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Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and

Z Khodabakhshi1, M Amini2, G Hajianfar2

  • 1Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.

Clinical Oncology (Royal College of Radiologists (Great Britain))
|August 20, 2023
PubMed
Summary
This summary is machine-generated.

Radiomic models show potential for classifying non-small cell lung cancer (NSCLC) subtypes using PET and fused images. Harmonisation improved PET performance, highlighting its value in radiomic analysis for NSCLC classification.

Keywords:
HistopathologyNSCLCPET/CTmachine learningradiomics

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Non-small cell lung cancer (NSCLC) classification relies on histopathology.
  • Radiomics offers a non-invasive method for cancer subtyping.
  • Multi-center studies require robust methods to address data variability.

Purpose of the Study:

  • To develop and evaluate radiomic models for NSCLC histopathological subtype classification.
  • To assess the impact of ComBat harmonisation on radiomic model performance.
  • To compare single- and multi-modality radiomic approaches.

Main Methods:

  • Utilized a dual-centre NSCLC dataset.
  • Extracted radiomic features from CT, PET, and fused images.
  • Applied ComBat harmonisation for data correction.
  • Employed Lasso and RFE for feature selection.
  • Evaluated LR, SVM, and AdaBoost classifiers.
  • Assessed model performance using AUC, accuracy, sensitivity, and specificity.

Main Results:

  • A support vector machine model using harmonised PET features achieved an AUC of 0.82.
  • Harmonisation significantly improved performance for PET and guided filtering-based fusion images.
  • CT image performance did not significantly improve post-harmonisation.
  • Image modality and feature selection methods significantly impacted model performance (P<0.001).

Conclusions:

  • Radiomic analysis of PET, CT, and hybrid images holds potential for NSCLC subtype classification.
  • ComBat harmonisation can enhance radiomic model performance, particularly for PET data.
  • Further validation is warranted to optimize these radiomic approaches for clinical application.