Intra- and interreader reproducibility of PI-RADSv2: A multireader study

Background: The Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) has been in use since 2015; while interreader reproducibility has been studied, there has been a paucity of studies investigating the intrareader reproducibility of PI-RADSv2. Purpose: To evaluate both intra- and interreader reproducibility of PI-RADSv2 in the assessment of intraprostatic lesions using multiparametric magnetic resonance imaging (mpMRI). Study Type: Retrospective. Population/Subjects: In all, 102 consecutive biopsy-naïve patients who underwent prostate MRI and subsequent MR/transrectal ultrasonography (MR/TRUS)-guided biopsy. Field Strength/Sequences: Prostate mpMRI at 3T using endorectal with phased array surface coils (TW MRI, DW MRI with ADC maps and b2000 DW MRI, DCE MRI). Assessment: Previously detected and biopsied lesions were scored by four readers from four different institutions using PI-RADSv2. Readers scored lesions during two readout rounds with a 4-week washout period. Statistical Tests: Kappa (κ) statistics and specific agreement (Po) were calculated to quantify intra- and interreader reproducibility of PI-RADSv2 scoring. Lesion measurement agreement was calculated using the intraclass correlation coefficient (ICC). Results: Overall intrareader reproducibility was moderate to substantial (κ = 0.43–0.67, Po = 0.60–0.77), while overall interreader reproducibility was poor to moderate (κ = 0.24, Po = 46). Readers with more experience showed greater interreader reproducibility than readers with intermediate experience in the whole prostate (P = 0.026) and peripheral zone (P = 0.002). Sequence-specific interreader agreement for all readers was similar to the overall PI-RADSv2 score, with κ = 0.24, 0.24, and 0.23 and Po = 0.47, 0.44, and 0.54 in T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE), respectively. Overall intrareader and interreader ICC for lesion measurement was 0.82 and 0.71, respectively. Data Conclusion: PI-RADSv2 provides moderate intrareader reproducibility, poor interreader reproducibility, and moderate interreader lesion measurement reproducibility. These findings suggest a need for more standardized reader training in prostate MRI. Level of Evidence: 2. Technical Efficacy: Stage 2

Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation.

For prostate cancer detection on prostate multiparametric MRI (mpMRI), the Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) and computer-aided diagnosis (CAD) systems aim to widely improve standardization across radiologists and centers. Our goal was to evaluate CAD assistance in prostate cancer detection compared with conventional mpMRI interpretation in a diverse dataset acquired from five institutions tested by nine readers of varying experience levels, in total representing 14 globally spread institutions. Index lesion sensitivities of mpMRI-alone were 79% (whole prostate (WP)), 84% (peripheral zone (PZ)), 71% (transition zone (TZ)), similar to CAD at 76% (WP, p=0.39), 77% (PZ, p=0.07), 79% (TZ, p=0.15). Greatest CAD benefit was in TZ for moderately-experienced readers at PI-RADSv2 <3 (84% vs mpMRI-alone 67%, p=0.055). Detection agreement was unchanged but CAD-assisted read times improved (4.6 vs 3.4 minutes, p<0.001). At PI-RADSv2 ≥ 3, CAD improved patient-level specificity (72%) compared to mpMRI-alone (45%, p<0.001). PI-RADSv2 and CAD-assisted mpMRI interpretations have similar sensitivities across multiple sites and readers while CAD has potential to improve specificity and moderately-experienced radiologists' detection of more difficult tumors in the center of the gland. The multi-institutional evidence provided is essential to future prostate MRI and CAD development

Molecular biomarkers in the context of focal therapy for prostate cancer: Recommendations of a delphi consensus from the focal therapy society

BACKGROUND: Focal therapy (FT) for prostate cancer (PCa) is promising. However, long-term oncological results are awaited and there is no consensus on follow-up strategies. Molecular biomarkers (MB) may be useful in selecting, treating and following up men undergoing FT, though there is limited evidence in this field to guide practice. We aimed to conduct a consensus meeting, endorsed by the Focal Therapy Society, amongst a large group of experts, to understand the potential utility of MB in FT for localized PCa. METHODS: A 38-item questionnaire was built following a literature search. The authors then performed three rounds of a Delphi Consensus using DelphiManager, using the GRADE grid scoring system, followed by a face-to-face expert meeting. Three areas of interest were identified and covered concerning MB for FT, 1) the current/present role; 2) the potential/future role; 3) the recommended features for future studies. Consensus was defined using a 70% agreement threshold. RESULTS: Of 95 invited experts, 42 (44.2%) completed the three Delphi rounds. Twenty-four items reached a consensus and they were then approved at the meeting involving (N.=15) experts. Fourteen items reached a consensus on uncertainty, or they did not reach a consensus. They were re-discussed, resulting in a consensus (N.=3), a consensus on a partial agreement (N.=1), and a consensus on uncertainty (N.=10). A final list of statements were derived from the approved and discussed items, with the addition of three generated statements, to provide guidance regarding MB in the context of FT for localized PCa. Research efforts in this field should be considered a priority. CONCLUSIONS: The present study detailed an initial consensus on the use of MB in FT for PCa. This is until evidence becomes available on the subject

Apical periurethral transition zone lesions: MRI and histology findings

Purpose Apical periurethral transition zone (TZ) cancers can pose unique problems for surgery and radiation therapy. Here, we describe the appearance of such cancers on multiparametric MRI (mpMRI) and correlate this with histopathology derived from MRI-targeted biopsy. Materials and methods Between May 2011 and January 2019, a total of 4381 consecutive patients underwent 3 T mpMRI. Of these, 53 patients with 58 apical periurethral TZ lesions underwent TRUS/MRI fusion-guided biopsy and 12-core systematic TRUS-guided biopsy. Correlation was made with patient age, PSA, PSA density, whole prostate volume, and Gleason scores. Results A total 53 men (median age 68 years, median PSA 7.94 ng/ml) were identified as having at least one apical periurethral TZ lesion on mpMRI and 5 (9%) patients had more than one apical periurethral lesion. Thus, 58 lesions were identified in 53 patients. Of these 37/53 patients (69%) and 40/58 lesions were positive at biopsy for prostate cancer. Seven were diagnosed by 12-core systematic TRUS-guided biopsy and 34 were diagnosed by TRUS/MRI fusion-guided biopsy. Gleason score was >= 3 + 4 in 34/58 (58%) lesions. Conclusion Identification of apical periurethral TZ prostate cancers is important to help guide surgical and radiation therapy as these tumors are adjacent to critical structures. Because of the tendency to undersample the periurethral zone during TRUS biopsy, MRI-guided biopsy is particularly helpful for detecting apical periurethral TZ prostate cancers many of which prove to be clinically significant

Apical periurethral transition zone lesions: MRI and histology findings

© 2019, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply. Purpose: Apical periurethral transition zone (TZ) cancers can pose unique problems for surgery and radiation therapy. Here, we describe the appearance of such cancers on multiparametric MRI (mpMRI) and correlate this with histopathology derived from MRI-targeted biopsy. Materials and methods: Between May 2011 and January 2019, a total of 4381 consecutive patients underwent 3 T mpMRI. Of these, 53 patients with 58 apical periurethral TZ lesions underwent TRUS/MRI fusion-guided biopsy and 12-core systematic TRUS-guided biopsy. Correlation was made with patient age, PSA, PSA density, whole prostate volume, and Gleason scores. Results: A total 53 men (median age 68 years, median PSA 7.94 ng/ml) were identified as having at least one apical periurethral TZ lesion on mpMRI and 5 (9%) patients had more than one apical periurethral lesion. Thus, 58 lesions were identified in 53 patients. Of these 37/53 patients (69%) and 40/58 lesions were positive at biopsy for prostate cancer. Seven were diagnosed by 12-core systematic TRUS-guided biopsy and 34 were diagnosed by TRUS/MRI fusion-guided biopsy. Gleason score was ≥ 3 + 4 in 34/58 (58%) lesions. Conclusion: Identification of apical periurethral TZ prostate cancers is important to help guide surgical and radiation therapy as these tumors are adjacent to critical structures. Because of the tendency to undersample the periurethral zone during TRUS biopsy, MRI-guided biopsy is particularly helpful for detecting apical periurethral TZ prostate cancers many of which prove to be clinically significant

Harnessing clinical annotations to improve deep learning performance in prostate segmentation

PurposeDeveloping large-scale datasets with research-quality annotations is challenging due to the high cost of refining clinically generated markup into high precision annotations. We evaluated the direct use of a large dataset with only clinically generated annotations in development of high-performance segmentation models for small research-quality challenge datasets.Materials and methodsWe used a large retrospective dataset from our institution comprised of 1,620 clinically generated segmentations, and two challenge datasets (PROMISE12: 50 patients, ProstateX-2: 99 patients). We trained a 3D U-Net convolutional neural network (CNN) segmentation model using our entire dataset, and used that model as a template to train models on the challenge datasets. We also trained versions of the template model using ablated proportions of our dataset, and evaluated the relative benefit of those templates for the final models. Finally, we trained a version of the template model using an out-of-domain brain cancer dataset, and evaluated the relevant benefit of that template for the final models. We used five-fold cross-validation (CV) for all training and evaluation across our entire dataset.ResultsOur model achieves state-of-the-art performance on our large dataset (mean overall Dice 0.916, average Hausdorff distance 0.135 across CV folds). Using this model as a pre-trained template for refining on two external datasets significantly enhanced performance (30% and 49% enhancement in Dice scores respectively). Mean overall Dice and mean average Hausdorff distance were 0.912 and 0.15 for the ProstateX-2 dataset, and 0.852 and 0.581 for the PROMISE12 dataset. Using even small quantities of data to train the template enhanced performance, with significant improvements using 5% or more of the data.ConclusionWe trained a state-of-the-art model using unrefined clinical prostate annotations and found that its use as a template model significantly improved performance in other prostate segmentation tasks, even when trained with only 5% of the original dataset

Supplementary Material for: The role of multiparametric MRI (mpMRI) in the prediction of adverse prostate cancer pathology in radical prostatectomy specimen

Introduction: Prostate cancer (PCa) risk stratification is essential in guiding therapeutic decision. Multiparametric magnetic resonance tomography (mpMRI) holds promise in prediction of adverse pathologies (AP) after prostatectomy (RP). This study aims to identify clinical and imaging markers in the prediction of adverse pathology. Methods: Patients with PCa, diagnosed by targeted biopsy after mpMRI and undergoing RP were included. The predictive accuracy of mpMRI for extraprostatic extension (ECE), seminal vesicle infiltration (SVI) and lymph node positivity was calculated from the final histopathology. Results: 846 patients were involved. Independent risk parameters include imaging findings as ECE (OR 3.12), SVI (OR 2.55) and PI-RADS scoring (4: OR 2.01 and 5: OR 4.34). mpMRI parameters such as ECE, SVI and lymph node metastases showed a high prognostic accuracy (73.28% vs. 95.35% vs. 93.38%) with moderate sensitivity compared to the final histopathology. The ROC-analysis of our combined scoring system (D’Amico classification, PSA density and MRI risk factors) improves prediction of adverse pathology (AUC 0.73 vs. 0.69). Conclusion: Our study supports the use of mpMRI for comprehensive pre-treatment risk assessment in PCa. Due to the high accuracy of factors like ECE, SVI and PI-RADS scoring, utilizing mpMRI data enabled accurate prediction of unfavourable pathology after RP

Data Augmentation and Transfer Learning to Improve Generalizability of an Automated Prostate Segmentation Model

OBJECTIVE. Deep learning applications in radiology often suffer from overfitting, limiting generalization to external centers. The objective of this study was to develop a high-quality prostate segmentation model capable of maintaining a high degree of performance across multiple independent datasets using transfer learning and data augmentation. MATERIALS AND METHODS. A retrospective cohort of 648 patients who underwent prostate MRI between February 2015 and November 2018 at a single center was used for training and validation. A deep learning approach combining 2D and 3D architecture was used for training, which incorporated transfer learning. A data augmentation strategy was used that was specific to the deformations, intensity, and alterations in image quality seen on radiology images. Five independent datasets, four of which were from outside centers, were used for testing, which was conducted with and without fine-tuning of the original model. The Dice similarity coefficient was used to evaluate model performance. RESULTS. When prostate segmentation models utilizing transfer learning were applied to the internal validation cohort, the mean Dice similarity coefficient was 93.1 for whole prostate and 89.0 for transition zone segmentations. When the models were applied to multiple test set cohorts, the improvement in performance achieved using data augmentation alone was 2.2% for the whole prostate models and 3.0% for the transition zone segmentation models. However, the best test-set results were obtained with models fine-tuned on test center data with mean Dice similarity coefficients of 91.5 for whole prostate segmentation and 89.7 for transition zone segmentation. CONCLUSION. Transfer learning allowed for the development of a high-performing prostate segmentation model, and data augmentation and fine-tuning approaches improved performance of a prostate segmentation model when applied to datasets from external centers