ArticleCell reports. Medicine2025
AI-enabled molecular phenotyping and prognostic predictions in lung cancer through multimodal clinical information integration.
Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Multi-modal AI-enabled steatotic liver disease diagnostics using facial images and metabolomics.Cell reports. Medicine · 2026Article
- Optimizing surgical decision-making for lung adenocarcinoma by preoperatively identifying pathological high-risk factors: retrospective development and prospective validation of a deep learning model.International journal of surgery (London, England) · 2026Article
- Preoperative identification of tumor deposits in rectal cancer using a transformer-based multimodal fusion model: a multicenter retrospective study.BMC medical imaging · 2026Article
- cGAS-STING pathway regulated by spatiotemporal heterogeneity of tumor microenvironment and precision therapy strategies in lung cancer.Journal of experimental & clinical cancer research : CR · 2026Review
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Application of artificial intelligence in lung cancer diagnosis, therapy, and prognosis.BMC medical genomics · 2026Review
- Operationalizing Large Language Models for Clinical Research Data Extraction: Methods, Quality Control, and Governance.Journal of medical systems · 2026Review
- A multimodal synergistic model for personalized neoadjuvant immunochemotherapy in esophageal cancer.Cell reports. Medicine · 2025Article
- Intraocular Cytokine Level Prediction from Fundus Images and Optical Coherence Tomography.Sensors (Basel, Switzerland) · 2025Article
- Harnessing multi-omics approaches to decipher tumor evolution and improve diagnosis and therapy in lung cancer.Biomarker research · 2025Review
- Predicting targeted therapy resistance in non-small cell lung cancer using multimodal machine learning.Journal of thoracic disease · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer remains the leading cause of cancer-related mortality worldwide. The need for cost-effective, non-invasive methods to detect specific gene mutations for targeted therapy and predict patient survival outcomes underscores the importance of advancing diagnostic and prognostic capabilities. Contemporary lung cancer diagnostic models often fail to integrate diverse patient data, leading to incomplete clinical assessments. To address these challenges, we propose LUCID, a multimodal data integration framework designed to predict epidermal growth factor receptor (EGFR) mutation status and survival outcomes in patients with lung cancer. Tailored for early-stage clinical assessment, LUCID leverages lung computed tomography (CT) images, chief complaints, laboratory test results, and demographic data to deliver comprehensive, non-invasive predictions. LUCID achieved strong performance in a retrospective cohort of 5,175 patients, with areas under the receiver operating characteristic curve (AUCs) ranging from 0.851 to 0.881 for EGFR mutation prediction and from 0.821 to 0.912 for survival time prediction. The model also demonstrated robustness across external validation cohorts and in scenarios with missing modalities.
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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 Socratic graph.