Radiomics and deep learning in lung cancer

Abstract

Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis–treatment–follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.

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Correspondence to Michele Avanzo.

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J. Stancanello is employed by the company Guerbet SA. M. Avanzo, G. Pirrone and G. Sartor declare that they have no competing interests.

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Avanzo, M., Stancanello, J., Pirrone, G. et al. Radiomics and deep learning in lung cancer. Strahlenther Onkol 196, 879–887 (2020). https://doi.org/10.1007/s00066-020-01625-9

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Keywords

  • Artificial Intelligence
  • Image biomarkers
  • Quantitative Imaging
  • Machine learning
  • PET
  • CT