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Micro-morphological feature visualization, auto-classification, and evolution quantitative analysis of tumors by

Gong-Xiang Wei1,2, Yun-Yan Liu1,2, Xue-Wen Ji2,3

  • 1School of Physics and Optoelectronic Engineering, Shandong University of Technology, Zibo, China.

Cancer Medicine
|March 8, 2021
PubMed
Summary

This study introduces synchrotron-based X-ray phase-contrast tomography (SR-PCT) for 3D tumor visualization. Combined with deep learning, it accurately auto-classifies eight types of digestive system tumors, aiding diagnosis.

Keywords:
angiogenesisliver cancermicroenvironmentpathology

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Area of Science:

  • Medical Imaging
  • Pathology
  • Artificial Intelligence

Background:

  • Accurate tumor visualization and quantitative analysis are crucial for early cancer detection and understanding metastasis.
  • Current medical imaging techniques face challenges in multiscale, 3D, non-destructive pathological assessment.

Purpose of the Study:

  • To develop and validate a novel method for high-resolution 3D pathological visualization and quantitative analysis of digestive system tumors.
  • To apply deep learning for automated classification of tumor types based on micro-morphological features.

Main Methods:

  • Utilized synchrotron-based X-ray phase-contrast tomography (SR-PCT) with phase-and-attenuation duality phase retrieval for 3D tumor reconstruction.
  • Extracted a feature set of eight tumor micro-lesion types from high-density resolution SR-PCT data.
  • Trained an AlexNet-based deep convolutional neural network for automated tumor classification.
  • Employed machine learning methods (AUC, PCA) to analyze micro-pathomorphological relationships in liver tumors.

Main Results:

  • Achieved 94.21% average accuracy in auto-classifying eight types of digestive system tumors using the deep learning model.
  • Revealed micro-pathomorphological relationships of liver tumor angiogenesis and progression through quantitative feature analysis.
  • Demonstrated that tumor lesion progression is linked to the inflammation microenvironment.

Conclusions:

  • High phase-contrast 3D pathological characteristics derived from SR-PCT offer excellent recognizability and classifiability for micro tumor lesions.
  • The developed automatic analysis methods show significant potential for improving tumor typing and statistical calculations.
  • This approach aids in understanding tumor evolution and its clinical manifestations.