Cross-Modal Attention Fusion Network: A Deep Learning Framework for Multi-Modal Lung Cancer Risk Prediction and Complication Assessment

Cross-Modal Attention Fusion Network: A Deep Learning Framework for Multi-Modal Lung Cancer Risk Prediction and Complication Assessment | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 23 March 2026 V1 Latest version Share on Cross-Modal Attention Fusion Network: A Deep Learning Framework for Multi-Modal Lung Cancer Risk Prediction and Complication Assessment Authors : Ardit Hoxha 0009-0004-4257-6398 [email protected] , Erion Kola , and Besnik Shehu Authors Info & Affiliations https://doi.org/10.22541/au.177429892.28233393/v1 100 views 58 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Lung cancer remains a critical global health challenge, with current risk assessment methods limited by single-modal data and traditional approaches. This research addresses the need for more accurate and comprehensive risk prediction by developing an advanced deep learning framework for multi-modal biomedical data fusion. Motivated by recent discoveries linking clinical phenotypes, molecular biomarkers (circRNAs), and gut microbiome to lung cancer and its complications, I propose the Cross-Modal Attention Fusion Network (CMAF-Net). CMAF-Net integrates specialized deep encoders for tabular clinical, circRNA expression, and phylogeneticstructured microbiome data. Its core innovation lies in a cross-modal attention fusion module that dynamically learns intermodal dependencies, complemented by a contrastive learning-based modal alignment loss for semantically consistent feature representations. A multi-task prediction head simultaneously forecasts lung cancer risk and associated complications. Evaluated on a comprehensive simulated dataset, CMAF-Net consistently outperforms traditional machine learning models and state-of-the-art baselines. Notably, it achieves an AUC-ROC of 0.91 for lung cancer prediction, demonstrating a significant improvement. Ablation studies confirmed the crucial contribution of each architectural component. This framework represents a significant step towards leveraging heterogeneous biological information for robust, precise lung cancer screening and personalized patient management. Supplementary Material File (cmaf_net.pdf) Download 1.51 MB Information & Authors Information Version history V1 Version 1 23 March 2026 Copyright This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License Keywords cross-modal attention deep learning lung cancer multi-modal data risk prediction Authors Affiliations Ardit Hoxha 0009-0004-4257-6398 [email protected] European University of Tirana View all articles by this author Erion Kola European University of Tirana View all articles by this author Besnik Shehu European University of Tirana View all articles by this author Metrics & Citations Metrics Article Usage 100 views 58 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ardit Hoxha, Erion Kola, Besnik Shehu. Cross-Modal Attention Fusion Network: A Deep Learning Framework for Multi-Modal Lung Cancer Risk Prediction and Complication Assessment. Authorea . 23 March 2026. 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