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Deep Learning-Driven Multimodal Integration of miRNA and Radiomic for Lung Cancer Diagnosis.

Yuanyuan Chen1, Dikang Chen1,2, Xiaohui Liu1

  • 1State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.

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|September 26, 2025
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Summary

This review highlights advanced lung cancer diagnostics by integrating microRNA (miRNA) biomarkers with imaging data. Functional nanomaterials and deep learning enhance accuracy, improving tumor characterization beyond traditional biopsies.

Keywords:
deep learninglung cancermiRNA biomarkersmiRNA-seqmultimodal analysisradiomic

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Area of Science:

  • Oncology
  • Biomarkers
  • Medical Imaging
  • Nanotechnology

Background:

  • Lung cancer diagnosis relies on biopsies, which offer limited molecular and microenvironment insights.
  • MicroRNA (miRNA) signatures show promise for lung cancer prediction (AUC=82%) but don't fully capture tumor heterogeneity.
  • Integrating imaging and genomic data offers enhanced diagnostic accuracy.

Purpose of the Study:

  • To review advances in miRNA biomarkers for lung cancer.
  • To explore deep learning applications in radiogenomics for multimodal data fusion.
  • To summarize the role of functional nanomaterials in biosensing for integrated diagnostics.

Main Methods:

  • Review of current literature on miRNA biomarkers and lung cancer.
  • Analysis of deep learning models (e.g., DenseNet) for multimodal data fusion.
  • Examination of functional nanomaterials in biosensing for radiomic-genomic integration.

Main Results:

  • Multimodal fusion of miRNA and radiomic features using DenseNet achieved high accuracy (AUC=0.98, sensitivity=85.7%).
  • Functional nanomaterials enable advanced biosensing for bridging miRNA detection and radiomic data.
  • Integrated approaches show potential to overcome limitations of single-modality diagnostics.

Conclusions:

  • Integrating imaging and genomic data, particularly miRNA profiles, significantly enhances lung cancer diagnostic accuracy.
  • Functional nanomaterials are crucial for developing advanced biosensing platforms for multimodal integration.
  • Further research and development are needed for clinical translation of these advanced diagnostic strategies.