Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jan;32(1):680-689.
doi: 10.1007/s00330-021-08151-x. Epub 2021 Jul 13.

Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance

Affiliations

Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance

Nikita Sushentsev et al. Eur Radiol. 2022 Jan.

Abstract

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.

Key points: • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.

Keywords: Active surveillance; Machine learning; Magnetic resonance imaging; PRECISE; Prostate cancer.

PubMed Disclaimer

Conflict of interest statement

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Figures

Fig. 1
Fig. 1
Comparison of T2-weighted images of the prostate obtained at baseline pre-biopsy (a, c, e) and follow-up (b, d, f) MRI scans from patients enrolled on active surveillance. Images (a, b) were obtained from a patient with stable 3 + 3 = 6 disease that showed neither radiological nor histopathological progression over a follow-up period of 3 years (PRECISE 3). Images (c, d) were obtained from a patient with both radiological (PRECISE 5) and histopathological (3 + 3 = 6 to 4 + 3 = 7) progression. Images (e, f) were obtained from a patient with confirmed histopathological progression (3 + 3 = 6 to 3 + 4 = 7) but radiologically stable disease (PRECISE 3). In all presented cases, the clinical outcome was successfully predicted by all three delta-radiomics models used
Fig. 2
Fig. 2
Flow diagram summarising the key stages of delta-radiomics analysis used in this study, including calibration, pre-processing, delta-radiomics feature calculation, and predictive modelling using the leave-one-out cross-validation (LOOCV) approach. ADC, apparent diffusion coefficient; ICC, intraclass correlation coefficient; MRI, magnetic resonance imaging; T2WI, T2-weighted imaging
Fig. 3
Fig. 3
Receiver operating characteristic (ROC) curves for PRECISE, parenclitic networks, lasso regression, and random forest for predicting histopathological progression of prostate cancer in patients on active surveillance. The embedded legend denotes areas under ROC curves for each method

Similar articles

Cited by

References

    1. Sung H, Ferlay J, Siegel RL et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 10.3322/caac.21660 - PubMed
    1. Negoita S, Feuer EJ, Mariotto A, et al. Annual Report to the Nation on the Status of Cancer, part II: recent changes in prostate cancer trends and disease characteristics. Cancer. 2018;124:2801–2814. doi: 10.1002/cncr.31549. - DOI - PMC - PubMed
    1. Results of the NPCA (2020) Prospective Audit in England and Wales for men diagnosed from 1 National Prostate Cancer Audit Seventh Year Annual Report-Results of the NPCA Prospective Audit in England and Wales for men diagnosed 1
    1. Mohler JL, Antonarakis ES, Armstrong AJ, et al. Prostate cancer, version 2.2019. JNCCN J Natl Compr Cancer Netw. 2019;17:479–505. doi: 10.6004/jnccn.2019.0023. - DOI - PubMed
    1. Mottet N, Bellmunt J, Bolla M, et al. EAU-ESTRO-SIOG guidelines on prostate cancer. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol. 2017;71:618–629. doi: 10.1016/j.eururo.2016.08.003. - DOI - PubMed