Systematic ReviewA systematic review and meta-analysis of the prognostic value of radiomics based models in non-small cell lung cancer treated with curative radiotherapy
Section snippets
Materials and methods
A structured review of the literature was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The study protocol was prospectively registered on PROSPERO (registration no. CRD42018115328). Medline, EMBASE and Cochrane databases were searched from January 1, 2010 up to November 26, 2019, for original English articles that analysed the value of radiomics features in predicting OS in patients with NSCLC treated with curative intent
Results
Of the 2746 articles retrieved, 40 studies of 55 datasets and 6223 patients were eligible for inclusion in the systematic review (see Table 1 and Fig. 2).
Nineteen (35%) of the datasets originated from the Netherlands, with six datasets explicitly using the MAASTRO clinic ‘Lung 1’ (or a subset of) dataset from Aerts et al. 2014 [23]. The median/mean age of patients ranged from 58 to 79 years old. Percentage of male patients ranged from 31% to 93%. Twenty-eight percent of patients were Stage I,
Discussion
This is the first study to the authors’ knowledge that performs both a systematic review and meta-analysis of the performance of radiomics based models in predicting survival in patients with NSCLC undergoing curative intent radiotherapy. The results of the meta-analysis show that radiomics based models have modest capabilities in predicting OS based upon the results of the pooled C-index.
The best performing model included within the meta-analysis reported a C-index of 0.72 (95%CI 0.64–0.80)
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References (80)
- et al.
The eighth edition lung cancer stage classification
Chest
(2017) - et al.
Refining prognosis in lung cancer: a report on the quality and relevance of clinical prognostic tools
J Thorac Oncol
(2015) - et al.
Radiomics: extracting more information from medical images using advanced feature analysis
Eur J Cancer
(2012) - et al.
Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology
Lung Cancer
(2019) - et al.
Predicting malignant nodules from screening CT scans
J Thorac Oncol
(2016) - et al.
CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer
Radiother Oncol
(2016) - et al.
Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy
Radiother Oncol
(2018) - et al.
Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images
Radiother Oncol
(2017) - et al.
Why validation of prognostic models matters?
Radiother Oncol
(2018) - et al.
Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer
Radiother Oncol
(2018)
Exploration of temporal stability and prognostic power of radiomic features based on electronic portal imaging device images
Physica Med
Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans
Physica Med
Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
Physica Med
Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients: Evaluation of the added prognostic value for overall survival and locoregional recurrence
Radiother Oncol
Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer
Int J Radiat Oncol Biol Phys
Cancer statistics, 2019
CA: Cancer J Clin
Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer
Br J Cancer
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Nat Commun
Radiomics for classifying histological subtypes of lung cancer based on multiphasic contrast-enhanced computed tomography
J Comput Assist Tomogr
Exploratory study to identify radiomics classifiers for lung cancer histology
Front Oncol
Texture Analysis on [18 F] FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types
Mol Imag Biol
Spatial-temporal variability of radiomic features and its effect on the classification of lung cancer histology
Phys Med Biol
Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer
Eur Radiol
Non-small cell lung cancer evaluated with quantitative contrast-enhanced CT and PET-CT: net enhancement and standardized uptake values are related to tumour size and histology
Med Sci Monitor
Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy
Phys Med Biol
Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non—small cell lung cancer
Radiology
Boosting the concordance index for survival data–a unified framework to derive and evaluate biomarker combinations
PLoS ONE
Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) The TRIPOD Statement
Circulation
Conducting meta-analyses in R with the metafor package
J Stat Softw
How to perform a meta-analysis with R: a practical tutorial
Evid-Based Mental Health
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. [Erratum appears in Nat Commun. 2014;5:4644 Note: Cavalho, Sara [corrected to Carvalho, Sara]]
Nat Commun
Imaging features from pretreatment CT scans are associated with clinical outcomes in nonsmall-cell lung cancer patients treated with stereotactic body radiotherapy
Med Phys
Pretreatment 18F-FDG PET textural features in locally advanced non-small cell lung cancer: secondary analysis of ACRIN 6668/RTOG 0235
J Nucl Med
Stage III non-small cell lung cancer: prognostic value of FDG PET quantitative imaging features combined with clinical prognostic factors
Radiology
Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics
PLoS ONE
CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy
Radiat Oncol
Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer
Eur Radiol
Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis
Radiat Oncol
Machine learning methods for quantitative radiomic biomarkers
Sci Rep
Cited by (40)
A Glimpse Into the Future for Unresectable Stage III Non-Small Cell Lung Cancer
2024, International Journal of Radiation Oncology Biology PhysicsA 3D lung lesion variational autoencoder
2024, Cell Reports MethodsArtificial intelligence predicts lung cancer radiotherapy response: A meta-analysis
2023, Artificial Intelligence in Medicine