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Radiomic and genomic machine learning method performance for prostate cancer diagnosis : systematic literature review
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Castaldo, Rossana, Cavaliere, Carlo, Soricelli, Andrea, Salvatore, Marco, Pecchia, Leandro and Franzese, Monica (2021) Radiomic and genomic machine learning method performance for prostate cancer diagnosis : systematic literature review. Journal of Medical Internet Research, 23 (4). e22394. doi:10.2196/22394 ISSN 1438-8871.
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WRAP-Radiomic-genomic-machine-learning-method-performance-prostate-cancer-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1541Kb) | Preview |
Official URL: https://doi.org/10.2196/22394
Abstract
Background Machine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. Objective This study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. Methods Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies–version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice. Results In the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. Conclusions The performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers.
| Item Type: | Journal Article | ||||||
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| Subjects: | R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| SWORD Depositor: | Library Publications Router | ||||||
| Library of Congress Subject Headings (LCSH): | Prostate -- Cancer -- Research, Cancer -- Imaging, Bioinformatics, Machine learning, Cancer -- Diagnosis, Tumor markers | ||||||
| Journal or Publication Title: | Journal of Medical Internet Research | ||||||
| Publisher: | JMIR Publications Inc. | ||||||
| ISSN: | 1438-8871 | ||||||
| Official Date: | 1 April 2021 | ||||||
| Dates: |
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| Volume: | 23 | ||||||
| Number: | 4 | ||||||
| Article Number: | e22394 | ||||||
| DOI: | 10.2196/22394 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Reuse Statement (publisher, data, author rights): | ** From Crossref journal articles via Jisc Publications Router ** History: epub 01-04-2021; issued 01-04-2021. | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 13 April 2021 | ||||||
| Date of first compliant Open Access: | 13 April 2021 | ||||||
| RIOXX Funder/Project Grant: |
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