ItemArticle

Multimodal artificial intelligence for early cancer detection via liquid biopsy, imaging, and clinical records

Title
Multimodal artificial intelligence for early cancer detection via liquid biopsy, imaging, and clinical records
Creator
Rajesh, M.; Prema, S.; Siva Balan, R.V.; Nilabar Nisha, U.; Chinnu, Silambarasu; Moorthy, Usha
Description
Tumours are diverse and multiscale, making it difficult for modern medicine to diagnose early cancer. Using structured clinical data, radiologic imaging features, and liquid samples, this research presents a multimodal AI framework for the early and reliable detection of cancer. The proposed approach surpasses single-modality approaches by integrating signals from various domains, including cancer genetic, anatomical, and physiological data. Using attention-based fusion, representation learning, and better preprocessing, we developed a prediction model that fine-tuned the weights of different modes. The results of the experiments demonstrated that it outperformed unimodal models on all datasets in terms of sensitivity, specificity, and generalisation. The framework has potential for screening purposes because of its ability to detect cancer at an early stage. Clinical confidence and interpretability were both boosted by the results of explainability tests, which revealed substantial feature contributions. The suggested multimodal framework outperformed unimodal baselines across all assessment cohorts with an AUC of 0.94, sensitivity of 0.91, and specificity of 0.88. Experimental results confirm multimodal fusion's clinically interpretable early cancer detection and precision oncology decision assistance. Copyright 2026. Published by Elsevier B.V.
Source
Intelligence-Based Medicine;Volume;14;Issue;;Article No.;100395;
Date
01-01-2026
Publisher
Elsevier B.V.
Subject
AI explainability; Clinical decision support; Early cancer detection; Liquid biopsy; Multi-omics integration; Multimodal artificial intelligence; Precision oncology; Radiomics
Coverage
Rajesh M., Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission's Research Foundation (DU), Paiyanur, Tamilnadu, Chennai, India; Prema S., Department of Computer Science, R.M.D. Engineering College, Kavaraipettai, Tamil Nadu, India; Siva Balan R.V., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Nilabar Nisha U., Department of Computer Science & Engineering, Mahendra Institute of Technology, Namakkal, India; Chinnu S., Department of child health Nursing, Government College of Nursing Gorakhpur, BRD Medical College Campus, India; Moorthy U., School of Computer Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Bengaluru, Karnataka, Manipal, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 26665212;
Format
online
Language
English
Type
Article

Citation

Rajesh, M.; Prema, S.; Siva Balan, R.V.; Nilabar Nisha, U.; Chinnu, Silambarasu; Moorthy, Usha, “Multimodal artificial intelligence for early cancer detection via liquid biopsy, imaging, and clinical records,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22275.

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