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Related Experiment Video

Updated: Jul 16, 2026

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PST-Radiomics: a PET/CT lymphoma classification method based on pseudo spatial-temporal radiomic features and

Meng Wang1, Huiyan Jiang1,2

  • 1Software College, Northeastern University, Shenyang 110819, People's Republic of China.

Physics in Medicine and Biology
|November 13, 2023
PubMed
Summary

This study introduces a novel pseudo spatial-temporal radiomics (PST-Radiomics) method for improved lymphoma classification using PET/CT scans. PST-Radiomics effectively captures intra-tumor metabolic heterogeneity, outperforming existing radiomic techniques.

Keywords:
PET/CTconvolutional Neural Networksintra-tumor metabolic heterogeneitypseudo Spatial-Temporal radiomicsrecurrent Neural Networksstructured atrous recurrent moduletumor subtype classification

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Area of Science:

  • Radiomics
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Existing radiomic methods often overlook intra-tumor metabolic heterogeneity (ITMH), a key factor in tumor subtyping.
  • This limitation can hinder the accuracy of lymphoma classification using imaging data.

Purpose of the Study:

  • To propose a novel pseudo spatial-temporal radiomic method (PST-Radiomics) for enhanced lymphoma classification.
  • To leverage intra-tumor metabolic heterogeneity (ITMH) information from PET/CT scans.

Main Methods:

  • Developed a multi-threshold gross tumor volume sequence (GTVS) to exploit ITMH.
  • Extracted 1D radiomic features from PET images and GTVS to create a pseudo spatial-temporal feature sequence (PSTFS).
  • Reshaped PSTFS into 2D pseudo spatial-temporal feature maps (PSTFM) and utilized a light-weighted pseudo spatial-temporal radiomic network (PSTR-Net) for end-to-end learning.

Main Results:

  • PST-Radiomics demonstrated superior performance in a PET/CT lymphoma subtype classification task compared to existing radiomic methods.
  • Quantitative experiments confirmed the effectiveness of the proposed method.

Conclusions:

  • PST-Radiomics successfully incorporates ITMH for improved lymphoma classification.
  • The method's feature map visualization indicates complex feature selection and hierarchical feature extraction, underscoring its superiority.