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Received — 11 December 2025 ⏭ Favourites

Toward an AI Reasoning-Enabled System for Patient-Clinical Trial Matching

✇cs.AI, q-bio.NC updates on arXiv.org
By: Caroline N. Leach · Mitchell A. Klusty · Samuel E. Armstrong · Justine C. Pickarski · Kristen L. Hankins · Emily B. Collier · Maya Shah · Aaron D. Mullen · V. K. Cody Bumgardner
10 December 2025 at 13:00
arXiv:2512.08026v1 Announce Type: new Abstract: Screening patients for clinical trial eligibility remains a manual, time-consuming, and resource-intensive process. We present a secure, scalable proof-of-concept system for Artificial Intelligence (AI)-augmented patient-trial matching that addresses key implementation challenges: integrating heterogeneous electronic health record (EHR) data, facilitating expert review, and maintaining rigorous security standards. Leveraging open-source, reasoning-enabled large language models (LLMs), the system moves beyond binary classification to generate structured eligibility assessments with interpretable reasoning chains that support human-in-the-loop review. This decision support tool represents eligibility as a dynamic state rather than a fixed determination, identifying matches when available and offering actionable recommendations that could render a patient eligible in the future. The system aims to reduce coordinator burden, intelligently broaden the set of trials considered for each patient and guarantee comprehensive auditability of all AI-generated outputs.

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

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rongzhao Zhang · Junqiao Wang · Shuyun Yang · Mouxiao Bian · Chao Ding · Yuwei Bai · Chihao Zhang · Yuguang Shen · Lei Wang · Lei Zheng · Qiujuan Yan · Yun Zhong · Meiling Liu · Jiwei Yu · Zheng Wang · Jie Xu · Meng Luo
10 December 2025 at 13:00
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.
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Multi state neurons Robert Worden
    arXiv:2512.08815v1 Announce Type: new Abstract: Neurons, as eukaryotic cells, have powerful internal computation capabilities. One neuron can have many distinct states, and brains can use this capability. Processes of neuron growth and maintenance use chemical signalling between cell bodies and synapses, ferrying chemical messengers over microtubules and actin fibres within cells. These processes are computations which, while slower than neural electrical signalling, could allow any neuron to c
     
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Multi state neurons

✇cs.AI, q-bio.NC updates on arXiv.org
By: Robert Worden
10 December 2025 at 13:00
arXiv:2512.08815v1 Announce Type: new Abstract: Neurons, as eukaryotic cells, have powerful internal computation capabilities. One neuron can have many distinct states, and brains can use this capability. Processes of neuron growth and maintenance use chemical signalling between cell bodies and synapses, ferrying chemical messengers over microtubules and actin fibres within cells. These processes are computations which, while slower than neural electrical signalling, could allow any neuron to change its state over intervals of seconds or minutes. Based on its state, a single neuron can selectively de-activate some of its synapses, sculpting a dynamic neural net from the static neural connections of the brain. Without this dynamic selection, the static neural networks in brains are too amorphous and dilute to do the computations of neural cognitive models. The use of multi-state neurons in animal brains is illustrated in hierarchical Bayesian object recognition. Multi-state neurons may support a design which is more efficient than two-state neurons, and scales better as object complexity increases. Brains could have evolved to use multi-state neurons. Multi-state neurons could be used in artificial neural networks, to use a kind of non-Hebbian learning which is faster and more focused and controllable than traditional neural net learning. This possibility has not yet been explored in computational models.

Multi-domain performance analysis with scores tailored to user preferences

✇cs.AI, q-bio.NC updates on arXiv.org
By: S\'ebastien Pi\'erard · Adrien Deli\`ege · Marc Van Droogenbroeck
10 December 2025 at 13:00
arXiv:2512.08715v1 Announce Type: cross Abstract: The performance of algorithms, methods, and models tends to depend heavily on the distribution of cases on which they are applied, this distribution being specific to the applicative domain. After performing an evaluation in several domains, it is highly informative to compute a (weighted) mean performance and, as shown in this paper, to scrutinize what happens during this averaging. To achieve this goal, we adopt a probabilistic framework and consider a performance as a probability measure (e.g., a normalized confusion matrix for a classification task). It appears that the corresponding weighted mean is known to be the summarization, and that only some remarkable scores assign to the summarized performance a value equal to a weighted arithmetic mean of the values assigned to the domain-specific performances. These scores include the family of ranking scores, a continuum parameterized by user preferences, and that the weights to consider in the arithmetic mean depend on the user preferences. Based on this, we rigorously define four domains, named easiest, most difficult, preponderant, and bottleneck domains, as functions of user preferences. After establishing the theory in a general setting, regardless of the task, we develop new visual tools for two-class classification.

AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Johann Wenckstern · Eeshaan Jain · Yexiang Cheng · Benedikt von Querfurth · Kiril Vasilev · Matteo Pariset · Phil F. Cheng · Petros Liakopoulos · Olivier Michielin · Andreas Wicki · Gabriele Gut · Charlotte Bunne
10 December 2025 at 13:00
arXiv:2501.06039v2 Announce Type: replace-cross Abstract: Spatial proteomics technologies have transformed our understanding of complex tissue architecture in cancer but present unique challenges for computational analysis. Each study uses a different marker panel and protocol, and most methods are tailored to single cohorts, which limits knowledge transfer and robust biomarker discovery. Here we present Virtual Tissues (VirTues), a general-purpose foundation model for spatial proteomics that learns marker-aware, multi-scale representations of proteins, cells, niches and tissues directly from multiplex imaging data. From a single pretrained backbone, VirTues supports marker reconstruction, cell typing and niche annotation, spatial biomarker discovery, and patient stratification, including zero-shot annotation across heterogeneous panels and datasets. In triple-negative breast cancer, VirTues-derived biomarkers predict anti-PD-L1 chemo-immunotherapy response and stratify disease-free survival in an independent cohort, outperforming state-of-the-art biomarkers derived from the same datasets and current clinical stratification schemes.

Somatic evolution following cancer treatment in normal tissue

✇Nature - Issue - nature.com science feeds
By: Oriol Pich · Sophia Ward · Andrew Rowan · Cristina Naceur-Lombardelli · Oliver Shutkever · Carlos Martinez-Ruiz · Siân Harries · Sonya Hessey · Babu Naidu · James D. Brenton · John Le Quesne · Anne Thomas · Cathy Richards · Matthew G. Krebs · Samra Turajlic · Sanjay Jogai · Simone Zaccaria · David Moore · Crispin T. Hiley · Kai-Keen Shiu · John Bridgewater · Daniel Hochhauser · Martin Forster · Siow Ming Lee · Tanya Ahmad · Dionysis Papadatos-Pastos · Sam Janes · Peter Van Loo · Katey Enfield · Ariana Huebner · Sergio Quezada · Stephan Beck · Tariq Enver · David R. Pearce · Mary Falzon · Ron Sinclair · Zoe Rhodes · Teresa Marafioti · Miriam Mitchison · Mark Linch · Sebastian Brandner · Selvaraju Veeriah · Heather Shaw · Gerhardt Attard · Faye Gishen · Maise Al-Bakir · Nnenna Kanu · Francisco Gimeno-Valiente · Emilia L. Lim · James Reading · Benny Chain · Adrienne Flanagan · Emma Colliver · Mihaela Angelova · James Black · Olivia Lucas · William Hill · Wing Kin Liu · Alexander Frankell · Roberto Salgado · Kristiana Grigoriadis · Takahiro Karasaki · Abigail Bunkum · Sara
10 December 2025 at 08:00

Nature, Published online: 10 December 2025; doi:10.1038/s41586-025-09792-4

High-depth sequencing of non-cancerous tissue from patients with metastatic cancer reveals single-base mutational signatures of alcohol, smoking and cancer treatments, and reveals how exogenous factors, including cancer therapies, affect somatic cell evolution.

Foundation models in oncology win benchmarks but miss the clinic

✇Nature Cancer
By: Zhiyun Duan · Qihao Duan · Benjamin Wild · Roland Eils
10 December 2025 at 08:00

Nature Cancer, Published online: 10 December 2025; doi:10.1038/s43018-025-01071-5

Foundation models hold transformative promise for oncology, yet their clinical implementation remains limited, largely owing to their current model design as narrow specialists optimized for static tasks, whereas clinical oncology requires generalist systems capable of integrating multimodal data, capturing disease evolution over time and considering patient perspectives. Design along these requirements is essential to integrating foundation models as trusted partners in cancer care.

Exploring the association between DNA methylation and pancreatic cancer susceptibility through epigenome-wide Mendelian randomization and multi-omics data integration

✇(Multiomics OR Omics) AND (Pancreatic)
By: Pengxu Wang · Feng Rong · Fubao Liu
10 December 2025 at 19:00

Epigenetics. 2025 Dec;20(1):2599682. doi: 10.1080/15592294.2025.2599682. Epub 2025 Dec 9.

ABSTRACT

Investigating the role of DNA methylation in the development of pancreatic cancer (PC) may facilitate identification of potential targets for both diagnosis and treatment. We carried out a comprehensive epigenome-wide Mendelian randomization (EWMR) analysis to investigate the correlation of genetically predicted blood CpG sites with PC. Following this, we conducted various sensitivity analyses and repeated analyses using different selection criteria for instrumental variables and conditional Bayesian colocalization to guarantee the reliability of the results. External validation and a meta-analysis were then performed to further validate these results. Next, we conducted CpG site enrichment analysis, overlap with phenome-wide association studies (PheWAS) catalog analysis, overlap with epigenome-wide association studies (EWAS) Toolkit analysis, and drug target analysis to explore the enrichment, biological functions, and potential therapeutic targets associated with these sites. Finally, we used the SMR-IVW software to perform mediation analysis, aiming to uncover potential tumorigenesis pathways of PC at the transcriptional level from three distinct perspectives. Results showed 253 CpG sites passing sensitivity analysis were significantly associated with PC and 159 CpG sites were validated in at least one replication. After meta-analysis, 38 CpG sites were retained, and all 253 CpG sites were classified into three tiers. Among these, cg26373071 (CLPTM1L), cg14271713, cg11652496 (PSTPIP1), and cg20575191 (PSTPIP1) were placed in tier 1 with strong support. Finally, this study identified genetic susceptibility linked to 253 PC-related CpG sites. This study provides insights into the disease's origins and underscores potential targets for future research.

PMID:41368819 | PMC:PMC12694910 | DOI:10.1080/15592294.2025.2599682

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