MRI in active surveillance: a critical review

Stavrinides, Vasilis; Giganti, Francesco; Emberton, Mark; Moore, Caroline M.

doi:10.1038/s41391-018-0077-2

Cited by 43 publications

(32 citation statements)

References 67 publications

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“…Current active surveillance protocols require monitoring with regular clinical evaluations and prostate biopsies. The mpMRI is increasingly used to monitor non-invasively the low-risk PCa patients on active surveillance and to enable targeted biopsies [ 18 – 20 ]. Assistance in identification and segmentation of clinically significant PCa may reduce MRI-reading variability in active surveillance patients.…”

Section: Introductionmentioning

confidence: 99%

Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI

Arif

Schoots

et al. 2020

Eur Radiol

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Objectives To develop an automatic method for identification and segmentation of clinically significant prostate cancer in low-risk patients and to evaluate the performance in a routine clinical setting. Methods A consecutive cohort (n = 292) from a prospective database of low-risk patients eligible for the active surveillance was selected. A 3-T multi-parametric MRI at 3 months after inclusion was performed. Histopathology from biopsies was used as reference standard. MRI positivity was defined as PI-RADS score ≥ 3, histopathology positivity was defined as ISUP grade ≥ 2. The selected cohort contained four patient groups: (1) MRI-positive targeted biopsy-positive (n = 116), (2) MRI-negative systematic biopsy-negative (n = 55), (3) MRI-positive targeted biopsy-negative (n = 113), (4) MRI-negative systematic biopsy-positive (n = 8). Group 1 was further divided into three sets and a 3D convolutional neural network was trained using different combinations of these sets. Two MRI sequences (T2w, b = 800 DWI) and the ADC map were used as separate input channels for the model. After training, the model was evaluated on the remaining group 1 patients together with the patients of groups 2 and 3 to identify and segment clinically significant prostate cancer. Results The average sensitivity achieved was 82–92% at an average specificity of 43–76% with an area under the curve (AUC) of 0.65 to 0.89 for different lesion volumes ranging from > 0.03 to > 0.5 cc. Conclusions The proposed deep learning computer-aided method yields promising results in identification and segmentation of clinically significant prostate cancer and in confirming low-risk cancer (ISUP grade ≤ 1) in patients on active surveillance. Key Points • Clinically significant prostate cancer identification and segmentation on multi-parametric MRI is feasible in low-risk patients using a deep neural network. • The deep neural network for significant prostate cancer localization performs better for lesions with larger volumes sizes (> 0.5 cc) as compared to small lesions (> 0.03 cc). • For the evaluation of automatic prostate cancer segmentation methods in the active surveillance cohort, the large discordance group (MRI positive, targeted biopsy negative) should be included.

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Section: Introductionmentioning

confidence: 99%

Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI

Arif

Schoots

et al. 2020

Eur Radiol

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“…The role of magnetic resonance imaging (MRI) in this setting has also expanded, and there is evidence that almost 90% of academic centres in the USA routinely perform prostate MRI [2]. Serial MRI during AS protocols has been fully incorporated in the UK National Institute for Health and Care Excellence (NICE) guidelines [3][4][5][6]. However, there is still a lack of consistency in how serial MRI data during AS for PCa should be acquired and reported, either for an individual patient or across different cohorts.…”

Section: Introductionmentioning

confidence: 99%

Interobserver reproducibility of the PRECISE scoring system for prostate MRI on active surveillance: results from a two-centre pilot study

et al. 2019

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Objectives We aimed to determine the interobserver reproducibility of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) criteria for magnetic resonance imaging in patients on active surveillance (AS) for prostate cancer (PCa) at two different academic centres. Methods The PRECISE criteria score the likelihood of clinically significant change over time. The system is a 1-to-5 scale, where 1 or 2 implies regression of a previously visible lesion, 3 denotes stability and 4 or 5 indicates radiological progression. A retrospective analysis of 80 patients (40 from each centre) on AS with a biopsyconfirmed low-or intermediate-risk PCa (i.e. ≤ Gleason 3 + 4 and prostate-specific antigen ≤ 20 ng/ml) and ≥ 2 prostate MR scans was performed. Two blinded radiologists reported all scans independently and scored the likelihood of radiological change (PRECISE score) from the second scan onwards. Cohen's κ coefficients and percent agreement were computed. Results Agreement was substantial both at a per-patient and a per-scan level (κ = 0.71 and 0.61; percent agreement = 79% and 81%, respectively) for each PRECISE score. The agreement was superior (κ = 0.83 and 0.67; percent agreement = 90% and 91%, respectively) when the PRECISE scores were grouped according to the absence/presence of radiological progression (PRECISE 1-3 vs 4-5). Higher inter-reader agreement was observed for the scans performed at University College London (UCL) (κ = 0.81 vs 0.55 on a per-patient level and κ = 0.70 vs 0.48 on a per-scan level, respectively). The discrepancies between institutions were less evident for percent agreement (80% vs 78% and 86% vs 75%, respectively). Conclusions Expert radiologists achieved substantial reproducibility for the PRECISE scoring system, especially when data were pooled together according to the absence/presence of radiological progression (PRECISE 1-3 vs 4-5).

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“…There is compelling evidence that multiparametric magnetic resonance imaging (mpMRI) shows potential in identifying candidates for AS, who may have little benefit from therapy, but still need to be monitored to allow prompt curative treatment if the disease shows signs of becoming harmful (radiological progression) [2][3][4].…”

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confidence: 99%

DWI and PRECISE criteria in men on active surveillance for prostate cancer: A multicentre preliminary experience of different ADC calculations

Giganti

Pecoraro

Fierro

et al. 2020

Magnetic Resonance Imaging

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A B S T R A C TPurpose: The PRECISE score estimates the likelihood of radiological progression in patients on active surveillance (AS) for prostate cancer (PCa) with serial multiparametric magnetic resonance imaging (mpMRI). A PRECISE score of 1 or 2 denotes radiological regression, PRECISE 3 indicates stability and PRECISE 4 or 5 implies progression.We evaluated the inter-reader reproducibility of different apparent diffusion coefficient (ADC) calculations and their relationship to the PRECISE score. Material and methods: Baseline and follow-up scans (on the same MR systems) of 30 patients with visible lesions from two different institutions (University College London and Sapienza University of Rome) were analysed by two radiologists (one from each site). The PRECISE score was initially assessed in consensus. At least six weeks later, to reduce the likelihood of being influenced by the consensus PRECISE reading, each radiologist independently calculated ADC for the following: lesion, non-cancerous tissue and urine in the bladder. Normalised ADC ratios were calculated with respect to normal prostatic tissue (npADC) and urine. Spearman's correlation (ρ), intraclass correlation coefficients (ICC), differences in ADC and ROC curves were computed. Results: Interobserver reproducibility was very good (ρ > 0.8; ICC > 0.90). Lesion ADC (0.91 vs 0.73 × 10 −3 mm 2 /s; p=0.025) and npADC ratio (0.68 vs 0.53; p=0.012) at follow-up mpMRI were different between patients with radiological regression or stability vs progression. Cut-offs of 0.77 × 10 −3 mm 2 /s (lesion ADC) and 0.59 (npADC ratio) could differentiate the two groups (area under the curve: 0.74 and 0.77, respectively). Conclusion: The ADC, npADC ratio and the PRECISE score should be recorded for MRI-based AS.

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MRI in active surveillance: a critical review

Abstract: Although there is an ongoing debate regarding the precise nature of its optimal implementation, mpMRI is a promising risk stratification tool and should be considered for men on AS.

Cited by 43 publications

References 67 publications

Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI

Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI

Interobserver reproducibility of the PRECISE scoring system for prostate MRI on active surveillance: results from a two-centre pilot study

DWI and PRECISE criteria in men on active surveillance for prostate cancer: A multicentre preliminary experience of different ADC calculations

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