Accurate prediction of disease-free and overall survival in non-small cell lung cancer using patient-level multimodal weakly supervised learning.

Journal: NPJ precision oncology
Published Date: Jun 19, 2025

Abstract

With the rapid progress in artificial intelligence (AI) and digital pathology, prognosis prediction for non-small cell lung cancer (NSCLC) patients has become a critical component of personalized medicine. In this study, we developed a multimodal AI model that integrated whole-slide images and dense clinical data to predict disease-free survival (DFS) and overall survival (OS) with high accuracy for NSCLC patients undergoing surgery. Utilizing data from 618 patients at Beijing Chest Hospital, the model achieved areas under the curve (AUC) of 0.8084 for predicting progression and 0.8021 for predicting death in the test set. Importantly, the model attained balanced accuracies of 0.7047 for predicting progression and 0.6884 for predicting death. By categorizing patients into high-risk and low-risk groups, the model identified significant differences in survival outcomes, with hazard ratios of 4.85 for progression and 4.57 for death, both with p values below 0.0001. Additionally, it uncovered novel digital biomarkers associated with poor prognosis, offering further insights into NSCLC treatment. This model has the potential to revolutionize postoperative decision-making by providing clinicians with a precise tool for predicting DFS and OS, thereby improving patient outcomes.

Authors

  • Yongmeng Li
    Department of Thoracic Surgery, Qianfoshan Hospital in the Shandong Province, Jinan, Shandong, China.
  • Xiaodong Chai
    School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China.
  • Moxuan Yang
    Department of Physics, Capital Normal University, Beijing, China.
  • Jiahang Xiong
    Thorough Lab, Thorough Future, Beijing, China.
  • Junyang Zeng
    College of Light Industry Science and Engineering, Tianjin University of Science and Technology, Tianjin, China.
  • Yun Chen
  • Gang Xu
    University Hospitals of Leicester NHS Trust; UK.
  • Haifeng Lin
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Shuhao Wang
    Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, P. R. China.
  • Nanying Che
    Department of Pathology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing, China cheny0448@163.com.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals