Reasons for Discordance between 68Ga-PSMA-PET and Magnetic Resonance Imaging in Men with Metastatic Prostate Cancer.

BACKGROUND: PSMA PET has emerged as a "gold standard" imaging modality for assessing prostate cancer metastases. However, it is not universally available, and this limits its impact. In contrast, whole-body MRI is much more widely available but misses more lesions. This study aims to improve the interpretation of whole-body MRI by comparing false negative scans retrospectively to PSMA PET. METHODS: This study was a retrospective sub-analysis of a prospectively collected database of patients who participated in a clinical trial of PSMA PET/MRI comparing PSMA PET and whole-body MRI from 2018-2021. Subjects whose separately read PSMA PET and MRI diagnostic reports showed discrepancies ("false negative" MRI cases) were selected for sub-analysis. The cases were reviewed by the same attending radiologist who originally read the scans. The radiologist noted specific features on MRI indicating metastatic disease that were initially missed. RESULTS: Of 263 cases, 38 (14%) met the inclusion criteria and were reviewed. Six classes of mpMRI false negatives were identified: anatomically normal (18, 47%), atypical MRI appearance (6, 16%), mischaracterization (1, 3%), undercall (6, 16%), obscured (4, 11%), and no abnormality on MRI (3, 8%). Considering that the atypical and undercalled cases could have been adjusted in retrospect, and that 4 additional cases had positive lesions to the same extent and 11 further cases had disease confined to the pelvis, only 11 (4%) of the original 263 would have had disease outside of a conventional radiation treatment plan. CONCLUSION: Notably, almost 50% of the cases, including most lymph node metastases, were anatomically normal using standard criteria. This suggests that current anatomic criteria for evaluating prostate cancer lymph node metastases are not ideal, and there is a need for improved criteria. In addition, 32% of cases involved some element of human interpretive error, and, therefore, improving reader training may lead to more accurate results.

MRI-Ultrasound Fused Approach for Prostate Biopsy-How It Is Performed.

The use of MRI-ultrasound image fusion targeted biopsy of the prostate in the face of an elevated serum PSA is now recommended by multiple societies, and results in improved detection of clinically significant cancer and, potentially, decreased detection of indolent disease. This combines the excellent sensitivity of MRI for clinically significant prostate cancer and the real-time biopsy guidance and confirmation of ultrasound. Both transperineal and transrectal approaches can be implemented using cognitive fusion, mechanical fusion with an articulated arm and electromagnetic registration, or pure software registration. The performance has been shown comparable to in-bore MRI biopsy performance. However, a number of factors influence the performance of this technique, including the quality and interpretation of the MRI, the approach used for biopsy, and experience of the practitioner, with most studies showing comparable performance of MRI-ultrasound fusion to in-bore targeted biopsy. Future improvements including artificial intelligence promise to refine the performance of all approaches.

Management of Patients With a Negative Multiparametric Prostate MRI Examination: AJR Expert Panel Narrative Review.

Multiparametric prostate MRI (mpMRI) aids risk stratification of patients with elevated PSA levels. While most clinically significant prostate cancers are detected by mpMRI, insignificant cancers are less evident. Thus, multiple international prostate cancer guidelines now endorse routine use of prostate MRI as a secondary screening test before prostate biopsy. Nonetheless, management of patients with negative mpMRI results (defined as PI-RADS category 1 or 2) remains unclear. This AJR Expert Panel Narrative Review summarizes the available literature on patients with an elevated screening PSA level and a negative prostate mpMRI, and provides guidance for these patients' management. Systematic biopsy should not be routinely performed after a negative mpMRI in patients at average risk but should be considered in patients at high risk. In patients who undergo PSA screening rather than systematic biopsy after negative mpMRI, clear triggers should be established for when to perform a repeat MRI. Patients with negative MRI followed by negative biopsy should follow their healthcare practitioners' preferred guidelines concerning subsequent PSA screening for the patient's risk level. Insufficient high-level data exist to support routine use of adjunctive serum or urine biomarkers, artificial intelligence, or PSMA PET to determine the need for prostate biopsy after negative mpMRI.

Partial-gland Cryoablation Outcomes for Localized Prostate Cancer in Patients with Magnetic Resonance Imaging (MRI)-visible and MRI-invisible Lesions.

Background: Expert consensus recommends treatment of magnetic resonance imaging (MRI)-visible prostate cancer (PCa). Outcomes of partial-gland ablation (PGA) for MRI-invisible PCa remain unknown. Objective: To compare recurrence-free survival, adverse events, and health-related quality of life (HRQoL) outcomes following cryoablation of MRI-visible vs invisible PCa. Design setting and participants: We analyzed data for 75 men who underwent cryoablation therapy between January 2017 and January 2022. PCa identified on MRI-targeted and/or adjacent systematic biopsy cores was defined as MRI-visible, whereas PCa identified on systematic biopsy beyond the targeted zone was defined as MRI-invisible. Outcome measurements and statistical analysis: The primary outcome was recurrence at 12 mo after PGA, defined as the presence of clinically significant PCa (grade group [GG] ≥2) on surveillance biopsy. Adverse events were captured using the Clavien-Dindo classification and HRQoL was captured using the Expanded Prostate Cancer Index-Clinical Practice (EPIC-CP) tool. Results and limitations: Of the 58 men treated for MRI-visible and 17 treated for MRI-invisible lesions, 51 (88%) and 16 (94%), respectively, had at least one surveillance biopsy performed. There were no statistically significant differences in age, race, body mass index, biopsy GG, prostate-specific antigen, prostate volume, or treatment extent between the MRI-visible and MRI-invisible groups. Median follow-up was 44 mo (interquartile range 17-54) and did not significantly differ between the groups. The recurrence rate at 12 mo did not significantly differ between the groups (MRI-visible 39%, MRI-invisible 19%; p = 0.2), and log-rank survival analysis demonstrated no significant difference in recurrence-free survival (p = 0.15). Adverse event rates did not significantly differ (MRI-visible 29%, MRI-invisible 53%; p = 0.092); no man in the MRI-visible group had a Clavien-Dindo grade ≥III complication, while one subject in the MRI-invisible group had a Clavien-Dindo grade III complication. Median EPIC-CP urinary and sexual function scores were similar for the two groups at baseline and at 12 mo after PGA. Study limitations include the retrospective design and small sample size. Conclusions: We observed similar cancer control, adverse event, and HRQoL outcomes for MRI-visible versus MRI-invisible PCa in the first comparison of partial-gland cryoablation. Longer follow-up and external validation of our findings are needed to inform patient selection for PGA for MRI-invisible PCa. Patient summary: Patients with prostate cancer lesions that are not visible on magnetic resonance imaging (MRI) scans who undergo partial gland ablation may have similar treatment outcomes compared to patients with cancer lesions that are visible on MRI.

The role of AI in prostate MRI quality and interpretation: Opportunities and challenges.

Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities. While efforts have been made to standardize image acquisition and interpretation via the development of systems, such as PI-RADS and PI-QUAL, the scoring systems still depend on the subjective experience and acumen of humans. Artificial intelligence (AI) has been increasingly used in many applications, including medical imaging, due to its ability to automate tasks and lower human error rates. These advantages have the potential to standardize the tasks of image interpretation and quality control of prostate MRI. Despite its potential, thorough validation is required before the implementation of AI in clinical practice. In this article, we explore the opportunities and challenges of AI, with a focus on the interpretation and quality of prostate MRI.

Variability of Prostate MRI Charges Among U.S. Hospital-Based Facilities.

The Premier Healthcare Database was used to assess charge variation for prostate MRI examinations in U.S. hospitals from January 2010 to March 2020. In 552 facilities performing 37,073 examinations, the median charge per examination was $4419 with 26-fold variation between the lowest ($593) and highest ($15,150) median facility charges. In multilevel linear regression analysis, interfacility variation explained 63.9% of charge variation. Patients may be charged vastly different prices for prostate MRI depending on the facility.

Pulse Sequence Dependence of a Simple and Interpretable Deep Learning Method for Detection of Clinically Significant Prostate Cancer Using Multiparametric MRI.

RATIONALE AND OBJECTIVES: Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for risk stratification and localization of prostate cancer (PCa). Thanks to the great success of deep learning models in computer vision, the potential application for early detection of PCa using mpMRI is imminent. MATERIALS AND METHODS: Deep learning analysis of the PROSTATEx dataset. RESULTS: In this study, we show a simple convolutional neural network (CNN) with mpMRI can achieve high performance for detection of clinically significant PCa (csPCa), depending on the pulse sequences used. The mpMRI model with T2-ADC-DWI achieved 0.90 AUC score in the held-out test set, not significantly better than the model using Ktrans instead of DWI (AUC 0.89). Interestingly, the model incorporating T2-ADC- Ktrans better estimates grade. We also describe a saliency "heat" map. Our results show that csPCa detection models with mpMRI may be leveraged to guide clinical management strategies. CONCLUSION: Convolutional neural networks incorporating multiple pulse sequences show high performance for detection of clinically-significant prostate cancer, and the model including dynamic contrast-enhanced information correlates best with grade.

Pilot study of the diagnostic utility of 89 Zr-df-IAB2M and 68 Ga-PSMA-11 PET imaging and multiparametric MRI in localized prostate cancer.

BACKGROUND: Accurate diagnosis of localized prostate cancer (PCa) is limited by inadequacy of multiparametric (mp) MRI to fully identify and differentiate localized malignant tissue from benign pathologies. Prostate-specific membrane antigen (PSMA) represents an excellent target for molecular imaging. IAB2M, an 85-kD minibody derived from a de-immunized monoclonal antibody directed at the extracellular domain of human PSMA (huJ591), and PSMA-11, a small molecule ligand have been previously tested as probes for visualization of recurrent/metastatic PCa with PET/CT. This pilot, non-randomized trial studied their diagnostic utility in patients (pts) with localized PCa. METHODS: Pts planned for radical prostatectomy (RP) were enrolled and underwent mpMRI and PET/CT imaging with 89 Zr-df-IAB2M and/or 68 Ga-PSMA-PET/CT. Image results were read by a radiologist blinded to clinical information and pathology results, mapped and compared to corresponding histopathology findings from all lesions, both clinically significant and nonsignificant. The detection rates of all three imaging modalities were measured and correlated. RESULTS: 20 pts with median age of 64.5 (46-79) years and PSA level of 7.5 (1.6-36.56) ng/ml were enrolled. 19 pts underwent RP and were imaged pre-operatively with 89 Zr-Df-IAB2M PET/CT and mpMRI. Nine of those were imaged using 68 Ga-PSMA-11 as well. Out of 48 intraprostatic lesions verified on surgical pathology, IAB2M PET/CT was able to detect 36 (75%). A similar proportion of pathologically confirmed, clinically significant lesions (22/29, 76%) was detected. IAB2M PET/CT was also able to identify 14/19 (74%) extraprostatic lesions. The performance of mpMRI was inferior, with 24/48 detectable lesions (50%) and 18/29 clinically significant intraprostatic lesions (62%). Compared to the current standard (mpMRI), IAB2M PET/CT had a sensitivity of 88%, specificity 38%, positive predictive value 58%, and accuracy 63%. In 9 pts who underwent Ga-PSMA-11 as well, the latter yielded a detection rate of 70% (14/20), which was also seen in clinically significant lesions (10/14, 71%). Ga-PSMA-11 PET/CT also detected 4/6 (67%) extraprostatic lesions. CONCLUSIONS: In this pilot study, the performance of 89 Zr-df-IAB2M was superior to mpMRI and similar to 68 Ga-PSMA-11 PET/CT. The higher detection rate of PSMA-PET supports its use as a diagnostic tool with consequent management change implications in men with localized PCa.

Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions.

BACKGROUND: While Prostate Imaging Reporting and Data System (PI-RADS) 4 and 5 lesions typically warrant prostate biopsy and PI-RADS 1 and 2 lesions may be safely observed, PI-RADS 3 lesions are equivocal. PURPOSE: To construct and cross-validate a machine learning model based on radiomics features from T2 -weighted imaging (T2 WI) of PI-RADS 3 lesions to identify clinically significant prostate cancer (csPCa), that is, pathological Grade Group ≥ 2. STUDY TYPE: Single-center retrospective study. POPULATION: A total of 240 patients were included (training cohort, n = 188, age range 43-82 years; test cohort, n = 52, age range 41-79 years). Eligibility criteria were 1) magnetic resonance imaging (MRI)-targeted biopsy between 2015 and 2020; 2) PI-RADS 3 index lesion identified on multiparametric MRI; (3) biopsy performed within 1 year of MRI. The percentages of csPCa lesions were 10.6% and 15.4% in the training and test cohorts, respectively. FIELD STRENGTH/SEQUENCE: A 3 T; T2 WI turbo-spin echo, diffusion-weighted spin-echo echo planar imaging, dynamic contrast-enhanced MRI with time-resolved T1-weighted imaging. ASSESSMENT: Multislice volumes-of-interest (VOIs) were drawn in the PI-RADS 3 index lesions on T2 WI. A total of 107 radiomics features (first-order histogram and second-order texture) were extracted from the segmented lesions. STATISTICAL TESTS: A random forest classifier using the radiomics features as input was trained and validated for prediction of csPCa. The performance of the machine learning classifier, prostate specific antigen (PSA) density, and prostate volume for csPCa prediction was evaluated using receiver operating characteristic (ROC) analysis. RESULTS: The trained random forest classifier constructed from the T2 WI radiomics features good and statistically significant area-under-the-curves (AUCs) of 0.76 (P = 0.022) for prediction of csPCa in the test set. Prostate volume and PSA density showed moderate and nonsignificant performance (AUC 0.62, P = 0.275 and 0.61, P = 0.348, respectively) for csPCa prediction in the test set. CONCLUSION: The machine learning classifier based on T2 WI radiomic features demonstrated good performance for prediction of csPCa in PI-RADS 3 lesions. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: 2.

Practice Patterns and Challenges of Performing and Interpreting Prostate MRI: A Survey by the Society of Abdominal Radiology Prostate Disease-Focused Panel.

OBJECTIVE. The purpose of this study was to report on the practice patterns and challenges of performing and interpreting prostate MRI. SUBJECTS AND METHODS. An electronic survey regarding prostate MRI practice patterns and challenges was sent to members of the Society of Abdominal Radiology. RESULTS. The response rate was 15% (212/1446). Most (65%) of the respondents were academic abdominal radiologists with 1-5 (52%), 6-10 (20%), 11-20 (15%), and more than 20 (5%) years of experience in reporting prostate MRI. The numbers of prostate MRI examinations reported per week were 0-5 (43%), 6-10 (38%), 11-20 (12%), 21-30 (5%), and more than 30 (2%). Imaging was performed at 3 T (58%), 1.5 T (20%), or either (21%), and most examinations (83%) were performed without an endorectal coil. Highest b values ranged from 800 to 5000 s/mm2; 1400 s/mm2 (26%) and 1500 s/mm2 (30%) were the most common. Most respondents (79%) acquired dynamic contrast-enhanced images with temporal resolution of less than 10 seconds. Most (71%) of the prostate MRI studies were used for fusion biopsy. PI-RADS version 2 was used by 92% of the respondents and template reporting by 80%. Challenges to performing and interpreting prostate MRI were scored on a 1-5 Likert scale (1, easy; 2, somewhat easy; 3, neutral; 4, somewhat difficult; 5, very difficult). The median scores were 2 or 3 for patient preparatory factors. Image acquisition and reporting factors were scored 1-2, except for performing spectroscopy or using an endorectal coil, both of which scored 4. Acquiring patient history scored 2 and quality factors scored 3. CONCLUSION. Most radiologists perform prostate MRI at 3 T without an endorectal coil and interpret the images using PI-RADS version 2. Challenges include obtaining quality images, acquiring feedback, and variability in the interpretation of PI-RADS scores.

Multivariate analysis of CT imaging, laboratory, and demographical features for prediction of acute kidney injury in COVID-19 patients: a Bi-centric analysis.

PURPOSE: To develop and externally validate a multivariate prediction model for the prediction of acute kidney injury (AKI) in COVID-19, based on baseline renal perfusion from contrast-enhanced CT together with clinical and laboratory parameters. METHODS: In this retrospective IRB-approved study, we identified COVID-19 patients who had a standard-of-care contrast-enhanced abdominal CT scan within 5 days of their COVID-19 diagnosis at our institution (training set; n = 45, mean age 65 years, M/F 23/22) and at a second institution (validation set; n = 41, mean age 61 years, M/F 22/19). The CT renal perfusion parameter, cortex-to-aorta enhancement index (CAEI), was measured in both sets. A multivariate logistic regression model for predicting AKI was constructed from the training set with stepwise feature selection with CAEI together with demographical and baseline laboratory/clinical data used as input variables. Model performance in the training and validation set was evaluated with ROC analysis. RESULTS: AKI developed in 16 patients (35.6%) of the training set and in 6 patients (14.6%) of the validation set. Baseline CAEI was significantly lower in the patients that ultimately developed AKI (P = 0.003). Logistic regression identified a model combining baseline CAEI, blood urea nitrogen, and gender as most significant predictor of AKI. This model showed excellent diagnostic performance for prediction of AKI in the training set (AUC = 0.89, P < 0.001) and good performance in the validation set (AUC 0.78, P = 0.030). CONCLUSION: Our results show diminished renal perfusion preceding AKI and a promising role of CAEI, combined with laboratory and demographic markers, for prediction of AKI in COVID-19.

PI-RADS Committee Position on MRI Without Contrast Medium in Biopsy-Naive Men With Suspected Prostate Cancer: Narrative Review.

The steadily increasing demand for diagnostic prostate MRI has led to concerns regarding the lack of access to and the availability of qualified MRI scanners and sufficiently experienced radiologists, radiographers, and technologists to meet the demand. Solutions must enhance operational benefits without compromising diagnostic performance, quality, and delivery of service. Solutions should also mitigate risks such as decreased reader confidence and referrer engagement. One approach may be the implementation of MRI without the use gadolinium-based contrast medium (bipara-metric MRI), but only if certain prerequisites such as high-quality imaging, expert interpretation quality, and availability of patient recall or on-table monitoring are mandated. Alternatively, or in combination, a clinical risk-based approach could be used for protocol selection, specifically, which biopsy-naive men need MRI with contrast medium (multiparametric MRI). There is a need for prospective studies in which biopsy decisions are made according to MRI without contrast enhancement. Such studies must define clinical and operational benefits and identify which patient groups can be scanned successfully without contrast enhancement. These higher-quality data are needed before the Prostate Imaging Reporting and Data System (PI-RADS) Committee can make evidence-based recommendations about MRI without contrast enhancement as an initial diagnostic approach for prostate cancer workup.

Variability of the Positive Predictive Value of PI-RADS for Prostate MRI across 26 Centers: Experience of the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel.

Background Prostate MRI is used widely in clinical care for guiding tissue sampling, active surveillance, and staging. The Prostate Imaging Reporting and Data System (PI-RADS) helps provide a standardized probabilistic approach for identifying clinically significant prostate cancer. Despite widespread use, the variability in performance of prostate MRI across practices remains unknown. Purpose To estimate the positive predictive value (PPV) of PI-RADS for the detection of high-grade prostate cancer across imaging centers. Materials and Methods This retrospective cross-sectional study was compliant with the HIPAA. Twenty-six centers with members in the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel submitted data from men with suspected or biopsy-proven untreated prostate cancer. MRI scans were obtained between January 2015 and April 2018. This was followed with targeted biopsy. Only men with at least one MRI lesion assigned a PI-RADS score of 2-5 were included. Outcome was prostate cancer with Gleason score (GS) greater than or equal to 3+4 (International Society of Urological Pathology grade group ≥2). A mixed-model logistic regression with institution and individuals as random effects was used to estimate overall PPVs. The variability of observed PPV of PI-RADS across imaging centers was described by using the median and interquartile range. Results The authors evaluated 3449 men (mean age, 65 years ± 8 [standard deviation]) with 5082 lesions. Biopsy results showed 1698 cancers with GS greater than or equal to 3+4 (International Society of Urological Pathology grade group ≥2) in 2082 men. Across all centers, the estimated PPV was 35% (95% confidence interval [CI]: 27%, 43%) for a PI-RADS score greater than or equal to 3 and 49% (95% CI: 40%, 58%) for a PI-RADS score greater than or equal to 4. The interquartile ranges of PPV at these same PI-RADS score thresholds were 27%-44% and 27%-48%, respectively. Conclusion The positive predictive value of the Prostate Imaging and Reporting Data System was low and varied widely across centers. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Milot in this issue.