18F-Choline PET/mpMRI for Detection of Clinically Significant Prostate Cancer: Part 1. Improved Risk Stratification for MRI-Guided Transrectal Prostate Biopsies.
ABSTRACT: A prospective single-arm clinical trial was conducted to determine whether 18F-choline PET/mpMRI can improve the specificity of multiparametric MRI (mpMRI) of the prostate for Gleason ? 3+4 prostate cancer. Methods: Before targeted and systematic prostate biopsy, mpMRI and 18F-choline PET/CT were performed on 56 evaluable subjects with 90 Likert score 3-5 mpMRI target lesions, using a 18F-choline target-to-background ratio of greater than 1.58 to indicate a positive 18F-choline result. Prostate biopsies were performed after registration of real-time transrectal ultrasound with T2-weighted MRI. A mixed-effects logistic regression was applied to measure the performance of mpMRI (based on prospective Likert and retrospective Prostate Imaging Reporting and Data System, version 2 [PI-RADS], scores) compared with 18F-choline PET/mpMRI to detect Gleason ? 3+4 cancer. Results: The per-lesion accuracy of systematic plus targeted biopsy for mpMRI alone was 67.8% (area under receiver-operating-characteristic curve [AUC], 0.73) for Likert 4-5 and 70.0% (AUC, 0.76) for PI-RADS 3-5. Several PET/MRI models incorporating 18F-choline with mpMRI data were investigated. The most promising model selected all high-risk disease on mpMRI (Likert 5 or PI-RADS 5) plus low- and intermediate-risk disease (Likert 4 or PI-RADS 3-4), with an elevated 18F-choline target-to-background ratio greater than 1.58 as positive for significant cancer. Using this approach, the accuracy on a per-lesion basis significantly improved to 88.9% for Likert (AUC, 0.90; P < 0.001) and 91.1% for PI-RADS (AUC, 0.92; P < 0.001). On a per-patient basis, the accuracy improved to 92.9% for Likert (AUC, 0.93; P < 0.001) and to 91.1% for PI-RADS (AUC, 0.91; P = 0.009). Conclusion: 18F-choline PET/mpMRI improved the identification of Gleason ? 3+4 prostate cancer compared with mpMRI, with the principal effect being improved risk stratification of intermediate-risk mpMRI lesions.
Project description:The objective of this study was to evaluate the cost-effectiveness of 18F-choline PET/multiparametric MRI (mpMRI) versus mpMRI alone for the detection of primary prostate cancer with a Gleason score of greater than or equal to 3 + 4 in men with elevated prostate-specific antigen levels. Methods: A Markov model of prostate cancer onset and progression was used to estimate the health and economic consequences of 18F-choline PET/mpMRI for the detection of primary prostate cancer with a Gleason score of greater than or equal to 3 + 4 in men with elevated prostate-specific antigen levels. Multiple simultaneous hybrid 18F-choline PET/mpMRI strategies were evaluated using Likert or Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) scoring; the first was biopsy for Likert 5 mpMRI lesions or Likert 3-4 lesions with 18F-choline target-to-background ratios of greater than or equal to 1.58, and the second was biopsy for PI-RADSv2 5 mpMRI lesions or PI-RADSv2 3-4 mpMRI lesions with 18F-choline target-to-background ratios of greater than or equal to 1.58. These strategies were compared with universal standard biopsy, mpMRI alone with biopsy only for PI-RADSv2 3-5 lesions, and mpMRI alone with biopsy only for Likert 4-5 lesions. For each mpMRI strategy, either no biopsy or standard biopsy could be performed after negative mpMRI results were obtained. Deaths averted, quality-adjusted life years (QALYs), cost, and incremental cost-effectiveness ratios were estimated for each strategy. Results: When the results of 18F-choline PET/mpMRI were negative, performing a standard biopsy was more expensive and had lower QALYs than performing no biopsy. The best screening strategy among those considered in this study performed hybrid 18F-choline PET/mpMRI with Likert scoring on men with elevated PSA, performed combined biopsy (targeted biopsy and standard 12-core biopsy) for men with positive imaging results, and no biopsy for men with negative imaging results ($22,706/QALY gained relative to mpMRI alone); this strategy reduced the number of biopsies by 35% in comparison to mpMRI alone. When the same policies were compared using PI-RADSv2 instead of Likert scoring, hybrid 18F-choline PET/mpMRI cost $46,867/QALY gained relative to mpMRI alone. In a threshold analysis, the best strategy among those considered remained cost-effective when the sensitivity and specificity of PET/mpMRI and combined biopsy (targeted biopsy and standard 12-core biopsy) were simultaneously reduced by 20 percentage points. Conclusion: 18F-choline PET/mpMRI for the detection of primary prostate cancer with a Gleason score of greater than or equal to 3 + 4 is cost-effective and can reduce the number of unneeded biopsies in comparison to mpMRI alone.
Project description:BACKGROUND:The study aims to assess the accuracy of multi-parametric prostate MRI (mpMRI) and 18F-choline PET/CT in tumor segmentation for clinically significant prostate cancer. 18F-choline PET/CT and 3 T mpMRI were performed in 10 prospective subjects prior to prostatectomy. All subjects had a single biopsy-confirmed focus of Gleason ??3+4 cancer. Two radiologists (readers 1 and 2) determined tumor boundaries based on in vivo mpMRI sequences, with clinical and pathologic data available. 18F-choline PET data were co-registered to T2-weighted 3D sequences and a semi-automatic segmentation routine was used to define tumor volumes. Registration of whole-mount surgical pathology to in vivo imaging was conducted utilizing two ex vivo prostate specimen MRIs, followed by gross sectioning of the specimens within a custom-made 3D-printed plastic mold. Overlap and similarity coefficients of manual segmentations (seg1, seg2) and 18F-choline-based segmented lesions (seg3) were compared to the pathologic reference standard. RESULTS:All segmentation methods greatly underestimated the true tumor volumes. Human readers (seg1, seg2) and the PET-based segmentation (seg3) underestimated an average of 79, 80, and 58% of the tumor volumes, respectively. Combining segmentation volumes (union of seg1, seg2, seg3?=?seg4) decreased the mean underestimated tumor volume to 42% of the true tumor volume. When using the combined segmentation with 5 mm contour expansion, the mean underestimated tumor volume was significantly reduced to 0.03?±?0.05 mL (2.04?±?2.84%). Substantial safety margins up to 11-15 mm were needed to include all tumors when the initial segmentation boundaries were drawn by human readers or the semi-automated 18F-choline segmentation tool. Combining MR-based human segmentations with the metabolic information based on 18F-choline PET reduced the necessary safety margin to a maximum of 9 mm to cover all tumors entirely. CONCLUSIONS:To improve the outcome of focal therapies for significant prostate cancer, it is imperative to recognize the full extent of the underestimation of tumor volumes by mpMRI. Combining metabolic information from 18F-choline with MRI-based segmentation can improve tumor coverage. However, this approach requires confirmation in further clinical studies.
Project description:The role of dynamic contrast-enhanced-MRI (DCE-MRI) for Prostate Imaging-Reporting and Data System (PI-RADS) scoring is a controversial topic. In this retrospective study, we aimed to measure the added value of DCE-MRI in combination with T2-weighted (T2W) and diffusion-weighted imaging (DWI) using PI-RADS v2.1, in terms of reproducibility and diagnostic accuracy, for detection of prostate cancer (PCa) and clinically significant PCa (CS-PCa, for Gleason Score ? 7). 117 lesions in 111 patients were identified as suspicion by multiparametric MRI (mpMRI) and addressed for biopsy. Three experienced readers independently assessed PI-RADS score, first using biparametric MRI (bpMRI, including DWI and T2W), and then multiparametric MRI (also including DCE). The inter-rater and inter-method agreement (bpMRI- vs. mpMRI-based scores) were assessed by Cohen's kappa (?). Receiver operating characteristics (ROC) analysis was performed to evaluate the diagnostic accuracy for PCa and CS-PCa detection among the two scores. Inter-rater agreement was excellent for the three pairs of readers (? ? 0.83), while the inter-method agreement was good (? ? 0.73). Areas under the ROC curve (AUC) showed similar high-values (0.8 ? AUC ? 0.85). The reproducibility of PI-RADS v2.1 scoring was comparable and high among readers, without relevant differences, depending on the MRI protocol used. The inclusion of DCE did not influence the diagnostic accuracy.
Project description:Background:In order to improve postoperative functional outcome, including urinary continence and erectile function, nerve sparing surgery is recommended for patients with clinically localized prostate cancer (PCa). However, due to poor diagnosis accuracy at the preoperative stage, upstaging occurs in a considerable proportion of patients. Multiparametric magnetic resonance imaging (mpMRI) and the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) have recently shown excellent performance in diagnosis and staging of PCa. The aim of this study was to develop a predictive model based on PI-RADS v2 for postoperative upstaging in patients with low-intermediate risk PCa. Methods:The medical records of 314 patients with low-intermediate risk PCa [prostate-specific antigen (PSA) level ?20 ng/mL, Gleason score (GS) <8, and clinical stage < T3] who underwent preoperative mpMRI and radical prostatectomy in the Department of Urology, Peking University First Hospital between January 2012 and July 2019 were reviewed retrospectively. Clinicopathological characteristics were collected. All MRI reports were done at our institution as part of routine clinical practice before prostate biopsy and there was no re-reporting occurred. Using PI-RADS v2, the mpMRI results were assigned to three groups: "negative", "suspicious", and "positive". Multivariate logistic regression analysis was used to assess factors associated with postoperative pathological upstaging, defined as the presence of pT3 at final pathology. A regression coef?cient based model for predicting postoperative upstaging was constructed and internally validated using 1,000 bootstrap resamples. The performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). With the optimal cutoff point the performance of the model was assessed through analysis of sensitivity, specificity, positive predictive value, and negative predictive value. Results:Upstaging was observed in 119 (37.9%) patients. The univariate and multivariate analyses revealed that PSA density, biopsy Gleason grade group (GGG), and mpMRI findings were significantly independent predictors for postoperative upstaging (all P<0.05). A predictive model showing very favorable calibration characteristics and higher accuracy than the single variables was constructed (AUC =0.74; P<0.001). At the optimal cutoff point, the model demonstrated a sensitivity and negative predictive value of 87.4% and 87.0%, respectively. Conclusions:PI-RADS v2 assessment proved to be one of the most valuable predictors for postoperative upstaging in patients with low-intermediate risk PCa. The predictive model, based on PI-RADS v2 assessment, PSA density, and biopsy GGG, may help to select suitable candidates for nerve sparing radical prostatectomy among patients with low-intermediate risk PCa.
Project description:Multiple scoring systems have been proposed for prostate MRI reporting. We sought to review the clinical impact of the new Prostate Imaging Reporting and Data System v2 (PI-RADS) and compare those results to our proposed Simplified Qualitative System (SQS) score with respect to detection of prostate cancers and clinically significant prostate cancers.All patients who underwent multiparametric prostate MRI (mpMRI) had their images interpreted using PI-RADS v1 and SQS score. PI-RADS v2 was calculated from prospectively collected data points. Patients with positive mpMRIs were then referred by their urologists for enrollment in an IRB-approved prospective phase III trial of mpMRI-Ultrasound (MR/TRUS) fusion biopsy of suspicious lesions. Standard 12-core biopsy was performed at the same setting. Clinical data were collected prospectively.1060 patients were imaged using mpMRI at our institution during the study period. 341 participants were then referred to the trial. 312 participants underwent MR/TRUS fusion biopsy of 452 lesions and were included in the analysis. 202 participants had biopsy-proven cancer (64.7%) and 206 (45.6%) lesions were positive for cancer. Distribution of cancer detected at each score produced a Gaussian distribution for SQS while PI-RADS demonstrates a negatively skewed curve with 82.1% of cases being scored as a 4 or 5. Patient-level data demonstrated AUC of 0.702 (95% CI 0.65 to 0.73) for PI-RADS and 0.762 (95% CI 0.72 to 0.81) for SQS (p< 0.0001) with respect to the detection of prostate cancer. The analysis for clinically significant prostate cancer at a per lesion level resulted in an AUC of 0.725 (95% CI 0.69 to 0.76) and 0.829 (95% CI 0.79 to 0.87) for the PI-RADS and SQS score, respectively (p< 0.0001).mpMRI is a useful tool in the workup of patients at risk for prostate cancer, and serves as a platform to guide further evaluation with MR/TRUS fusion biopsy. SQS score provided a more normal distribution of scores and yielded a higher AUC than PI-RADS v2. However until our findings are validated, we recommend reporting of detailed sequence-specific findings. This will allow for prospectively collected data to be utilized in determining the impact of ongoing changes to these scoring systems as our understanding of mpMRI interpretation evolves.
Project description:We assessed the value of fusion (18)F-fluoromethylcholine ((18)F-choline) PET/MRI for image-guided (targeted) prostate biopsies to detect significant prostate cancer (Gleason ? 3 + 4) compared with standard (systematic 12-core) biopsies.Within an ongoing prospective clinical trial, hybrid (18)F-choline PET/CT and multiparametric 3T MRI (mpMRI) of the pelvis were performed in 36 subjects with a rising prostate-specific antigen for known (n = 15) or suspected (n = 21) prostate cancer before a prostate biopsy procedure. PET and T2-weighted MR volumes of the prostate were spatially registered using commercially available software. Biopsy targets were selected on the basis of visual appearance on MRI and graded as low, intermediate, or high risk for significant disease. Volumes of interest were defined for MR-identified lesions. (18)F-choline uptake measures were obtained from the MR target and a mirrored background volume of interest. The biopsy procedure was performed after registration of real-time transrectal ultrasound with T2-weighted MR and included image-guided cores plus standard cores. Histologic results were determined from standard and targeted biopsy cores as well as prostatectomy specimens (n = 10).Fifteen subjects were ultimately identified with Gleason ? 3 + 4 prostate cancer, of which targeted biopsy identified significantly more (n = 12) than standard biopsies (n = 5; P = 0.002). A total of 52 lesions were identified by mpMRI (19 low, 18 intermediate, 15 high risk), and mpMRI-assigned risk was a strong predictor of final pathology (area under the curve = 0.81; P < 0.001). When the mean (18)F-choline target-to-background ratio was used, the addition of (18)F-choline to mpMRI significantly improved the prediction of Gleason ? 3 + 4 cancers over mpMRI alone (area under the curve = 0.92; P < 0.001).Fusion PET/MRI transrectal ultrasound image registration for targeted prostate biopsies is clinically feasible and accurate. The addition of (18)F-choline PET to mpMRI improves the identification of significant prostate cancer.
Project description:INTRODUCTION:Objective of our study was to determine the agreement between version 1 (v1) and v2 of the Prostate Imaging Reporting and Data System (PI-RADS) for evaluation of multiparametric prostate MRI (mpMRI) and to compare their diagnostic accuracy, their inter-observer agreement and practicability. MATERIAL AND METHODS:mpMRI including T2-weighted imaging, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced imaging (DCE) of 54 consecutive patients, who subsequently underwent MRI-guided in-bore biopsy were re-analyzed according to PI-RADS v1 and v2 by two independent readers. Diagnostic accuracy for detection of prostate cancer (PCa) was assessed using ROC-curve analysis. Agreement between PI-RADS versions and observers was calculated and the time needed for scoring was determined. RESULTS:MRI-guided biopsy revealed PCa in 31 patients. Diagnostic accuracy for detection of PCa was equivalent with both PI-RADS versions for reader 1 with sensitivities and specificities of 84%/91% (AUC = 0.91 95%CI[0.8-1]) for PI-RADS v1 and 100%/74% (AUC = 0.92 95% CI[0.8-1]) for PI-RADS v2. Reader 2 achieved similar diagnostic accuracy with sensitivity and specificity of 74%/91% (AUC = 0.88 95%CI[0.8-1]) for PI-RADS v1 and 81%/91% (AUC = 0.91 95%CI[0.8-1]) for PI-RADS v2. Agreement between scores determined with different PI-RADS versions was good (reader 1: ? = 0.62, reader 2: ? = 0.64). Inter-observer agreement was moderate with PI-RADS v2 (? = 0.56) and fair with v1 (? = 0.39). The time required for building the PI-RADS score was significantly lower with PI-RADS v2 compared to v1 (24.7±2.3 s vs. 41.9±2.6 s, p<0.001). CONCLUSION:Agreement between PI-RADS versions was high and both versions revealed high diagnostic accuracy for detection of PCa. Due to better inter-observer agreement for malignant lesions and less time demand, the new PI-RADS version could be more practicable for clinical routine.
Project description:OBJECTIVE:Prostate dose painting radiotherapy requires the accurate identification of dominant intraprostatic lesions (DILs) to be used as boost volumes; these can be identified on multiparametric MRI (mpMRI) or choline positron emission tomography (PET)/CT. Planning scans are usually performed after 2-3 months of androgen deprivation therapy (ADT). We examine the effect of ADT on choline tracer uptake and boost volumes identified on choline PET/CT. METHODS:Fluoroethylcholine (18F choline) PET/CT was performed for dose painting radiotherapy planning in patients with intermediate- to high-risk prostate cancer. Initially, they were performed at planning. Owing to low visual tracer uptake, PET/CT for subsequent patients was performed at staging. We compared these two approaches on intraprostatic lesions obtained on PET using both visual and automatic threshold methods [prostate maximum standardized uptake value (SUVmax) 60%] when compared with mpMRI. RESULTS:PET/CT was performed during ADT in 11 patients (median duration of 85 days) and before ADT in 29 patients. ADT significantly reduced overall prostate volume by 17%. During ADT, prostate SUVmax was lower although it did not reach statistical significance (4.2 vs 6.6, p?=?0.06); three patients had no visually identifiable PET DIL; and visually defined PET DILs were significantly smaller than corresponding mpMRI DILs (p?=?0.03). However, all patients scanned before ADT had at least one visually identifiable PET DIL, with no significant size difference between MRI and visually defined PET DILs. In both groups, threshold PET produced larger DILs than visual PET. Both PET methods have moderate sensitivity (0.50-0.68) and high specificity (0.85-0.98) for identifying MRI-defined disease. CONCLUSION:For visual contouring of boost volumes in prostate dose painting radiotherapy, 18F choline PET/CT should be performed before ADT. For threshold contouring of boost volumes using our PET/CT scanning protocol, threshold levels of above 60% prostate SUVmax may be more suitable. Additional use of PET with MRI for radiotherapy planning can significantly change the overall boost volumes compared with using MRI alone. Advances in knowledge: For prostate dose painting radiotherapy, the additional use of 18F choline PET with MRI can significantly change the overall boost volumes, and PET should be performed before hormone therapy, especially if boost volumes are visually identified.
Project description:CONTEXT:The Prostate Imaging-Reporting and Data System (PI-RADS) v2 analysis system for multiparametric magnetic resonance imaging (mpMRI) detection of prostate cancer (PCa) is based on PI-RADS v1, accumulated scientific evidence, and expert consensus opinion. OBJECTIVE:To summarize the accuracy, strengths and weaknesses of PI-RADS v2, discuss pathway implications of its use and outline opportunities for improvements and future developments. EVIDENCE ACQUISITION:For this consensus expert opinion from the PI-RADS steering committee, clinical studies, systematic reviews, and professional guidelines for mpMRI PCa detection were evaluated. We focused on the performance characteristics of PI-RADS v2, comparing data to systems based on clinicoradiologic Likert scales and non-PI-RADS v2 imaging only. Evidence selections were based on high-quality, prospective, histologically verified data, with minimal patient selection and verifications biases. EVIDENCE SYNTHESIS:It has been shown that the test performance of PI-RADS v2 in research and clinical practice retains higher accuracy over systematic transrectal ultrasound (TRUS) biopsies for PCa diagnosis. PI-RADS v2 fails to detect all cancers but does detect the majority of tumors capable of causing patient harm, which should not be missed. Test performance depends on the definition and prevalence of clinically significant disease. Good performance can be attained in practice when the quality of the diagnostic process can be assured, together with joint working of robustly trained radiologists and urologists, conducting biopsy procedures within multidisciplinary teams. CONCLUSIONS:It has been shown that the test performance of PI-RADS v2 in research and clinical practice is improved, retaining higher accuracy over systematic TRUS biopsies for PCa diagnosis. PATIENT SUMMARY:Multiparametric magnetic resonance imaging (MRI) and MRI-directed biopsies using the Prostate Imaging-Reporting and Data System improves the detection of prostate cancers likely to cause harm, and at the same time decreases the detection of disease that does not lead to harms if left untreated. The keys to success are high-quality imaging, reporting, and biopsies by radiologists and urologists working together in multidisciplinary teams.
Project description:Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist's evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779; p = 0.054) as well as for clinical significance prediction (AUC = 0.688; p = 0.209) and showed a significantly better performance compared to mADC for csPCa prediction (AUC = 0.571; p = 0.022). In our study, radiomics accurately characterizes prostatic index lesions and shows performance comparable to radiologists for PCa characterization. Quantitative image data represent a potential biomarker, which, when combined with PI-RADS, PSAD and DRE, predicts csPCa more accurately than mADC. Prognostic machine learning models could assist in csPCa detection and patient selection for MRI-guided biopsy.