapy using external validation and data-insufficient environments, which attributes highly conducive to real-world applications. We demonstrate that the proposed multimodal LLMSeg exhibits markedly improved performance compared to conventional unimodal AI models, particularly exhibiting robust generalization performance and data-efficiency.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.