Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis

• Published in: European radiology 2020-12, Vol.30 (12), p.6877-6887
• Main Authors: Cuocolo, Renato, Cipullo, Maria Brunella, Stanzione, Arnaldo, Romeo, Valeria, Green, Roberta, Cantoni, Valeria, Ponsiglione, Andrea, Ugga, Lorenzo, Imbriaco, Massimo
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Area Under Curve; Biopsy; Cancer surgery; Clinical significance; Confidence intervals; Deep learning; Design standards; Diagnosis, Computer-Assisted - methods; Diagnostic Radiology; Diagnostic systems; Heterogeneity; Humans; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Learning algorithms; Literature reviews; Machine Learning; Magnetic Resonance Imaging; Male; Medicine; Medicine & Public Health; Meta-analysis; Model accuracy; Neuroradiology; Pipelines; Prevalence; Prostate cancer; Prostatectomy; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - pathology; Prostatic Neoplasms - surgery; Radiology; Reference Standards; ROC Curve; Statistical analysis; Statistics; Subgroups; Ultrasound
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-020-07027-w

Objectives The aim of this study was to systematically review the literature and perform a meta-analysis of machine learning (ML) diagnostic accuracy studies focused on clinically significant prostate cancer (csPCa) identification on MRI. Methods Multiple medical databases were systematically searched for studies on ML applications in csPCa identification up to July 31, 2019. Two reviewers screened all papers independently for eligibility. The area under the receiver operating characteristic curves (AUC) was pooled to quantify predictive accuracy. A random-effects model estimated overall effect size while statistical heterogeneity was assessed with the I 2 value. A funnel plot was used to investigate publication bias. Subgroup analyses were performed based on reference standard (biopsy or radical prostatectomy) and ML type (deep and non-deep). Results After the final revision, 12 studies were included in the analysis. Statistical heterogeneity was high both in overall and in subgroup analyses. The overall pooled AUC for ML in csPCa identification was 0.86, with 0.81–0.91 95% confidence intervals (95%CI). The biopsy subgroup ( n  = 9) had a pooled AUC of 0.85 (95%CI = 0.79–0.91) while the radical prostatectomy one ( n  = 3) of 0.88 (95%CI = 0.76–0.99). Deep learning ML ( n  = 4) had a 0.78 AUC (95%CI = 0.69–0.86) while the remaining 8 had AUC = 0.90 (95%CI = 0.85–0.94). Conclusions ML pipelines using prostate MRI to identify csPCa showed good accuracy and should be further investigated, possibly with better standardisation in design and reporting of results. Key Points • Overall pooled AUC was 0.86 with 0.81–0.91 95% confidence intervals. • In the reference standard subgroup analysis, algorithm accuracy was similar with pooled AUCs of 0.85 (0.79–0.91 95% confidence intervals) and 0.88 (0.76–0.99 95% confidence intervals) for studies employing biopsies and radical prostatectomy, respectively. • Deep learning pipelines performed worse (AUC = 0.78, 0.69–0.86 95% confidence intervals) than other approaches (AUC = 0.90, 0.85–0.94 95% confidence intervals).