In this study involving 2103 men with elevated PSA levels, the use of both MRI-targeted and 12-core systematic biopsies was more effective at detecting clinically significant prostate cancers than ...either biopsy method alone.
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
Aim
To investigate the effects of cleansing Fleet’s™ enema (FE) on rectal distention and image quality of diffusion-weighted imaging (DWI) in prostate magnetic resonance imaging (MRI).
Methods
This ...study included 117 prospectively accrued active surveillance patients who underwent prostate MRI both without (prep−) and with bowel preparation consisting of FE (prep+) obtained within 12 months of each other. The anterior–posterior (AP) diameter of the rectum, degree of perceived distention in the rectum and image quality scores were assessed by two independent readers for both (prep− and prep+) scans. DWI distortion was assessed quantitatively using the degree of anatomic mismatches between images obtained at different
b
values and the T2-weighted MRI. DWI artifact was qualitatively scored based on the presence of blurring, poor signal-to-noise, and artifact lines. The difference in rectal AP diameters between the two methods was tested by the paired Wilcoxon rank test. Stuart Maxell test was used in comparing rectal distention, DWI distortion, and artifact. Reader agreement was estimated by kappa statistics.
p
values < 0.05 were considered statistically significant.
Results
Mean rectal AP diameter was significantly larger in prep− compared with prep+ scans (
p
= 0.002). Subjective scores demonstrated inter-reader variability. For instance, the rectal distention score was significantly lower in prep+ for reader 2 (
p
< 0.001) whereas it was not significant for reader 1 (
p
= 0.09). Reader 2 also found significant improvement in DWI distortion (
p
= 0.02) in prep+ scans. There was no significant difference between prep− and prep+ in DWI distortion and artifacts for reader 1 (
p
= 0.17 and
p
= 0.49, respectively), or DWI artifacts for reader 2 (
p
= 0.55). Kappa scores were moderate for rectal distension, but weak for DWI distortion, and artifacts.
Conclusion
Bowel preparation with enema prior to prostate MRI may diminish rectal gas but has modest effects on DWI distortion and overall image quality. The value of bowel prep is not conclusively validated in this study.
Purpose
To present fully automated DL-based prostate cancer detection system for prostate MRI.
Methods
MRI scans from two institutions, were used for algorithm training, validation, testing. ...MRI-visible lesions were contoured by an experienced radiologist. All lesions were biopsied using MRI-TRUS-guidance. Lesions masks, histopathological results were used as ground truth labels to train UNet, AH-Net architectures for prostate cancer lesion detection, segmentation. Algorithm was trained to detect any prostate cancer ≥ ISUP1. Detection sensitivity, positive predictive values, mean number of false positive lesions per patient were used as performance metrics.
Results
525 patients were included for training, validation, testing of the algorithm. Dataset was split into training (
n
= 368, 70%), validation (
n
= 79, 15%), test (
n
= 78, 15%) cohorts. Dice coefficients in training, validation sets were 0.403, 0.307, respectively, for AHNet model compared to 0.372, 0.287, respectively, for UNet model. In validation set, detection sensitivity was 70.9%, PPV was 35.5%, mean number of false positive lesions/patient was 1.41 (range 0–6) for UNet model compared to 74.4% detection sensitivity, 47.8% PPV, mean number of false positive lesions/patient was 0.87 (range 0–5) for AHNet model. In test set, detection sensitivity for UNet was 72.8% compared to 63.0% for AHNet, mean number of false positive lesions/patient was 1.90 (range 0–7), 1.40 (range 0–6) in UNet, AHNet models, respectively.
Conclusion
We developed a DL-based AI approach which predicts prostate cancer lesions at biparametric MRI with reasonable performance metrics. While false positive lesion calls remain as a challenge of AI-assisted detection algorithms, this system can be utilized as an adjunct tool by radiologists.
Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and has clinical potential as a prognostic biomarker. The objective of this work was to develop an ...artificial intelligence (AI) system for automated detection and localization of PTEN loss on immunohistochemically (IHC) stained sections. PTEN loss was assessed using IHC in two prostate tissue microarrays (TMA) (internal cohort, n = 272 and external cohort, n = 129 patients). TMA cores were visually scored for PTEN loss by pathologists and, if present, spatially annotated. Cores from each patient within the internal TMA cohort were split into 90% cross-validation (N = 2048) and 10% hold-out testing (N = 224) sets. ResNet-101 architecture was used to train core-based classification using a multi-resolution ensemble approach (×5, ×10, and ×20). For spatial annotations, single resolution pixel-based classification was trained from patches extracted at ×20 resolution, interpolated to ×40 resolution, and applied in a sliding-window fashion. A final AI-based prediction model was created from combining multi-resolution and pixel-based models. Performance was evaluated in 428 cores of external cohort. From both cohorts, a total of 2700 cores were studied, with a frequency of PTEN loss of 14.5% in internal (180/1239) and external 13.5% (43/319) cancer cores. The final AI-based prediction of PTEN status demonstrated 98.1% accuracy (95.0% sensitivity, 98.4% specificity; median dice score = 0.811) in internal cohort cross-validation set and 99.1% accuracy (100% sensitivity, 99.0% specificity; median dice score = 0.804) in internal cohort test set. Overall core-based classification in the external cohort was significantly improved in the external cohort (area under the curve = 0.964, 90.6% sensitivity, 95.7% specificity) when further trained (fine-tuned) using 15% of cohort data (19/124 patients). These results demonstrate a robust and fully automated method for detection and localization of PTEN loss in prostate cancer tissue samples. AI-based algorithms have potential to streamline sample assessment in research and clinical laboratories.
Developing 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.
We 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.
Our 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.
We 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.
The androgen receptor (AR) is a crucial player in various aspects of male reproduction and has been associated with the development and progression of prostate cancer (PCa). Therefore, the protein is ...the linchpin of current PCa therapies. Despite great research efforts, the AR signaling pathway has still not been deciphered, and the emergence of resistance is still the biggest problem in PCa treatment. To discuss the latest developments in AR research, the "1st International Androgen Receptor Symposium" offered a forum for the exchange of clinical and scientific innovations around the role of the AR in prostate cancer (PCa) and to stimulate new collaborative interactions among leading scientists from basic, translational, and clinical research. The symposium included three sessions covering preclinical studies, prognostic and diagnostic biomarkers, and ongoing prostate cancer clinical trials. In addition, a panel discussion about the future direction of androgen deprivation therapy and anti-AR therapy in PCa was conducted. Therefore, the newest insights and developments in therapeutic strategies and biomarkers are discussed in this report.
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