Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis

Peng Xue,Yimin Qu,Youlin Qiao,Huijiao Yan,Jiaxu Wang,Dongxu Qin,Samuel Seery,Yu Jiang

doi:10.1038/s41746-022-00559-z

Abstract

Accurate early detection of breast and cervical cancer is vital for treatment success. Here, we conduct a meta-analysis to assess the diagnostic performance of deep learning (DL) algorithms for early breast and cervical cancer identification. Four subgroups are also investigated: cancer type (breast or cervical), validation type (internal or external), imaging modalities (mammography, ultrasound, cytology, or colposcopy), and DL algorithms versus clinicians. Thirty-five studies are deemed eligible for systematic review, 20 of which are meta-analyzed, with a pooled sensitivity of 88% (95% CI 85–90%), specificity of 84% (79–87%), and AUC of 0.92 (0.90–0.94). Acceptable diagnostic performance with analogous DL algorithms was highlighted across all subgroups. Therefore, DL algorithms could be useful for detecting breast and cervical cancer using medical imaging, having equivalent performance to human clinicians. However, this tentative assertion is based on studies with relatively poor designs and reporting, which likely caused bias and overestimated algorithm performance. Evidence-based, standardized guidelines around study methods and reporting are required to improve the quality of DL research.

Highlights

Female breast and cervical cancer remain as major contributors to the burden of cancer[1,2]
Most studies used more than one deep learning (DL) algorithm to report diagnostic performance, we reported the highest accuracy of different DL algorithms for included studies in 20 contingency tables
Artificial Intelligence in medical imaging is without question improving we must subject emerging knowledge to the same rigorous testing we would for any other diagnostic procedure

Summary

Introduction

Female breast and cervical cancer remain as major contributors to the burden of cancer[1,2]. The World Health Organization (WHO) reported that approximately 2.86 million new cases (14.8% of all cancer cases) and 1.03 million deaths (10.3% of all cancer deaths) were recorded worldwide in 20203. This disproportionately affects women, especially in low- and middle-income countries (LMICs), which can be largely attributed to more advanced stage diagnoses, limited access to early diagnostics, and suboptimal treatment[4,5]. Integrative screening for cancer is a complex procedure that needs to take biological and social determinants, as well as ethical constraints into consideration, and as is already known, early detection of breast and cervical cancers are associated with improved prognosis and survival[8,9]. It is vital to select the most accurate and reliable technologies that are capable of identifying early symptoms

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