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Abstract

Background

Pulmonary nodules are an early imaging indication of lung cancer, and early detection of pulmonary nodules can improve the prognosis of lung cancer. As one of the applications of machine learning, the convolutional neural network (CNN) applied to computed tomography (CT) imaging data improves the accuracy of diagnosis, but the results could be more consistent.

Purpose

To evaluate the diagnostic performance of CNN in assisting in detecting pulmonary nodules in CT images.

Material and Methods

PubMed, Cochrane Library, Web of Science, Elsevier, CNKI and Wanfang databases were systematically retrieved before 30 April 2023. Two reviewers searched and checked the full text of articles that might meet the criteria. The reference criteria are joint diagnoses by experienced physicians. The pooled sensitivity, specificity and the area under the summary receiver operating characteristic curve (AUC) were calculated by a random-effects model. Meta-regression analysis was performed to explore potential sources of heterogeneity.

Results

Twenty-six studies were included in this meta-analysis, involving 2,391,702 regions of interest, comprising segmented images with a few wide pixels. The combined sensitivity and specificity values of the CNN model in detecting pulmonary nodules were 0.93 and 0.95, respectively. The pooled diagnostic odds ratio was 291. The AUC was 0.98. There was heterogeneity in sensitivity and specificity among the studies. The results suggested that data sources, pretreatment methods, reconstruction slice thickness, population source and locality might contribute to the heterogeneity of these eligible studies.

Conclusion

The CNN model can be a valuable diagnostic tool with high accuracy in detecting pulmonary nodules.

Keywords

Pulmonary nodules deep learning convolutional neural network diagnosis subgroup analysis

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Supplementary Material

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The diagnosis performance of convolutional neural network in the detection of pulmonary nodules: a systematic review and meta-analysis

Abstract

Background

Purpose

Material and Methods

Results

Conclusion

Keywords

Get full access to this article

References

Supplementary Material