LungCURE: Benchmarking Multimodal Real-World Clinical Reasoning for Precision Lung Cancer Diagnosis and Treatment
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911577986301952 |
|---|---|
| author | Hao, Fangyu Yang, Jiayu Zhu, Yifan Yu, Zijun Wu, Qicen Yunlong, Wang Li, Jiawei Liu, Yulin Zeng, Xu Chen, Guanting Li, Shihao Ou, Zhonghong Song, Meina Sun, Mengyang Luo, Haoran Shi, Yu Wang, Yingyi |
| author_facet | Hao, Fangyu Yang, Jiayu Zhu, Yifan Yu, Zijun Wu, Qicen Yunlong, Wang Li, Jiawei Liu, Yulin Zeng, Xu Chen, Guanting Li, Shihao Ou, Zhonghong Song, Meina Sun, Mengyang Luo, Haoran Shi, Yu Wang, Yingyi |
| contents | Lung cancer clinical decision support demands precise reasoning across complex, multi-stage oncological workflows. Existing multimodal large language models (MLLMs) fail to handle guideline-constrained staging and treatment reasoning. We formalize three oncological precision treatment (OPT) tasks for lung cancer, spanning TNM staging, treatment recommendation, and end-to-end clinical decision support. We introduce LungCURE, the first standardized multimodal benchmark built from 1,000 real-world, clinician-labeled cases across more than 10 hospitals. We further propose LCAgent, a multi-agent framework that ensures guideline-compliant lung cancer clinical decision-making by suppressing cascading reasoning errors across the clinical pathway. Experiments reveal large differences across various large language models (LLMs) in their capabilities for complex medical reasoning, when given precise treatment requirements. We further verify that LCAgent, as a simple yet effective plugin, enhances the reasoning performance of LLMs in real-world medical scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06925 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LungCURE: Benchmarking Multimodal Real-World Clinical Reasoning for Precision Lung Cancer Diagnosis and Treatment Hao, Fangyu Yang, Jiayu Zhu, Yifan Yu, Zijun Wu, Qicen Yunlong, Wang Li, Jiawei Liu, Yulin Zeng, Xu Chen, Guanting Li, Shihao Ou, Zhonghong Song, Meina Sun, Mengyang Luo, Haoran Shi, Yu Wang, Yingyi Multimedia Lung cancer clinical decision support demands precise reasoning across complex, multi-stage oncological workflows. Existing multimodal large language models (MLLMs) fail to handle guideline-constrained staging and treatment reasoning. We formalize three oncological precision treatment (OPT) tasks for lung cancer, spanning TNM staging, treatment recommendation, and end-to-end clinical decision support. We introduce LungCURE, the first standardized multimodal benchmark built from 1,000 real-world, clinician-labeled cases across more than 10 hospitals. We further propose LCAgent, a multi-agent framework that ensures guideline-compliant lung cancer clinical decision-making by suppressing cascading reasoning errors across the clinical pathway. Experiments reveal large differences across various large language models (LLMs) in their capabilities for complex medical reasoning, when given precise treatment requirements. We further verify that LCAgent, as a simple yet effective plugin, enhances the reasoning performance of LLMs in real-world medical scenarios. |
| title | LungCURE: Benchmarking Multimodal Real-World Clinical Reasoning for Precision Lung Cancer Diagnosis and Treatment |
| topic | Multimedia |
| url | https://arxiv.org/abs/2604.06925 |