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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

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Official URL: https://doi.org/10.2196/22394

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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
Subjects: R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions: Faculty of Science > 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:
DateEvent
1 April 2021Published
17 January 2021Accepted
Volume: 23
Number: 4
Article Number: e22394
DOI: 10.2196/22394
Status: Peer Reviewed
Publication Status: Published
Publisher Statement: ** From Crossref journal articles via Jisc Publications Router ** History: epub 01-04-2021; issued 01-04-2021.
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDMinistero della Salutehttp://dx.doi.org/10.13039/501100003196

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