期刊
EUROPEAN RADIOLOGY
卷 32, 期 1, 页码 680-689出版社
SPRINGER
DOI: 10.1007/s00330-021-08151-x
关键词
Prostate cancer; Magnetic resonance imaging; Active surveillance; PRECISE; Machine learning
资金
- National Institute of Health Research Cambridge Biomedical Research Centre, Cancer Research UK (Cambridge Imaging Centre) [C197/A16465]
- Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and Manchester
- Cambridge Experimental Cancer Medicine Centre
- Gates Cambridge Trust
- Medical Research Council [MR/R02524X/1]
- Ministry of Science and Higher Education of the Russian Federation within the programme developing World-Class Research Centres Digital Biodesign and Personalized Healthcare [075-15-2020-926]
The study compared the performance of the PRECISE scoring system and several MRI-derived delta-radiomics models for predicting histopathological prostate cancer progression in patients on active surveillance. PRECISE scores had the highest specificity and positive predictive value, while RF had the highest sensitivity and negative predictive value. Although PRECISE had a slightly higher AUC compared to other models, the differences were not significant, indicating comparable performance in predicting PCa progression.
Objectives To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). Methods The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5-16 years' experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong's test. Results The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34-0.77). Conclusions PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients.
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