Natural history of prostate cancer on active surveillance: stratification by MRI using the PRECISE recommendations in a UK cohort

Giganti, Francesco; Stabile, Armando; Stavrinides, Vasilis; Osinibi, Elizabeth; Retter, Adam; Orczyk, Clément; Panebianco, Valeria; Trock, Bruce J.; Freeman, Alex; Haider, Aiman; Punwani, Shonit; Allen, Clare; Kirkham, Alex; Emberton, Mark; Moore, Caroline M.

doi:10.1007/s00330-020-07256-z

Cited by 44 publications

(28 citation statements)

References 29 publications

(38 reference statements)

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“…mpMRI is an important tool for biopsy planning and also for prostate cancer detection [4,31]. Furthermore, in the future, mpMRI might play an important role for patients under active surveillance [32]. However, an increase in demand for an examination does not automatically lead to an increase in supply, due to the restricted availability of MRI scanners and comparably long examination times using standard techniques.…”

Section: Discussionmentioning

confidence: 99%

Accelerated T2-Weighted TSE Imaging of the Prostate Using Deep Learning Image Reconstruction: A Prospective Comparison with Standard T2-Weighted TSE Imaging

Gassenmaier

Afat

Nickel

et al. 2021

Cancers

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Multiparametric MRI (mpMRI) of the prostate has become the standard of care in prostate cancer evaluation. Recently, deep learning image reconstruction (DLR) methods have been introduced with promising results regarding scan acceleration. Therefore, the aim of this study was to investigate the impact of deep learning image reconstruction (DLR) in a shortened acquisition process of T2-weighted TSE imaging, regarding the image quality and diagnostic confidence, as well as PI-RADS and T2 scoring, as compared to standard T2 TSE imaging. Sixty patients undergoing 3T mpMRI for the evaluation of prostate cancer were prospectively enrolled in this institutional review board-approved study between October 2020 and March 2021. After the acquisition of standard T2 TSE imaging (T2S), the novel T2 TSE sequence with DLR (T2DLR) was applied in three planes. Overall, the acquisition time for T2S resulted in 10:21 min versus 3:50 min for T2DLR. The image evaluation was performed by two radiologists independently using a Likert scale ranging from 1–4 (4 best) applying the following criteria: noise levels, artifacts, overall image quality, diagnostic confidence, and lesion conspicuity. Additionally, T2 and PI-RADS scoring were performed. The mean patient age was 69 ± 9 years (range, 49–85 years). The noise levels and the extent of the artifacts were evaluated to be significantly improved in T2DLR versus T2S by both readers (p < 0.05). Overall image quality was also evaluated to be superior in T2DLR versus T2S in all three acquisition planes (p = 0.005–<0.001). Both readers evaluated the item lesion conspicuity to be superior in T2DLR with a median of 4 versus a median of 3 in T2S (p = 0.001 and <0.001, respectively). T2-weighted TSE imaging of the prostate in three planes with an acquisition time reduction of more than 60% including DLR is feasible with a significant improvement of image quality.

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Section: Discussionmentioning

confidence: 99%

Accelerated T2-Weighted TSE Imaging of the Prostate Using Deep Learning Image Reconstruction: A Prospective Comparison with Standard T2-Weighted TSE Imaging

Gassenmaier

Afat

Nickel

et al. 2021

Cancers

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“…The PRECISE scoring system was developed in 2017 [16] in order to standardise reporting of serial MRI scans in patients on AS to detect clinically significant radiological changes sufficient to either trigger additional investigations or switch to immediate radical treatment. Since then, several groups [17][18][19][20][21] have demonstrated a high negative predictive value of PRECISE, suggesting that it can be used to avoid routine repeat biopsy if the disease is regarded as radiologically stable. However, the comparatively moderate positive predictive value of PRECISE for predicting histopathological disease progression emphasises the relatively subjective nature of the system, particularly when determining PRECISE category 4 lesions as demonstrating "clinically significant" radiological progression [12].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…[24]. In addition, findings of the PRECISE trial revealed that patients without radiological progression during active surveillance have a very low probability of clinical progression [25]. However, the Canary Prostate Active Surveillance Study (PASS) investigators found in a large cohort of men on active surveillance, who subsequently underwent mpMRI-targeted biopsy, that 11% harbored GG 2 PCa within the targeted lesion, while 13% had GG 2 PCa outside of it [26].…”

Section: Implication Of Comprehensive Evaluation For Therapy Decisionmentioning

confidence: 99%

Intermediate-risk Prostate Cancer—A Sheep in Wolf’s Clothing?

Heidegger

Hamdy

Bergh

et al. 2023

European Urology Oncology

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This case-based discussion describes a 65-year-old man newly diagnosed with International Society of Urological Pathology (ISUP) grade 2 prostate cancer (PCa). According to the European Association of Urology classification system, the patient harbors an intermediate-risk cancer. In step-by step discussion, we elaborate guideline-based treatment modalities for intermediate-risk PCa focused on debating active surveillance versus active treatment. Thereby, we discuss the importance of patient characteristics, including age, hereditary factors, life expectancy and comorbidity status, findings of multiparametric magnetic resonance imaging, as well as prostate-specific antigen (PSA) density and PSA kinetics, in predicting the clinical course of the disease. In addition, we focus on cribriform pathology as a predictor of adverse outcomes and critically discuss its relevance in patient management. Lastly, we outline genomic stratification in ISUP 2 cancer as a future tool to predict PCa aggressiveness. Patient summary: Based on current guidelines, patients with intermediate-risk prostate cancer are treated actively or can alternatively undergo an active surveillance approach when favorable risk factors are present. One major issue is to discriminate between patients who benefit from an active therapy approach and those who benefit from a deferred treatment. Therefore, reliable biomarkers and early predictors of disease progression are needed urgently.

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Natural history of prostate cancer on active surveillance: stratification by MRI using the PRECISE recommendations in a UK cohort

Cited by 44 publications

References 29 publications

Accelerated T2-Weighted TSE Imaging of the Prostate Using Deep Learning Image Reconstruction: A Prospective Comparison with Standard T2-Weighted TSE Imaging

Accelerated T2-Weighted TSE Imaging of the Prostate Using Deep Learning Image Reconstruction: A Prospective Comparison with Standard T2-Weighted TSE Imaging

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

Intermediate-risk Prostate Cancer—A Sheep in Wolf’s Clothing?

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