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

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.

Fig. 1. PRISMA flowchart of study selection.

Displayed is the PRISMA (preferred reporting items for systematic reviews and meta-analyses) flow of search methodology and literature selection process.

Fig. 2. Pooled overall performance of DL algorithms.

a Receiver operator characteristic (ROC) curves of all studies included in the meta-analysis (20 studies with 55 tables), and b ROC curves of studies reporting the highest accuracy (20 studies with 20 tables).

Fig. 3. Pooled performance of DL algorithms using different validation types.

a Receiver operator characteristic (ROC) curves of studies with internal validations (15 studies with 40 tables), b ROC curves of studies with external validations (8 studies with 15 tables).

Fig. 4. Pooled performance of DL algorithms using different cancer types.

a Receiver operator characteristic (ROC) curves of studies in detecting breast cancer (10 studies with 36 tables), and b ROC curves of studies in detecting cervical cancer (10 studies with 19 tables).

Fig. 5. Pooled performance of DL algorithms using different imaging modalities.

a Receiver operator characteristic (ROC) curves of studies using mammography (4 studies with 15 tables), b ROC curves of studies using ultrasound (4 studies with 17 tables), c ROC curves of studies using cytology (4 studies with 6 tables), and d presented ROC curves of studies using colposcopy (4 studies with 11 tables).

Fig. 6. Pooled performance of DL algorithms versus human clinicians and human clinicians using the same sample.

a Receiver operator characteristic (ROC) curves of studies using DL algorithms (11 studies with 29 tables), and b ROC curves of studies using human clinicians (11 studies with 18 tables).

Fig. 7. Summary estimate of pooled performance using forest plot.

Data presented forest plot of studies included in the meta-analysis (20 studies).