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Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients.

Jingjing Zhang1, Qiang Zhang2, Bo Zhao3

  • 1Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China.

Abdominal Radiology (New York)
|May 26, 2024
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Summary

A deep learning nomogram accurately predicts neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients using CT scans. This tool aids personalized treatment decisions for gastric cancer.

Keywords:
Contrast-enhanced computed tomographyDeep learningLocally advanced gastric cancerNeoadjuvant chemotherapyNomogram

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Area of Science:

  • Radiology
  • Oncology
  • Artificial Intelligence

Background:

  • Locally advanced gastric cancer (LAGC) requires effective neoadjuvant chemotherapy (NAC) to improve treatment outcomes.
  • Predicting NAC response is crucial for tailoring treatment strategies and improving patient prognosis.

Purpose of the Study:

  • To develop and validate a deep learning radiomics nomogram for predicting NAC response in LAGC patients.
  • To integrate multi-phase contrast-enhanced computed tomography (CECT) imaging features with clinical data for enhanced prediction accuracy.

Main Methods:

  • A multi-center retrospective study included 322 LAGC patients.
  • Handcrafted radiomics and EfficientNet V2 deep learning models were applied to CECT images.
  • A nomogram was constructed using handcrafted features, deep learning features, and clinical data.

Main Results:

  • The nomogram demonstrated excellent discriminative ability with an Area Under the ROC Curve (AUC) of 0.848 for the training set.
  • The model showed superior performance compared to clinical models and handcrafted radiomics alone.
  • The nomogram exhibited good calibration and clinical utility via decision curve analysis.

Conclusions:

  • A validated deep learning radiomics nomogram can accurately predict NAC response in LAGC patients.
  • The nomogram, utilizing CECT images and clinical data, offers personalized treatment insights.
  • This approach supports tailored therapeutic strategies for LAGC patients undergoing surgical resection.