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Deep Learning Analysis Based on Dual-energy CT-Derived Iodine Map for Predicting PD-L1 Expression in Gastric Cancer:

Lihong Chen1, Yuncong Zhao2, Xiaomin Tian3

  • 1Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China (L.C., Y.C., Y.X.); The School of Medical Imaging, Fujian Medical University, Fuzhou 350100, China (L.C., Y.Z., S.L., K.C., Y.X.); Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou 350001, China (L.C., Y.X.).

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Summary

This study shows that a deep learning model using dual-energy CT iodine maps can accurately predict PD-L1 expression in gastric cancer (GC) non-invasively. This tool aids in guiding immunotherapy decisions for GC patients.

Keywords:
Deep LearningDual-energy CTGastric CancerIodine MapPD-L1

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Programmed death-ligand 1 (PD-L1) expression is a crucial biomarker for predicting immunotherapy response in gastric cancer (GC).
  • Accurate prediction of PD-L1 expression is essential for optimizing treatment strategies in GC.
  • Current methods for assessing PD-L1 expression can be invasive or time-consuming.

Purpose of the Study:

  • To evaluate the efficacy of a deep learning (DL) model utilizing dual-energy CT (DECT)-derived iodine maps for non-invasively predicting PD-L1 expression levels in GC.
  • To compare the performance of the DL model against traditional clinical models and a combined DL-clinical model.

Main Methods:

  • A multi-center prospective study enrolled 267 GC patients undergoing DECT and gastrectomy.
  • A 50-layer Residual Network was used to extract DL features from iodine maps.
  • A DL feature signature model (DFSigM) was developed and validated internally and externally, alongside clinical and fusion models.

Main Results:

  • The DFSigM demonstrated strong predictive performance with AUCs of 0.854 (training), 0.836 (internal validation), and 0.818 (external validation).
  • DFSigM outperformed the clinical model and showed comparable results to the DL-clinical fusion model.
  • Model interpretability was achieved using SHAP and Grad-CAM, visualizing the decision-making process.

Conclusions:

  • Deep learning analysis of DECT-derived iodine maps provides a valuable, reliable, and interpretable method for non-invasive PD-L1 expression prediction in GC.
  • This approach can potentially improve patient selection for immunotherapy and guide clinical decision-making in GC management.