Towards a universal MRI atlas of the prostate and prostate zones

Padgett, Kyle R.; Swallen, Amy; Pirozzi, Sara; Piper, Jon; Chinea, Felix M.; Abramowitz, Matthew C.; Nelson, Aaron; Pollack, Alan; Stoyanova, Radka

doi:10.1007/s00066-018-1348-5

Cited by 11 publications

(4 citation statements)

References 43 publications

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“…Advanced software providing the registration with minimal deformation of image scans and landmarks should be analysed in further studies. Despite some diagnostic limitations, the mpMRI was recommended in several guidelines as staging method as well as method for targeted biopsy in the diagnostic of primary PCa [26][27][28][39][40][41][42][43][44]. In agreement with other studies, both manual and automatic contouring using PSMA-PET techniques showed more accuracy compared to mpMRI for localizing IPLs when correlated with reference biopsy results [4][5][6][7][8].…”

Section: Discussionsupporting

confidence: 60%

Diagnostic efficiency of hybrid imaging using PSMA ligands, PET/CT, PET/MRI and MRI in identifying malignant prostate lesions

et al. 2021

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Objective The objective of this study was to assess the accuracy of 68Ga-PSMA-11 PET/MRI, 18F-PSMA-1007 PET/CT, 68Ga-PSMA-11 PET/CT, and multiparametric (mp)MRI for the delineating of dominant intraprostatic lesions (IPL). Materials and methods 35 patients with organ-confined prostate cancer who were assigned to definitive radiotherapy (RT) were divided into three groups based on imaging techniques: 68Ga-PSMA-PET/MRI (n = 9), 18F-PSMA-PET/CT (n = 16) and 68Ga-PSMA-PET/CT (n = 10). All patients without PSMA-PET/MRI received an additional mpMRI. PSMA-PET-based automatic isocontours and manual contours of the dominant IPLs were generated for each modality. The biopsy results were then used to validate whether any of the prostate biopsies were positive in the marked lesion using Dice similarity coefficient (DSC), Youden index (YI), sensitivity and specificity. Factors that can predict the accuracy of IPLs contouring were analysed. Results Diagnostic performance was significantly superior both for manual and automatic IPLs contouring using 68Ga-PSMA-PET/MRI (DSC/YI SUV70%—0.62/0.51), 18F-PSMA-PET/CT (DSC/YI SUV70%—0.67/0.53) or 68Ga-PSMA-PET/CT (DSC/YI SUV70%—0.63/0.51) compared to mpMRI (DSC/YI—0.47/0.41; p < 0.001). The accuracy for delineating IPLs was not improved by combination of PET/CT and mpMRI images compared to PET/CT alone. Significantly superior diagnostic accuracy was found for large prostate lesions (at least 15% from the prostate volume) and higher Gleason score (at least 7b) comparing to smaller lesions with lower GS. Conclusion IPL localization was significantly improved when using PSMA-imaging procedures compared to mpMRI. No significant difference for delineating IPLs was found between hybrid method PSMA-PET/MRI and PSMA-PET/CT. PSMA-based imaging technique should be considered for the diagnostics of IPLs and focal treatment modality.

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Section: Discussionsupporting

confidence: 60%

Diagnostic efficiency of hybrid imaging using PSMA ligands, PET/CT, PET/MRI and MRI in identifying malignant prostate lesions

et al. 2021

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“…To our knowledge, two studies have evaluated the inter-reader variability of the zonal anatomy [ 16 , 21 ]; Becker et al [ 16 ] found in a multi-reader study (2 experts radiologists, 2 residents, and 2 computer vision scientists), in a cohort of 80 patients using a 3.0 T MRI and endo-rectal coil, a DSC of 0.733 for the WG and a higher variability for the TZ (DSC 0.738), in the apex (2D DSC 0.85) and basal part of the gland (2D DSC 0.87). Padgett et al [ 22 ] in a multi-reader study ( n = 2) of zonal segmentation on 2D T2W images obtained on 3 T of 30 consecutive patients found for the WG a DSC of 0.88 ± 0.04 and 0.81 ± 0.1 for the TZ.…”

Section: Discussionmentioning

confidence: 99%

Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology

Montagne

Hamzaoui²,

Allera

et al. 2021

Insights Imaging

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Background Accurate prostate zonal segmentation on magnetic resonance images (MRI) is a critical prerequisite for automated prostate cancer detection. We aimed to assess the variability of manual prostate zonal segmentation by radiologists on T2-weighted (T2W) images, and to study factors that may influence it. Methods Seven radiologists of varying levels of experience segmented the whole prostate gland (WG) and the transition zone (TZ) on 40 axial T2W prostate MRI images (3D T2W images for all patients, and both 3D and 2D images for a subgroup of 12 patients). Segmentation variabilities were evaluated based on: anatomical and morphological variation of the prostate (volume, retro-urethral lobe, intensity contrast between zones, presence of a PI-RADS ≥ 3 lesion), variation in image acquisition (3D vs 2D T2W images), and reader’s experience. Several metrics including Dice Score (DSC) and Hausdorff Distance were used to evaluate differences, with both a pairwise and a consensus (STAPLE reference) comparison. Results DSC was 0.92 (± 0.02) and 0.94 (± 0.03) for WG, 0.88 (± 0.05) and 0.91 (± 0.05) for TZ respectively with pairwise comparison and consensus reference. Variability was significantly (p < 0.05) lower for the mid-gland (DSC 0.95 (± 0.02)), higher for the apex (0.90 (± 0.06)) and the base (0.87 (± 0.06)), and higher for smaller prostates (p < 0.001) and when contrast between zones was low (p < 0.05). Impact of the other studied factors was non-significant. Conclusions Variability is higher in the extreme parts of the gland, is influenced by changes in prostate morphology (volume, zone intensity ratio), and is relatively unaffected by the radiologist’s level of expertise.

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“…T2 harmonization between different scanners is a recognized challenge using the T2W sequence and this is emphasized, particularly when conducting multicenter studies. 24,30 As with our previous work, 36,41 the proposed normalization was developed and tested in a very heterogeneous dataset (Tables 1 and 2).…”

Section: Discussionmentioning

confidence: 99%

Uncovering prostate cancer aggressiveness signal in T2‐weighted MRI through a three‐reference tissues normalization technique

Algohary,

Zacharaki,

Breto

et al. 2023

NMR in Biomedicine

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Quantitative T2‐weighted MRI (T2W) interpretation is impeded by the variability of acquisition‐related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning‐based network utilizing MASK R‐CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities. The T2W distributions in the prostate cancer regions of interest (ROIs) and normal appearing prostate tissue (NAT) were compared before and after normalization using Student's t‐test. The ROIs' T2W associations with the Gleason Score (GS), Decipher genomic score, and a three‐tier prostate cancer risk were evaluated with Spearman’s correlation coefficient (rS). T2W differences in indolent and aggressive prostate cancer lesions were also assessed. The MASK R‐CNN was trained with manual contours from 32 patients. The normalization procedure was applied to an independent MRI dataset from 83 patients. T2W differences between ROIs and NAT significantly increased after normalization. T2W intensities in 231 biopsy ROIs were significantly negatively correlated with GS (rS = −0.21, p = 0.001), Decipher (rS = −0.193, p = 0.003), and three‐tier risk (rS = −0.235, p < 0.001). The average T2W intensities in the aggressive ROIs were significantly lower than in the indolent ROIs after normalization. In conclusion, the automated triple‐reference tissue normalization method significantly improved the discrimination between prostate cancer and normal prostate tissue. In addition, the normalized T2W intensities of cancer exhibited a significant association with tumor aggressiveness. By improving the quantitative utilization of the T2W in the assessment of prostate cancer on MRI, the new normalization method represents an important advance over clinical protocols that do not include sequences for the measurement of T2 relaxation times.

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Towards a universal MRI atlas of the prostate and prostate zones

Cited by 11 publications

References 43 publications

Diagnostic efficiency of hybrid imaging using PSMA ligands, PET/CT, PET/MRI and MRI in identifying malignant prostate lesions

Diagnostic efficiency of hybrid imaging using PSMA ligands, PET/CT, PET/MRI and MRI in identifying malignant prostate lesions

Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology

Uncovering prostate cancer aggressiveness signal in T2‐weighted MRI through a three‐reference tissues normalization technique

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