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Multi-Agent Intelligence for Multidisciplinary Decision-Making in Gastrointestinal Oncology

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Rongzhao Zhang Β· Junqiao Wang Β· Shuyun Yang Β· Mouxiao Bian Β· Chao Ding Β· Yuwei Bai Β· Chihao Zhang Β· Yuguang Shen Β· Lei Wang Β· Lei Zheng Β· Qiujuan Yan Β· Yun Zhong Β· Meiling Liu Β· Jiwei Yu Β· Zheng Wang Β· Jie Xu Β· Meng Luo
arXiv:2512.08674v1 Announce Type: new Abstract: Multimodal clinical reasoning in the field of gastrointestinal (GI) oncology necessitates the integrated interpretation of endoscopic imagery, radiological data, and biochemical markers. Despite the evident potential exhibited by Multimodal Large Language Models (MLLMs), they frequently encounter challenges such as context dilution and hallucination when confronted with intricate, heterogeneous medical histories. In order to address these limitations, a hierarchical Multi-Agent Framework is proposed, which emulates the collaborative workflow of a human Multidisciplinary Team (MDT). The system attained a composite expert evaluation score of 4.60/5.00, thereby demonstrating a substantial improvement over the monolithic baseline. It is noteworthy that the agent-based architecture yielded the most substantial enhancements in reasoning logic and medical accuracy. The findings indicate that mimetic, agent-based collaboration provides a scalable, interpretable, and clinically robust paradigm for automated decision support in oncology.
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LcProt: Proteomics-based identification of plasma biomarkers for lung cancer multievent, a multicentre study

βœ‡(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Hengrui Liang Β· Runchen Wang Β· Ran Cheng Β· Zhiming Ye Β· Na Zhao Β· Xiaohong Zhao Β· Ying Huang Β· Zhanpeng Jiang Β· Wangzhong Li Β· Jianqi Zheng Β· Hongsheng Deng Β· Yu Jiang Β· Yuechun Lin Β· Yun Yan Β· Lei Song Β· Jie Li Β· Xin Xu Β· Wenhua Liang Β· Jun Liu Β· Jianxing He

Clin Transl Med. 2025 Jan;15(1):e70160. doi: 10.1002/ctm2.70160.

ABSTRACT

BACKGROUND: Plasma protein has gained prominence in the non-invasive predicting of lung cancer. We utilised Zeolite Zotero NaY-based plasma proteomics to investigate its potential for multiple event predicting, including lung cancer diagnosis (task #1), lymph node metastasis detection (task #2) and tumourβ€’nodeβ€’metastasis (TNM) staging (task #3).

METHODS: A total of 4703 plasma proteins were quantified from 241 participants based on a prospective cohort of 2757 participants. An additional 46 participants from external prospective cohort of 735 participants were used for validation. Feature selection was performed using differential expressed protein analysis, area under curve (AUC) evaluation and least absolute shrinkage and selection operator (LASSO) regression. Random forest was used for multitask model construction based on the key proteins. Feature importance was interpreted using Shapley additive explanations (SHAP) algorithm.

RESULTS: For task #1, 10 proteins panel showed an AUC of .87 (.77β€’.97) in the external validation. After integrating clinical factors, a significant increase diagnostic accuracy was observed with AUC of .91 (.85β€’.98). For task #2, nine proteins panel achieved an AUC of .88 (.80β€’.96), integration model showed an increase diagnostic accuracy with AUC of .90 (.85β€’.97). For task #3, 10 proteins panel showed an AUC of .88 (.74β€’.96) for stage I, .92 (.84β€’.97) for stage II, .88 (.76β€’.96) for stage III and .99 (.98β€’.99) for stage IV in the integration model.

CONCLUSIONS: This study comprehensively profiled the NaY-based plasma proteome biomarker, laying the foundation for a high-performance blood test for predicting multiple events in lung cancer.

KEY POINTS: Our study developed an innovative nanomaterial, Zeolite NaY, which addressed the masking effect and improved the depth of the proteome. The performance of NaY-based plasma proteomics as a preclinical diagnostic tool was validated through both internal and external cohort. Furthermore, we explored the different patterns of plasma protein changes during the progression of lung cancer and used the explanations method to elucidate the roles of proteins in the multitask predictive model.

PMID:39783847 | PMC:PMC11714244 | DOI:10.1002/ctm2.70160

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