The Effect of Including Bone in DIXON-based Attenuation Correction for 18F-fluciclovine PET/MRI of Prostate Cancer

The objective of this study was to evaluate the effect of including bone in DIXON-based attenuation correction for 18F-fluciclovine Positron Emission Tomography (PET) / Magnetic Resonance Imaging (MRI) of primary and recurrent prostate cancer. Methods:18F-fluciclovine PET data from two PET/MRI studies - one for staging of high-risk prostate cancer (28 patients) and one for diagnosis of recurrent prostate cancer (81 patients) - were reconstructed with a 4-compartment (reference) and 5-compartment attenuation map. In the latter, continuous linear attenuation coefficients for bone were included by co-registration with an atlas. The maximum and mean 50% isocontour standardized uptake values (SUVmax and SUViso, respectively) of primary, locally recurrent, and metastatic lesions were compared between the two reconstruction methods using linear mixed-effects models. In addition, mean SUVs were obtained from bone marrow in the third lumbar vertebra (L3) to investigate the effect of including bone attenuation on lesion-to-bone marrow SUV ratios (SUVRmax and SUVRiso; recurrence study only). The 5-compartment attenuation maps were visually compared to the in-phase DIXON MR images for evaluation of bone registration errors near the lesions. P-values < 0.05 were considered significant. Results: Sixty-two (62) lesions from 39 patients were evaluated. Bone registration errors were found near 19 (31%) of these lesions. In the remaining 8 primary prostate tumors, 7 locally recurrent lesions, and 28 lymph node metastases without bone registration errors, using the 5-compartment attenuation map was associated with small but significant increases in SUVmax [2.5%; 95% confidence interval (CI) 2.0%-3.0%; p<0.001] and SUViso (2.5%; 95% CI 1.9%-3.0%; p<0.001), but not SUVRmax (0.2%; 95% CI -0.5%-0.9%; P = 0.604) and SUVRiso (0.2%; 95% CI -0.6%-1.0%; P = 0.581), in comparison to the 4-compartment attenuation map. Conclusion: The investigated method for atlas-based inclusion of bone in 18F-fluciclovine PET/MRI attenuation correction has only a small effect on the SUVs of soft-tissue prostate cancer lesions, and no effect on their lesion-to-bone marrow SUVRs when using signal from L3 as a reference. The attenuation maps should always be checked for registration artefacts for lesions in or close to the bones

Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition

Objectives To develop and evaluate an automated method for prostate T2-weighted (T2W) image normalization using dual-reference (fat and muscle) tissue. Materials and methods Transverse T2W images from the publicly available PROMISE12 (N = 80) and PROSTATEx (N = 202) challenge datasets, and an in-house collected dataset (N = 60) were used. Aggregate channel features object detectors were trained to detect reference fat and muscle tissue regions, which were processed and utilized to normalize the 3D images by linear scaling. Mean prostate pseudo T2 values after normalization were compared to literature values. Inter-patient histogram intersections of voxel intensities in the prostate were compared between our approach, the original images, and other commonly used normalization methods. Healthy vs. malignant tissue classification performance was compared before and after normalization. Results The prostate pseudo T2 values of the three tested datasets (mean ± standard deviation = 78.49 ± 9.42, 79.69 ± 6.34 and 79.29 ± 6.30 ms) corresponded well to T2 values from literature (80 ± 34 ms). Our normalization approach resulted in significantly higher (p < 0.001) inter-patient histogram intersections (median = 0.746) than the original images (median = 0.417) and most other normalization methods. Healthy vs. malignant classification also improved significantly (p < 0.001) in peripheral (AUC 0.826 vs. 0.769) and transition (AUC 0.743 vs. 0.678) zones. Conclusion An automated dual-reference tissue normalization of T2W images could help improve the quantitative assessment of prostate cancer

Multiparametric Prostate MRI in Biopsy-Naïve Men: A Prospective Evaluation of Performance and Biopsy Strategies

Objectives: This study aims to prospectively estimate the diagnostic performance of multiparametric prostate MRI (mpMRI) and compare the detection rates of prostate cancer using cognitive targeted transrectal ultrasound (TRUS) guided biopsies, targeted MR-guided in-bore biopsies (MRGB), or both methods combined in biopsy-naïve men. Methods: The biopsy-naïve men referred for mpMRI (including T2-weighted, diffusion-weighted and dynamic contrast enhanced MRI) due to prostate cancer suspicion (elevated prostate-specific antigen or abnormal digital rectal examination) were eligible for inclusion. The images were scored according to Prostate Imaging Reporting and Data System (PI-RADS) v2, and men with PI-RADS 1–2 lesions were referred for routine systematic TRUS, while those with PI-RADS 3–5 lesions were randomized to MRGB or cognitive targeted TRUS. Men randomized to MRGB were referred to a secondary TRUS 2 weeks after MRGB. Gleason grade group ≥2 was defined as clinically significant prostate cancer. The performance of mpMRI was estimated using prostate cancer detected by any biopsy method as the reference test. Results: A total of 210 men were included. There was no suspicion of prostate cancer after mpMRI (PI-RADS 1–2) in 48% of the men. Among these, significant and insignificant prostate cancer was diagnosed in five and 11 men, respectively. Thirty-five men who scored as PI-RADS 1–2 did not undergo biopsy and were therefore excluded from the calculation of diagnostic accuracy. The overall sensitivity, specificity, negative predictive value, and positive predictive value of mpMRI for the detection of significant prostate cancer were 0.94, 0.63, 0.92, and 0.67, respectively. In patients with PI-RADS 3–5 lesions, the detection rates for significant prostate cancer were not significantly different between cognitive targeted TRUS (68.4%), MRGB (57.7%), and the combination of the two biopsy methods (64.4%). The median numbers of biopsy cores taken per patient undergoing systematic TRUS, cognitive targeted TRUS, and MRGB were 14 [8-16], 12 [6-17], and 2 [1-4] respectively. Conclusions: mpMRI, in a cohort of biopsy-naïve men, has high negative predictive value, and our results support that it is safe to avoid biopsy after negative mpMRI. Furthermore, MRGB provides a similar diagnosis to the cognitive targeted TRUS but with fewer biopsies

Prostate-Specific Membrane Antigen PET/Magnetic Resonance Imaging for the Planning of Salvage Radiotherapy in Patients with Prostate Cancer with Biochemical Recurrence After Radical Prostatectomy

Prostate-specific membrane antigen (PSMA) PET/magnetic resonance (MR) imaging can help distinguish between patients with prostate cancer with locoregional recurrence and those with distant metastases, even at low prostate-specific antigen levels. PSMA PET/MR imaging may have advantages compared with PET/computed tomography for the detection of local recurrence and anatomic correlates to PET-positive lymph node and bone lesions. PSMA PET/MR imaging can help in making informed treatment decisions in patients with biochemical recurrence after radical prostatectomy. PSMA PET/MR imaging enables dose-escalated and metastases-directed salvage radiotherapy in patients with biochemical recurrence after radical prostatectomy

Utility of T2-weighted MRI texture analysis in assessment of peripheral zone prostate cancer aggressiveness: a single-arm, multicenter study

T2-weighted (T2W) MRI provides high spatial resolution and tissue-specific contrast, but it is predominantly used for qualitative evaluation of prostate anatomy and anomalies. This retrospective multicenter study evaluated the potential of T2W image-derived textural features for quantitative assessment of peripheral zone prostate cancer (PCa) aggressiveness. A standardized preoperative multiparametric MRI was performed on 87 PCa patients across 6 institutions. T2W intensity and apparent diffusion coefficient (ADC) histogram, and T2W textural features were computed from tumor volumes annotated based on whole-mount histology. Spearman correlations were used to evaluate association between textural features and PCa grade groups (i.e. 1–5). Feature utility in differentiating and classifying low-(grade group 1) vs. intermediate/high-(grade group ≥ 2) aggressive cancers was evaluated using Mann–Whitney U-tests, and a support vector machine classifier employing “hold-one-institution-out” cross-validation scheme, respectively. Textural features indicating image homogeneity and disorder/complexity correlated significantly (p < 0.05) with PCa grade groups. In the intermediate/high-aggressive cancers, textural homogeneity and disorder/complexity were significantly lower and higher, respectively, compared to the low-aggressive cancers. The mean classification accuracy across the centers was highest for the combined ADC and T2W intensity-textural features (84%) compared to ADC histogram (75%), T2W histogram (72%), T2W textural (72%) features alone or T2W histogram and texture (77%), T2W and ADC histogram (79%) combined. Texture analysis of T2W images provides quantitative information or features that are associated with peripheral zone PCa aggressiveness and can augment their classification

The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images

Volume of interest segmentation is an essential step in computer-aided detection and diagnosis (CAD) systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the reproducibility of these methods. In this work, an in-house collected dataset from 244 patients was used to investigate the intra-patient reproducibility of 14 shape features for DL-based segmentation methods of the whole prostate gland (WP), peripheral zone (PZ), and the remaining prostate zones (non-PZ) on T2-weighted (T2W) magnetic resonance (MR) images compared to manual segmentations. The DL-based segmentation was performed using three different convolutional neural networks (CNNs): V-Net, nnU-Net-2D, and nnU-Net-3D. The two-way random, single score intra-class correlation coefficient (ICC) was used to measure the inter-scan reproducibility of each feature for each CNN and the manual segmentation. We found that the reproducibility of the investigated methods is comparable to manual for all CNNs (14/14 features), except for V-Net in PZ (7/14 features). The ICC score for segmentation volume was found to be 0.888, 0.607, 0.819, and 0.903 in PZ; 0.988, 0.967, 0.986, and 0.983 in non-PZ; 0.982, 0.975, 0.973, and 0.984 in WP for manual, V-Net, nnU-Net-2D, and nnU-Net-3D, respectively. The results of this work show the feasibility of embedding DL-based segmentation in CAD systems, based on multiple T2W MR scans of the prostate, which is an important step towards the clinical implementation

Relative Enhanced Diffusivity in Prostate Cancer: Protocol Optimization and Diagnostic Potential

Background Relative enhanced diffusivity (RED) is a potential biomarker for indirectly measuring perfusion in tissue using diffusion‐weighted magnetic resonance imaging (MRI) with 3 b values. Purpose To optimize the RED MRI protocol for the prostate, and to investigate its potential for prostate cancer (PCa) diagnosis. Study Type Prospective. Population Ten asymptomatic healthy volunteers and 35 patients with clinical suspicion of PCa. Sequence 3T T2‐ and diffusion‐weighted MRI with b values: b = 0, 50, [100], 150, [200], 250, [300], 400, 800 s/mm2 (values in brackets were only used for patients). Assessment Monte Carlo simulations were performed to assess noise sensitivity of RED as a function of intermediate b value. Volunteers were scanned 3 times to assess repeatability of RED. Patient data were used to investigate RED's potential for discriminating between biopsy‐confirmed cancer and healthy tissue, and between true and false positive radiological findings. Statistical Tests Within‐subject coefficient of variation (WCV) to assess repeatability and receiver‐operating characteristic curve analysis and logistic regression to assess diagnostic performance of RED. Results The repeatability was acceptable (WCV = 0.2‐0.3) for all intermediate b values tested, apart from b = 50 s/mm2 (WCV = 0.3‐0.4). The simulated RED values agreed well with the experimental data, showing that an intermediate b value between 150‐250 s/mm2 minimizes noise sensitivity in both peripheral zone (PZ) and transition zone (TZ). RED calculated with the b values 0, 150 and 800 s/mm2 was significantly higher in tumors than in healthy tissue in both PZ (P < 0.001, area under the curve [AUC] = 0.85) and PZ + TZ (P < 0.001, AUC = 0.84). RED was shown to aid apparent diffusion coefficient (ADC) in differentiating between false‐positive findings and true‐positive PCa in the PZ (AUC; RED = 0.71, ADC = 0.74, RED+ADC = 0.77). Data Conclusion RED is a repeatable biomarker that may have value for prostate cancer diagnosis. An intermediate b value in the range of 150‐250 s/mm2 minimizes the influence of noise and maximizes repeatability. Level of Evidence: 2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019

Tissue microstructure is linked to MRI parameters and metabolite levels in prostate cancer

Introduction: Magnetic resonance imaging (MRI) can portray spatial variations in tumor heterogeneity, architecture, and its microenvironment in a non-destructive way. The objective of this study was to assess the relationship between MRI parameters measured on patients in vivo, individual metabolites measured in prostatectomy tissue ex vivo, and quantitative histopathology. Materials and methods: Fresh frozen tissue samples (n = 53 from 15 patients) were extracted from transversal prostate slices and linked to in vivo MR images, allowing spatially matching of ex vivo measured metabolites with in vivo MR parameters. Color-based segmentation of cryosections of each tissue sample was used to identify luminal space, stroma, and nuclei. Results: Cancer samples have significantly lower area percentage of lumen and higher area percentage of nuclei than non-cancer samples (p ≤ 0.001). Apparent diffusion coefficient is significantly correlated with percentage area of lumen (ρ = 0.6, p < 0.001) and percentage area of nuclei (ρ = −0.35, p = 0.01). There is a positive correlation (ρ = 0.31, p = 0.053) between citrate and percentage area of lumen. Choline is negatively correlated with lumen (ρ = −0.38, p = 0.02) and positively correlated with percentage area of nuclei (ρ = 0.38, p = 0.02). Conclusion: Microstructures that are observed by histopathology are linked to MR characteristics and metabolite levels observed in prostate cancer

A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI

Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations

A PET/MRI study towards finding the optimal [18F]Fluciclovine PET protocol for detection and characterisation of primary prostate cancer.

Purpose: [18F]Fluciclovine PET imaging shows promise for the assessment of prostate cancer. The purpose of this PET/MRI study is to optimise the PET imaging protocol for detection and characterisation of primary prostate cancer, by quantitative evaluation of the dynamic uptake of [18F]Fluciclovine in cancerous and benign tissue. Methods: Patients diagnosed with high-risk primary prostate cancer underwent an integrated [18F]Fluciclovine PET/MRI exam before robot-assisted radical prostatectomy with extended pelvic lymph node dissection. Volumes-of-interest (VOIs) of selected organs (prostate, bladder, blood pool) and sub-glandular prostate structures (tumour, benign prostatic hyperplasia (BPH), inflammation, healthy tissue) were delineated on T2-weighted MR images, using whole-mount histology samples as a reference. Three candidate windows for optimal PET imaging were identified based on the dynamic curves of the mean and maximum standardised uptake value (SUVmean and SUVmax, respectively). The statistical significance of differences in SUV between VOIs were analysed using Wilcoxon rank sum tests (p<0.05, adjusted for multiple testing). Results: Twenty-eight (28) patients [median (range) age: 66 (55-72) years] were included. An early (W1: 5-10 minutes post-injection) and two late candidate windows (W2: 18-23; W3: 33-38 minutes post-injection) were selected. Late compared with early imaging was better able to distinguish between malignant and benign tissue [W3, SUVmean: tumour vs. BPH 2.5 vs. 2.0 (p<0.001), tumour vs. inflammation 2.5 vs. 1.7 (p<0.001), tumour vs. healthy tissue 2.5 vs. 2.0 (p<0.001); W1, SUVmean: tumour vs. BPH 3.1 vs. 3.1 (p=0.771), tumour vs inflammation 3.1 vs. 2.2 (p=0.021), tumour vs. healthy tissue 3.1 vs. 2.5 (p<0.001)] as well as between high-grade and low/intermediate-grade tumours (W3, SUVmean: 2.6 vs. 2.1 (p=0.040); W1, SUVmean: 3.1 vs. 2.8 (p=0.173)). These differences were relevant to the peripheral zone, but not the central gland. Conclusion: Late-window [18F]Fluciclovine PET imaging shows promise for distinguishing between prostate tumours and benign tissue and for assessment of tumour aggressiveness