Cargando…

Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives

OBJECTIVES: Radiomics is the extraction of quantitative data from medical imaging, which has the potential to characterise tumour phenotype. The radiomics approach has the capacity to construct predictive models for treatment response, essential for the pursuit of personalised medicine. In this lite...

Descripción completa

Detalles Bibliográficos
Autores principales: Chetan, Madhurima R., Gleeson, Fergus V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813733/
https://www.ncbi.nlm.nih.gov/pubmed/32809167
http://dx.doi.org/10.1007/s00330-020-07141-9
_version_ 1783637915172077568
author Chetan, Madhurima R.
Gleeson, Fergus V.
author_facet Chetan, Madhurima R.
Gleeson, Fergus V.
author_sort Chetan, Madhurima R.
collection PubMed
description OBJECTIVES: Radiomics is the extraction of quantitative data from medical imaging, which has the potential to characterise tumour phenotype. The radiomics approach has the capacity to construct predictive models for treatment response, essential for the pursuit of personalised medicine. In this literature review, we summarise the current status and evaluate the scientific and reporting quality of radiomics research in the prediction of treatment response in non-small-cell lung cancer (NSCLC). METHODS: A comprehensive literature search was conducted using the PubMed database. A total of 178 articles were screened for eligibility and 14 peer-reviewed articles were included. The radiomics quality score (RQS), a radiomics-specific quality metric emulating the TRIPOD guidelines, was used to assess scientific and reporting quality. RESULTS: Included studies reported several predictive markers including first-, second- and high-order features, such as kurtosis, grey-level uniformity and wavelet HLL mean respectively, as well as PET-based metabolic parameters. Quality assessment demonstrated a low median score of + 2.5 (range − 5 to + 9), mainly reflecting a lack of reproducibility and clinical evaluation. There was extensive heterogeneity between studies due to differences in patient population, cancer stage, treatment modality, follow-up timescales and radiomics workflow methodology. CONCLUSIONS: Radiomics research has not yet been translated into clinical use. Efforts towards standardisation and collaboration are needed to identify reproducible radiomic predictors of response. Promising radiomic models must be externally validated and their impact evaluated within the clinical pathway before they can be implemented as a clinical decision-making tool to facilitate personalised treatment for patients with NSCLC. KEY POINTS: • The included studies reported several promising radiomic markers of treatment response in lung cancer; however, there was a lack of reproducibility between studies. • Quality assessment using the radiomics quality score (RQS) demonstrated a low median total score of + 2.5 (range − 5 to + 9). • Future radiomics research should focus on implementation of standardised radiomics features and software, together with external validation in a prospective setting.
format Online
Article
Text
id pubmed-7813733
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Springer Berlin Heidelberg
record_format MEDLINE/PubMed
spelling pubmed-78137332021-01-25 Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives Chetan, Madhurima R. Gleeson, Fergus V. Eur Radiol Chest OBJECTIVES: Radiomics is the extraction of quantitative data from medical imaging, which has the potential to characterise tumour phenotype. The radiomics approach has the capacity to construct predictive models for treatment response, essential for the pursuit of personalised medicine. In this literature review, we summarise the current status and evaluate the scientific and reporting quality of radiomics research in the prediction of treatment response in non-small-cell lung cancer (NSCLC). METHODS: A comprehensive literature search was conducted using the PubMed database. A total of 178 articles were screened for eligibility and 14 peer-reviewed articles were included. The radiomics quality score (RQS), a radiomics-specific quality metric emulating the TRIPOD guidelines, was used to assess scientific and reporting quality. RESULTS: Included studies reported several predictive markers including first-, second- and high-order features, such as kurtosis, grey-level uniformity and wavelet HLL mean respectively, as well as PET-based metabolic parameters. Quality assessment demonstrated a low median score of + 2.5 (range − 5 to + 9), mainly reflecting a lack of reproducibility and clinical evaluation. There was extensive heterogeneity between studies due to differences in patient population, cancer stage, treatment modality, follow-up timescales and radiomics workflow methodology. CONCLUSIONS: Radiomics research has not yet been translated into clinical use. Efforts towards standardisation and collaboration are needed to identify reproducible radiomic predictors of response. Promising radiomic models must be externally validated and their impact evaluated within the clinical pathway before they can be implemented as a clinical decision-making tool to facilitate personalised treatment for patients with NSCLC. KEY POINTS: • The included studies reported several promising radiomic markers of treatment response in lung cancer; however, there was a lack of reproducibility between studies. • Quality assessment using the radiomics quality score (RQS) demonstrated a low median total score of + 2.5 (range − 5 to + 9). • Future radiomics research should focus on implementation of standardised radiomics features and software, together with external validation in a prospective setting. Springer Berlin Heidelberg 2020-08-18 2021 /pmc/articles/PMC7813733/ /pubmed/32809167 http://dx.doi.org/10.1007/s00330-020-07141-9 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Chest
Chetan, Madhurima R.
Gleeson, Fergus V.
Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives
title Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives
title_full Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives
title_fullStr Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives
title_full_unstemmed Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives
title_short Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives
title_sort radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives
topic Chest
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813733/
https://www.ncbi.nlm.nih.gov/pubmed/32809167
http://dx.doi.org/10.1007/s00330-020-07141-9
work_keys_str_mv AT chetanmadhurimar radiomicsinpredictingtreatmentresponseinnonsmallcelllungcancercurrentstatuschallengesandfutureperspectives
AT gleesonfergusv radiomicsinpredictingtreatmentresponseinnonsmallcelllungcancercurrentstatuschallengesandfutureperspectives