Prediction of extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review

被引:0
|
作者
Guerra, Adalgisa [1 ]
Wang, Helen [2 ]
Orton, Matthew R. [3 ]
Konidari, Marianna [3 ]
Papanikolaou, Nickolas K. [3 ]
Koh, Dow Mu [3 ]
Donato, Helena [4 ,5 ]
Alves, Filipe Caseiro [5 ]
机构
[1] Hosp Luz Lisboa, Dept Radiol, Lisbon, Portugal
[2] Royal Marsden Hosp NHS Fdn Trust, Royal Surrey Cty Hosp HSH Fdn Trust, London, England
[3] Royal Marsden Hosp NHS Fdn Trust, London, England
[4] Ctr Hosp & Univ Coimbra, Documentat & Sci Informat Serv, Coimbra, Portugal
[5] Univ Coimbra, Fac Med, Coimbra, Portugal
来源
INSIGHTS INTO IMAGING | 2024年 / 15卷 / 01期
关键词
Systematic review; Radiomics; Machine learning; Prostate cancer; Extracapsular extension; RADICAL PROSTATECTOMY; CLINICAL STAGE; NOMOGRAM;
D O I
10.1186/s13244-024-01776-8
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy.Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score.From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72-0.92 and 0.69-0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias.After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE. Critical relevant statement The use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients.
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页数:10
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