Radiomics and deep learning in lung cancer.
Journal:
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Published Date:
Oct 1, 2020
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.
Authors
Keywords
Computational Biology
Databases, Factual
Deep Learning
False Positive Reactions
Forecasting
Genotype
Humans
Image Processing, Computer-Assisted
Imaging Genomics
Lung Neoplasms
Neoplasm Recurrence, Local
Neoplasm Staging
Phenotype
Prognosis
Radiation Injuries
Radiosurgery
Radiotherapy
Radiotherapy Planning, Computer-Assisted
Tomography, X-Ray Computed
Treatment Outcome