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Lung adenocarcinoma subtype classification based on contrastive learning model with multimodal integration.

Changmiao Wang1, Lijian Liu2, Chenchen Fan3

  • 1Shenzhen Research Institute of Big Data, Shenzhen, China.

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|August 19, 2025
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Summary

This study introduces a multimodal deep neural network for lung adenocarcinoma classification, integrating CT scans and electronic health records. The model achieves 81.42% accuracy, outperforming existing methods for precise cancer staging.

Keywords:
Clinical InformationLung AdenocarcinomaMultimodal Learning

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in oncology
  • Computational pathology

Background:

  • Accurate lung adenocarcinoma staging is crucial for treatment planning.
  • Challenges include data integration, subtype similarity, and contextual feature extraction.
  • Existing methods struggle with precise differentiation of lung adenocarcinoma subtypes.

Purpose of the Study:

  • To develop a multimodal deep neural network for improved lung adenocarcinoma classification.
  • To integrate computed tomography (CT) images, lesion bounding boxes, and electronic health records.
  • To enhance diagnostic accuracy by overcoming limitations of current approaches.

Main Methods:

  • A multimodal deep neural network integrating CT images, lesion bounding boxes, and electronic health records.
  • Utilized a vision transformer for feature extraction from regions of interest and a fully connected encoder for clinical data.
  • Employed contrastive language-image pre-training for feature optimization and an attention-based module for feature fusion.

Main Results:

  • Achieved a validation accuracy of 81.42% and an area under the curve of 0.9120.
  • Demonstrated superior performance compared to recent multimodal classification approaches.
  • Successfully distinguished among three types of lung adenocarcinomas using integrated data.

Conclusions:

  • The proposed multimodal deep neural network effectively classifies lung adenocarcinoma subtypes.
  • Integration of imaging and clinical data significantly improves diagnostic accuracy.
  • The model offers a promising tool for precise lung cancer staging and treatment selection.