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Distinguishing multiple primary lung cancers from intrapulmonary metastasis using CT-based radiomics.

Mei Huang1, Qinmei Xu2, Mu Zhou3

  • 1Department of Medical Imaging, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.

European Journal of Radiology
|February 5, 2023
PubMed
Summary

This study developed CT-based radiomics models to differentiate multiple primary lung cancers from intrapulmonary metastases. Refined and fusion models showed high accuracy, aiding early diagnosis and treatment guidance.

Keywords:
Intrapulmonary metastasisMultiple primary lung cancersRadiomicsRefined-radiomics

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging Analysis

Background:

  • Distinguishing multiple primary lung cancers (MPLCs) from intrapulmonary metastases (IPMs) is crucial for effective treatment planning.
  • Accurate differentiation impacts patient management and prognosis in lung cancer cases.

Purpose of the Study:

  • To develop and evaluate CT-based radiomics models for efficient discrimination between MPLCs and IPMs.
  • To compare the performance of radiomics models against clinical-CT models.

Main Methods:

  • Retrospective analysis of 127 patients with 254 pathologically confirmed lung tumors.
  • Extraction of radiomics features from CT scans, including refined radiomics with relative differences.
  • Development of radiomics model (RM), refined radiomics model (RRM), and fusion models (FM1, FM2) using L1-norm regularization and ANOVA for feature selection.
  • Performance assessment using Area Under the Receiver Operating Characteristic Curve (AUC-ROC).

Main Results:

  • The refined radiomics model (RRM) achieved a higher AUC (0.870) than the standard radiomics model (RM, 0.857) and the clinical-CT model (CCM, 0.782).
  • Fusion models (FM1, FM2) further improved prediction performance, with AUCs of 0.885 and 0.889, respectively.
  • No significant performance difference was observed among the fusion models and the RRM.

Conclusions:

  • CT-based radiomics models demonstrate strong performance in differentiating MPLCs from IPMs.
  • These models hold potential for early diagnosis and guiding treatment strategies for lung cancer patients.
  • The refined and fusion radiomics approaches offer enhanced diagnostic accuracy compared to conventional methods.