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Meta-Analysis
. 2022 Jul 18:10:938113.
doi: 10.3389/fpubh.2022.938113. eCollection 2022.

Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis

Xiushan Zheng  1 , Bo He  1 , Yunhai Hu  1 , Min Ren  1 , Zhiyuan Chen  1 , Zhiguang Zhang  1 , Jun Ma  1 , Lanwei Ouyang  1 , Hongmei Chu  1 , Huan Gao  1 , Wenjing He  2 , Tianhu Liu  3 , Gang Li  3
Affiliations
Meta-Analysis

Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis

Xiushan Zheng et al. Front Public Health. .

Abstract

Background: Artificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the diagnostic accuracy of the models based on deep learning or radiomics for lung cancer staging.

Methods: Studies were systematically reviewed using literature searches from PubMed, EMBASE, Web of Science, and Wanfang Database, according to PRISMA guidelines. Studies about the diagnostic accuracy of radiomics and deep learning, including the identifications of lung cancer, tumor types, malignant lung nodules and lymph node metastase, were included. After identifying the articles, the methodological quality was assessed using the QUADAS-2 checklist. We extracted the characteristic of each study; the sensitivity, specificity, and AUROC for lung cancer diagnosis were summarized for subgroup analysis.

Results: The systematic review identified 19 eligible studies, of which 14 used radiomics models and 5 used deep learning models. The pooled AUROC of 7 studies to determine whether patients had lung cancer was 0.83 (95% CI 0.78-0.88). The pooled AUROC of 9 studies to determine whether patients had NSCLC was 0.78 (95% CI 0.73-0.83). The pooled AUROC of the 6 studies that determined patients had malignant lung nodules was 0.79 (95% CI 0.77-0.82). The pooled AUROC of the other 6 studies that determined whether patients had lymph node metastases was 0.74 (95% CI 0.66-0.82).

Conclusion: The models based on deep learning or radiomics have the potential to improve diagnostic accuracy for lung cancer staging.

Systematic review registration: https://inplasy.com/inplasy-2022-3-0167/, identifier: INPLASY202230167.

Keywords: deep learning; diagnostic accuracy; lung cancer; lymph node metastasis; meta-analysis; radiomics.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
PRISMA flow chart outlining the selection of studies for review.
Figure 2
Figure 2
Summary of forest plots for different classifications. (A) The forest plot of determine if a patient has lung cancer. (B) The forest plot of determining whether the cancer type is NSCLC. (C) The forest plot of predicting benign and malignant pulmonary nodules. (D) The forest plot of predicting lymph node metastasis in lung cancer.
Figure 3
Figure 3
Summary of QUADAS-2 assessments of included studies.
Figure 4
Figure 4
Funnel plot of the area under the receiver operating characteristic in 14 studies.
See this image and copyright information in PMC

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