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Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis
SIMPLE SUMMARY: Lung cancer screening has been shown to help reduce mortality in selected populations of smokers; however, performing screening programs at a larger scale with high accuracy is still a challenge. The use of artificial intelligence (AI) has been investigated to improve large scale scr...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405626/ https://www.ncbi.nlm.nih.gov/pubmed/36010850 http://dx.doi.org/10.3390/cancers14163856 |
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author | Forte, Gabriele C. Altmayer, Stephan Silva, Ricardo F. Stefani, Mariana T. Libermann, Lucas L. Cavion, Cesar C. Youssef, Ali Forghani, Reza King, Jeremy Mohamed, Tan-Lucien Andrade, Rubens G. F. Hochhegger, Bruno |
author_facet | Forte, Gabriele C. Altmayer, Stephan Silva, Ricardo F. Stefani, Mariana T. Libermann, Lucas L. Cavion, Cesar C. Youssef, Ali Forghani, Reza King, Jeremy Mohamed, Tan-Lucien Andrade, Rubens G. F. Hochhegger, Bruno |
author_sort | Forte, Gabriele C. |
collection | PubMed |
description | SIMPLE SUMMARY: Lung cancer screening has been shown to help reduce mortality in selected populations of smokers; however, performing screening programs at a larger scale with high accuracy is still a challenge. The use of artificial intelligence (AI) has been investigated to improve large scale screening. We have performed a meta-analysis of the diagnostic accuracy of deep learning (DL) algorithms to diagnose lung cancer. Combining six eligible studies, the pooled sensitivity and specificity of DL algorithms were 0.93 (95% CI 0.85–0.98) and 0.68 (95% CI 0.49–0.84), respectively. Despite remaining challenges in the field, AI is likely to play an important role in disease screening in the future. ABSTRACT: We conducted a systematic review and meta-analysis of the diagnostic performance of current deep learning algorithms for the diagnosis of lung cancer. We searched major databases up to June 2022 to include studies that used artificial intelligence to diagnose lung cancer, using the histopathological analysis of true positive cases as a reference. The quality of the included studies was assessed independently by two authors based on the revised Quality Assessment of Diagnostic Accuracy Studies. Six studies were included in the analysis. The pooled sensitivity and specificity were 0.93 (95% CI 0.85–0.98) and 0.68 (95% CI 0.49–0.84), respectively. Despite the significantly high heterogeneity for sensitivity (I(2) = 94%, p < 0.01) and specificity (I(2) = 99%, p < 0.01), most of it was attributed to the threshold effect. The pooled SROC curve with a bivariate approach yielded an area under the curve (AUC) of 0.90 (95% CI 0.86 to 0.92). The DOR for the studies was 26.7 (95% CI 19.7–36.2) and heterogeneity was 3% (p = 0.40). In this systematic review and meta-analysis, we found that when using the summary point from the SROC, the pooled sensitivity and specificity of DL algorithms for the diagnosis of lung cancer were 93% and 68%, respectively. |
format | Online Article Text |
id | pubmed-9405626 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94056262022-08-26 Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis Forte, Gabriele C. Altmayer, Stephan Silva, Ricardo F. Stefani, Mariana T. Libermann, Lucas L. Cavion, Cesar C. Youssef, Ali Forghani, Reza King, Jeremy Mohamed, Tan-Lucien Andrade, Rubens G. F. Hochhegger, Bruno Cancers (Basel) Systematic Review SIMPLE SUMMARY: Lung cancer screening has been shown to help reduce mortality in selected populations of smokers; however, performing screening programs at a larger scale with high accuracy is still a challenge. The use of artificial intelligence (AI) has been investigated to improve large scale screening. We have performed a meta-analysis of the diagnostic accuracy of deep learning (DL) algorithms to diagnose lung cancer. Combining six eligible studies, the pooled sensitivity and specificity of DL algorithms were 0.93 (95% CI 0.85–0.98) and 0.68 (95% CI 0.49–0.84), respectively. Despite remaining challenges in the field, AI is likely to play an important role in disease screening in the future. ABSTRACT: We conducted a systematic review and meta-analysis of the diagnostic performance of current deep learning algorithms for the diagnosis of lung cancer. We searched major databases up to June 2022 to include studies that used artificial intelligence to diagnose lung cancer, using the histopathological analysis of true positive cases as a reference. The quality of the included studies was assessed independently by two authors based on the revised Quality Assessment of Diagnostic Accuracy Studies. Six studies were included in the analysis. The pooled sensitivity and specificity were 0.93 (95% CI 0.85–0.98) and 0.68 (95% CI 0.49–0.84), respectively. Despite the significantly high heterogeneity for sensitivity (I(2) = 94%, p < 0.01) and specificity (I(2) = 99%, p < 0.01), most of it was attributed to the threshold effect. The pooled SROC curve with a bivariate approach yielded an area under the curve (AUC) of 0.90 (95% CI 0.86 to 0.92). The DOR for the studies was 26.7 (95% CI 19.7–36.2) and heterogeneity was 3% (p = 0.40). In this systematic review and meta-analysis, we found that when using the summary point from the SROC, the pooled sensitivity and specificity of DL algorithms for the diagnosis of lung cancer were 93% and 68%, respectively. MDPI 2022-08-09 /pmc/articles/PMC9405626/ /pubmed/36010850 http://dx.doi.org/10.3390/cancers14163856 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Systematic Review Forte, Gabriele C. Altmayer, Stephan Silva, Ricardo F. Stefani, Mariana T. Libermann, Lucas L. Cavion, Cesar C. Youssef, Ali Forghani, Reza King, Jeremy Mohamed, Tan-Lucien Andrade, Rubens G. F. Hochhegger, Bruno Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis |
title | Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis |
title_full | Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis |
title_fullStr | Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis |
title_full_unstemmed | Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis |
title_short | Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis |
title_sort | deep learning algorithms for diagnosis of lung cancer: a systematic review and meta-analysis |
topic | Systematic Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405626/ https://www.ncbi.nlm.nih.gov/pubmed/36010850 http://dx.doi.org/10.3390/cancers14163856 |
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