Elsevier

Radiotherapy and Oncology

Volume 155, February 2021, Pages 188-203
Radiotherapy and Oncology

Systematic Review
A systematic review and meta-analysis of the prognostic value of radiomics based models in non-small cell lung cancer treated with curative radiotherapy

https://doi.org/10.1016/j.radonc.2020.10.023Get rights and content

Highlights

  • This is a systematic review of radiomics based prognostic models in lung cancer.

  • It includes 40 studies of 55 datasets and 6223 patients.

  • There was heterogeneity in methodology and features included in prognostic models.

  • Meta-analysis found a C-index random effects estimate of 0.57 (95%CI 0.53 – 0.62).

  • Standard features and robust selection techniques should be used in future studies.

Abstract

Background and purpose

Radiomics allows extraction of quantifiable features from imaging. This study performs a systematic review and meta-analysis of the performance of radiomics based prognostic models in non-small cell lung cancer (NSCLC).

Materials and methods

A literature review was performed following PRISMA guidelines. Medline, EMBASE and Cochrane databases were searched for articles investigating radiomics features predictive of overall survival (OS) in NSCLC treated with curative intent radiotherapy. A random-effects meta-analysis of Harrell’s Concordance Index (C-index) was performed on the performance of radiomics models.

Results

Of the 2746 articles retrieved, 40 studies of 55 datasets and 6223 patients were eligible for inclusion in the systematic review. There was significant heterogeneity in the methodology for feature selection and model development. Twelve datasets reported the C-index of radiomics based models in predicting OS and were included in the meta-analysis. The C-index random effects estimate was 0.57 (95% CI 0.53–0.62). There was significant heterogeneity (I2 = 70.3%).

Conclusions

Based on this review, radiomics based models for lung cancer have to date demonstrated modest prognostic capabilities. Future research should consider using standardised radiomics features, robust feature selection and model development, and deep learning techniques, absolving the need for pre-defined features, to improve imaging-based models.

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.

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