Faculty Bibliography

AIM/OBJECTIVE:To investigate the associations between the hour of the day and Prostate Imaging-Reporting and Data System (PI-RADS) scores assigned by radiologists in prostate MRI reports. MATERIALS AND METHODS/METHODS:Retrospective single-center collection of prostate MRI reports over an 8-year period. Mean PI-RADS scores assigned between 0800 and 1800 h were examined with a regression model. RESULTS: = 0.005, p < 0.001), with malignant scores more frequently assigned later in the day. CONCLUSION/CONCLUSIONS:These findings suggest chronobiological factors may contribute to variability in radiological assessments. Though the magnitude of the effect is small, this may potentially add variability and impact diagnostic accuracy.

INTRODUCTION/BACKGROUND:Local disease recurrence following focal therapy (FT) for prostate cancer may be due to failure to eradicate focal disease or development of disease in the untreated prostate (in- and out-of-field recurrences). Several studies suggest in-field contrast enhancement (CE) on post-treatment multi-parametric (mp) MRI between 6-12 months following FT indicates residual disease. The present study assesses the incidence and oncologic implications of early CE observed following primary partial gland cryoablation (PPGCA). MATERIAL AND METHODS/METHODS:The surveillance protocol for men enrolled in our prospective outcomes study following PPGCA included mpMRI at 6-12 months, 2 years, 3.5 years, and 5 years. All cases of in-field early CE were re-reviewed retrospectively and graded using the previously described Prostate Imaging after Focal Ablation scoring system. All patients exhibiting early CE were re-evaluated by a single radiologist at 2-year mpMRI Results: A total of 320 men enrolled in our PPGCA outcomes study had at least 6 months of follow up. Three hundred fifteen (98%) of these men had undergone post-PPGCA mpMRI at 6-12 months. Of these men, 9 were found to have early in-field CE and 8 underwent repeat MRI at 2 years. In all 8 cases, the CE resolved on the 2-year mpMRI. Of these 8 patients, seven underwent repeat protocol biopsy at 2 years and in-field significant disease was detected in only 1 case. CONCLUSIONS:The most compelling evidence that early CE is not indicative of prostate cancer recurrence is that all lesions resolved within 24 months. While incidence of early CE is low, its consistent resolution calls into question the clinical significance of this finding after PPGCA.

Purpose To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite biparametric (bp) MRI datasets. Materials and Methods This retrospective study included data from 5150 patients (14 191 samples) collected across nine different imaging centers. A novel UDA method using a unified generative model was developed for PCa detection using multisite bpMRI datasets. This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual diffusion-weighted (DW) images acquired using various b values, to align with the style of images acquired using b values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines. The generated ADC and DW images replace the original images for PCa detection. An independent set of 1692 test cases (2393 samples) was used for evaluation. The area under the receiver operating characteristic curve (AUC) was used as the primary metric, and statistical analysis was performed via bootstrapping. Results For all test cases, the AUC values for baseline SL and UDA methods were 0.73 and 0.79 (P < .001), respectively, for PCa lesions with PI-RADS score of 3 or greater and 0.77 and 0.80 (P < .001) for lesions with PI-RADS scores of 4 or greater. In the 361 test cases under the most unfavorable image acquisition setting, the AUC values for baseline SL and UDA were 0.49 and 0.76 (P < .001) for lesions with PI-RADS scores of 3 or greater and 0.50 and 0.77 (P < .001) for lesions with PI-RADS scores of 4 or greater. Conclusion UDA with generated images improved the performance of SL methods in PCa lesion detection across multisite datasets with various b values, especially for images acquired with significant deviations from the PI-RADS-recommended DWI protocol (eg, with an extremely high b value). Keywords: Prostate Cancer Detection, Multisite, Unsupervised Domain Adaptation, Diffusion-weighted Imaging, b Value Supplemental material is available for this article. © RSNA, 2024.

OBJECTIVE:The aim of the study is to evaluate the performance of the ovarian-adnexal reporting and data system magnetic resonance imaging (O-RADS MRI) score and perform individual MRI feature analysis for differentiating between benign and malignant ovarian teratomas. METHODS:In this institutional review board-approved retrospective study, consecutive patients with a pathology-proven fat-containing ovarian mass imaged with contrast-enhanced MRI (1.5T or 3T) from 2013 to 2022 were included. Two blinded radiologists independently evaluated masses per the O-RADS MRI lexicon, including having a "characteristic" or "large" Rokitansky nodule (RN). Additional features analyzed included the following: nodule size/percentage volume relative to total teratoma volume, presence of bulk/intravoxel fat in the nodule, diffusion restriction in the nodule, angular interface, nodule extension through the teratoma border, presence/type of nodule enhancement pattern (solid versus peripheral), and evidence for metastatic disease. An overall O-RADS MRI score was assigned. Patient and lesion features associated with malignancy were evaluated and used to create a malignant teratoma score. χ2, Fisher's exact tests, receiver operating characteristic curve, and κ analysis was performed. RESULTS:One hundred thirty-seven women (median age 34, range 9-84 years) with 123 benign and 14 malignant lesions were included. Mean teratoma size was 7.3 cm (malignant: 14.4 cm, benign: 6.5 cm). 18/123 (14.6%) of benign teratomas were assigned an O-RADS 4 based on the presence of a "large" (11/18) or "noncharacteristic" (12/18) RN. 12/14 malignant nodules occupied >25% of the total teratoma volume (P = 0.09). Features associated with malignancy included the following: age <18 years, an enhancing noncharacteristic RN, teratoma size >12 cm, irregular cystic border, and extralesional extension; these were incorporated into a malignant teratoma score, with a score of 2 or more associated with area under the curve of 0.991 for reviewer 1 and 0.993 for reviewer 2. Peripheral enhancement in a RN was never seen with malignancy (64/123 benign, 0/14 malignant) and would have appropriated downgraded 9/18 overcalled O-RADS 4 benign teratomas. CONCLUSIONS:O-RADS MRI overcalled 15% (18/123) benign teratomas as O-RADS 4 but correctly captured all malignant teratomas. We propose defining a "characteristic" RN as an intravoxel or bulk fat-containing nodule. Observation of a peripheral rim of enhancement in a noncharacteristic RN allowed more accurate prediction of benignity and should be added to the MRI lexicon for improved O-RADS performance.

Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.

INTRODUCTION/BACKGROUND:Prostate cancer diffusion weighted imaging (DWI) MRI is typically performed at high-field strength (3.0 T) in order to overcome low signal-to-noise ratio (SNR). In this study, we demonstrate the feasibility of prostate DWI at low field enabled by random matrix theory (RMT)-based denoising, relying on the MP-PCA algorithm applied during image reconstruction from multiple coils. METHODS:Twenty-one volunteers and 2 prostate cancer patients were imaged with a 6-channel pelvic surface array coil and an 18-channel spine array on a prototype 0.55 T system created by ramping down a commercial magnetic resonance imaging system (1.5 T MAGNETOM Aera Siemens Healthcare) with 45 mT/m gradients and 200 T/m/s slew rate. Diffusion-weighted imagings were acquired with 4 non-collinear directions, for which b = 50 s/mm2 was used with 8 averages and b = 1000 s/mm2 with 40 averages; 2 extra b = 50 s/mm2 were used as part of the dynamic field correction. Standard and RMT-based reconstructions were applied on DWI over different ranges of averages. Accuracy/precision was evaluated using the apparent diffusion coefficient (ADC), and image quality was evaluated over 5 separate reconstructions by 3 radiologists with a 5-point Likert scale. For the 2 patients, we compare image quality and lesion visibility of the RMT reconstruction versus the standard one on 0.55 T and on clinical 3.0 T. RESULTS:The RMT-based reconstruction in this study reduces the noise floor by a factor of 5.8, thereby alleviating the bias on prostate ADC. Moreover, the precision of the ADC in prostate tissue after RMT increases over a range of 30%-130%, with the increase in both signal-to-noise ratio and precision being more prominent for a low number of averages. Raters found that the images were consistently of moderate to good overall quality (3-4 on the Likert scale). Moreover, they determined that b = 1000 s/mm2 images from a 1:55-minute scan with the RMT-based reconstruction were on par with the corresponding images from a 14:20-minute scan with standard reconstruction. Prostate cancer was visible on ADC and calculated b = 1500 images even with the abbreviated 1:55 scan reconstructed with RMT. CONCLUSIONS:Prostate imaging using DWI is feasible at low field and can be performed more rapidly with noninferior image quality compared with standard reconstruction.

BACKGROUND:Demand for prostate MRI is increasing, but scan times remain long even in abbreviated biparametric MRIs (bpMRI). Deep learning can be leveraged to accelerate T2-weighted imaging (T2WI). PURPOSE/OBJECTIVE:To compare conventional bpMRIs (CL-bpMRI) with bpMRIs including a deep learning-accelerated T2WI (DL-bpMRI) in diagnosing prostate cancer. STUDY TYPE/METHODS:Retrospective. POPULATION/METHODS:Eighty consecutive men, mean age 66 years (47-84) with suspected prostate cancer or prostate cancer on active surveillance who had a prostate MRI from December 28, 2020 to April 28, 2021 were included. Follow-up included prostate biopsy or stability of prostate-specific antigen (PSA) for 1 year. FIELD STRENGTH AND SEQUENCES/UNASSIGNED:. ASSESSMENT/RESULTS:CL-bpMRI and DL-bpMRI including the same conventional diffusion-weighted imaging (DWI) were presented to three radiologists (blinded to acquisition method) and to a deep learning computer-assisted detection algorithm (DL-CAD). The readers evaluated image quality using a 4-point Likert scale (1 = nondiagnostic, 4 = excellent) and graded lesions using Prostate Imaging Reporting and Data System (PI-RADS) v2.1. DL-CAD identified and assigned lesions of PI-RADS 3 or greater. STATISTICAL TESTS/METHODS:Quality metrics were compared using Wilcoxon signed rank test, and area under the receiver operating characteristic curve (AUC) were compared using Delong's test. SIGNIFICANCE/CONCLUSIONS:P = 0.05. RESULTS:Eighty men were included (age: 66 ± 9 years; 17/80 clinically significant prostate cancer). Overall image quality results by the three readers (CL-T2, DL-T2) are reader 1: 3.72 ± 0.53, 3.89 ± 0.39 (P = 0.99); reader 2: 3.33 ± 0.82, 3.31 ± 0.74 (P = 0.49); reader 3: 3.67 ± 0.63, 3.51 ± 0.62. In the patient-based analysis, the reader results of AUC are (CL-bpMRI, DL-bpMRI): reader 1: 0.77, 0.78 (P = 0.98), reader 2: 0.65, 0.66 (P = 0.99), reader 3: 0.57, 0.60 (P = 0.52). Diagnostic statistics from DL-CAD (CL-bpMRI, DL-bpMRI) are sensitivity (0.71, 0.71, P = 1.00), specificity (0.59, 0.44, P = 0.05), positive predictive value (0.23, 0.24, P = 0.25), negative predictive value (0.88, 0.88, P = 0.48). CONCLUSION/CONCLUSIONS:Deep learning-accelerated T2-weighted imaging may potentially be used to decrease acquisition time for bpMRI. EVIDENCE LEVEL/METHODS:3. TECHNICAL EFFICACY/UNASSIGNED:Stage 2.

The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In this study, we describe the April 2023 expansion of the fastMRI dataset to include biparametric prostate MRI data acquired on a clinical population. The dataset consists of raw k-space and reconstructed images for T2-weighted and diffusion-weighted sequences along with slice-level labels that indicate the presence and grade of prostate cancer. As has been the case with fastMRI, increasing accessibility to raw prostate MRI data will further facilitate research in MR image reconstruction and evaluation with the larger goal of improving the utility of MRI for prostate cancer detection and evaluation. The dataset is available at https://fastmri.med.nyu.edu.