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Related Concept Videos

Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

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Related Experiment Video

Updated: May 13, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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A Transfer Learning Radiomics Nomogram to Predict the Postoperative Recurrence of Advanced Gastric Cancer.

Liebin Huang1,2, Bao Feng2,3, Zhiqi Yang4

  • 1Department of Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.

Journal of Gastroenterology and Hepatology
|December 27, 2024
PubMed
Summary

Transfer learning (TL) effectively predicts postoperative recurrence in advanced gastric cancer (AGC). The TL radiomic model (TLRM), combining TL signatures from whole slide images (TLS-WSI) and clinical factors, demonstrated superior predictive performance in small-sample studies.

Keywords:
advanced gastric cancerrecurrencetomography (X‐ray computed)transfer learningwhole slide images

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Advanced gastric cancer (AGC) recurrence poses a significant clinical challenge.
  • Predicting postoperative recurrence is crucial for effective patient management.

Purpose of the Study:

  • To develop and evaluate a transfer learning (TL) algorithm for predicting postoperative recurrence in AGC.
  • To assess the value of TL in small-sample clinical studies for AGC recurrence prediction.

Main Methods:

  • A retrospective study included 431 AGC cases from three centers.
  • Transfer learning signatures (TLSs) were constructed from whole slide images (TLS-WSI) and natural images (TLS-ImageNet).
  • A TL radiomic model (TLRM) was built by integrating optimal TLS with clinical factors and CT-based features.

Main Results:

  • TLS-WSI significantly outperformed other models, including TLS-ImageNet, non-TLS, and clinical models (p < 0.05).
  • The TL radiomic model (TLRM) achieved high AUC values in both training (0.9643) and validation cohorts (up to 0.9195).
  • Integrated discriminant improvement (IDI) and decision curve analysis (DCA) confirmed TLRM's superior performance.

Conclusions:

  • Transfer learning, particularly TLS-WSI, shows promise for predicting postoperative recurrence in AGC.
  • The developed TL radiomic model (TLRM) is highly effective for predicting recurrence.
  • TL enhances the performance of clinical research models, especially in small sample sizes.