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. 2020 Dec 25:31:100669.
doi: 10.1016/j.eclinm.2020.100669. eCollection 2021 Jan.

Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis

Qiuhan Zheng  1   2 , Le Yang  1   2 , Bin Zeng  1   2 , Jiahao Li  1   2 , Kaixin Guo  1   2 , Yujie Liang  1   2 , Guiqing Liao  1   2
Affiliations

Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis

Qiuhan Zheng et al. EClinicalMedicine. .

Abstract

Background: Early diagnosis of tumor metastasis is crucial for clinical treatment. Artificial intelligence (AI) has shown great promise in the field of medicine. We therefore aimed to evaluate the diagnostic accuracy of AI algorithms in detecting tumor metastasis using medical radiology imaging.

Methods: We searched PubMed and Web of Science for studies published from January 1, 1997, to January 30, 2020. Studies evaluating an AI model for the diagnosis of tumor metastasis from medical images were included. We excluded studies that used histopathology images or medical wave-form data and those focused on the region segmentation of interest. Studies providing enough information to construct contingency tables were included in a meta-analysis.

Findings: We identified 2620 studies, of which 69 were included. Among them, 34 studies were included in a meta-analysis with a pooled sensitivity of 82% (95% CI 79-84%), specificity of 84% (82-87%) and AUC of 0·90 (0·87-0·92). Analysis for different AI algorithms showed a pooled sensitivity of 87% (83-90%) for machine learning and 86% (82-89%) for deep learning, and a pooled specificity of 89% (82-93%) for machine learning, and 87% (82-91%) for deep learning.

Interpretation: AI algorithms may be used for the diagnosis of tumor metastasis using medical radiology imaging with equivalent or even better performance to health-care professionals, in terms of sensitivity and specificity. At the same time, rigorous reporting standards with external validation and comparison to health-care professionals are urgently needed for AI application in the medical field.

Funding: College students' innovative entrepreneurial training plan program .

Keywords: Artiificial intelligence; Deep learning; Diagnostic meta-analysis; Medical imaging; Tumor metastasis.

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Conflict of interest statement

All authors declare no competing interests.

Figures

Fig 1
Fig. 1
Study selection.
Fig 2
Fig. 2
International research situation.
Fig 3
Fig. 3
(a, b). ROC curves of all studies included in the meta-analysis (34 studies) a: ROC curves of all studies included in the meta-analysis (34 studies with 123 tables) b: ROC curves of studies when selecting contingency tables reporting the highest accuracy (34 studies with 48 tables) Abbreviations: ROC=receiver operating characteristic; SENS= sensitivity; SPEC= specificity.
Fig 4
Fig. 4
(a, b): ROC curves of studies using different algorithms a: ROC curves of studies using machine learning algorithms (32 tables) b: ROC curves of studies using deep learning algorithms (16 tables).
Fig 5
Fig. 5
(a, b): ROC curves of studies with or without external validation a: ROC curves of studies without external validation (41 tables) b: ROC curves of studies with external validation (7 tables).
Fig 6
Fig. 6
(a, b). ROC curves of studies using the same sample for comparing performance between health-care professionals and artificial intelligence algorithms (8 studies) a: Artificial intelligence models (10 tables) b: Health-care professionals (16 tables).
Fig 7
Fig. 7
Forest plot of studies included in the meta-analysis (34 studies).
Fig 8
Fig. 8
(a, b, c). Forest plot of 3 subgroups a: Subgroup 1. Different metastasis types b: Subgroup 2. Different primary tumors c: Subgroup 3. Different imaging types Abbreviations: ES= estimate.
See this image and copyright information in PMC

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