The Role of Radiomics in the Prediction of Clinically Significant Prostate Cancer in the PI-RADS v2 and v2.1 Era : A Systematic Review
Antolín, Andreu (Hospital Universitari Vall d'Hebron)
Roson, Nuria (Hospital Universitari Vall d'Hebron)
Mast, Richard (Hospital Universitari Vall d'Hebron)
Arce, Javier (Hospital Universitari Vall d'Hebron)
Almodovar, Ramon (Hospital Universitari Vall d'Hebron)
Cortada, Roger (Hospital Universitari Vall d'Hebron)
Maceda, Almudena (Vall d'Hebron Institut de Recerca (VHIR))
Escobar, Manuel (Hospital Universitari Vall d'Hebron)
Trilla Herrera, Enrique (Universitat Autònoma de Barcelona. Departament de Cirurgia)
Morote Robles, Juan (Universitat Autònoma de Barcelona. Departament de Cirurgia)

Data:	2024
Resum:	There is still an overdiagnosis of indolent prostate cancer (iPCa) lesions using the Prostate Imaging-Reporting and Data System (PI-RADS), and radiomics has emerged as a promising tool to improve the diagnosis of clinically significant prostate cancer (csPCa) lesions. However, the current state and applicability of radiomics remains a challenge. This systematic review aims at evaluating the evidence of handcrafted and deep radiomics in differentiating lesions at risk of having csPCa from those with iPCa and benign pathology. The review highlighted a good performance of radiomics but without significant differences with radiologist assessment (PI-RADS), as well as several methodological limitations in the reported studies, which might induce bias. Future studies should improve methodological aspects to ensure the clinical applicability of radiomics, especially the need for clinical prospective studies and the comparison with PI-RADS. Early detection of clinically significant prostate cancer (csPCa) has substantially improved with the latest PI-RADS versions. However, there is still an overdiagnosis of indolent lesions (iPCa), and radiomics has emerged as a potential solution. The aim of this systematic review is to evaluate the role of handcrafted and deep radiomics in differentiating lesions with csPCa from those with iPCa and benign lesions on prostate MRI assessed with PI-RADS v2 and/or 2. 1. The literature search was conducted in PubMed, Cochrane, and Web of Science databases to select relevant studies. Quality assessment was carried out with Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2), Radiomic Quality Score (RQS), and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) tools. A total of 14 studies were deemed as relevant from 411 publications. The results highlighted a good performance of handcrafted and deep radiomics methods for csPCa detection, but without significant differences compared to radiologists (PI-RADS) in the few studies in which it was assessed. Moreover, heterogeneity and restrictions were found in the studies and quality analysis, which might induce bias. Future studies should tackle these problems to encourage clinical applicability. Prospective studies and comparison with radiologists (PI-RADS) are needed to better understand its potential.
Ajuts:	European Commission 101095382
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Clinically significant prostate cancer ; PI-RADS ; Magnetic resonance imaging ; Radiomics ; Deep learning ; Machine learning ; Systematic review ; Prediction
Publicat a:	Cancers, Vol. 16, Núm. 17 (august 2024) , ISSN 2072-6694

DOI: 10.3390/cancers16172951
PMID: 39272809

20 p, 1.7 MB

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