Promoting the use of the PI-QUAL score for prostate MRI quality: results from the ESOR Nicholas Gourtsoyiannis teaching fellowship

OBJECTIVES: The Prostate Imaging Quality (PI-QUAL) score is a new metric to evaluate the diagnostic quality of multiparametric magnetic resonance imaging (MRI) of the prostate. This study assesses the impact of an intervention, namely a prostate MRI quality training lecture, on the participant's ability to apply PI-QUAL. METHODS: Sixteen participants (radiologists, urologists, physicists, and computer scientists) of varying experience in reviewing diagnostic prostate MRI all assessed the image quality of ten examinations from different vendors and machines. Then, they attended a dedicated lecture followed by a hands-on workshop on MRI quality assessment using the PI-QUAL score. Five scans assessed by the participants were evaluated in the workshop using the PI-QUAL score for teaching purposes. After the course, the same participants evaluated the image quality of a new set of ten scans applying the PI-QUAL score. Results were assessed using receiver operating characteristic analysis. The reference standard was the PI-QUAL score assessed by one of the developers of PI-QUAL. RESULTS: There was a significant improvement in average area under the curve for the evaluation of image quality from baseline (0.59 [95 % confidence intervals: 0.50-0.66]) to post-teaching (0.96 [0.92-0.98]), an improvement of 0.37 [0.21-0.41] (p < 0.001). CONCLUSIONS: A teaching course (dedicated lecture + hands-on workshop) on PI-QUAL significantly improved the application of this scoring system to assess the quality of prostate MRI examinations. KEY POINTS: • A significant improvement in the application of PI-QUAL for the assessment of prostate MR image quality was observed after an educational intervention. • Appropriate training on image quality can be delivered to those involved in the acquisition and interpretation of prostate MRI. • Further investigation will be needed to understand the impact on improving the acquisition of high-quality diagnostic prostate MR examinations

Is perfect the enemy of good? Weighing the evidence for biparametric MRI in prostate cancer

The role of multiparametric MRI in diagnosis, staging and treatment planning for prostate cancer is well established. However, there remain several challenges to widespread adoption. One such challenge is the duration and cost of the examination. Abbreviated exams omitting contrast-enhanced sequences may help address this challenge. In this review, we will discuss the rationale for biparametric MRI for detection and characterization of clinically significant prostate cancer prior to biopsy and synthesize the published literature. We will weigh up the advantages and disadvantages to this approach and lay out a conceptual cost/benefit analysis regarding adoption of biparametric MRI. </jats:p

Risk and predictors of ipilimumab-associated cardiac adverse events among patients treated for melanoma: A national cohort analysis.

e14592 Background: Ipilimumab is a CTLA-4 inhibitor widely used to treat advanced melanoma. While ipilimumab-induced cardiac immune-related adverse events (irAE) have been reported, there is a paucity of data from large cohorts. We investigated the risk and predictors of ipilimumab-associated cardiac irAE in a national cohort of patients with cutaneous melanoma. Methods: Using SEER-Medicare linked data, we compared the risk of cardiac irAE between patients treated with ipilimumab with or without concomitant treatment for cutaneous melanoma and controls not treated with ipilimumab following a primary diagnosis of cutaneous melanoma. We excluded patients ≤65 years and patients with cardiac comorbidities diagnosed within one year prior to the initiation of melanoma treatment. The primary endpoint was the incidence of at least one cardiac irAE after ipilimumab initiation including acute pericarditis, myocarditis, cardiomyopathy, conduction disorders, cardiac dysthymias, acute heart failure, and takotsubo syndrome. To estimate the risk of cardiac irAE, we conducted a multivariable competing-risk analysis adjusting for death of any cause within one year of treatment as a competing event. Then, we constructed a stepwise logistic regression to assess the predictors of having at least one cardiac irAE within one year of ipilimumab initiation. Subgroup analysis was conducted among patients who received ipilimumab only. The models were adjusted for patient demographics, disease stage, Charlson comorbidity index (CCI), history of hypertension, autoimmune disease, end stage renal disease (ESRD), chronic anticoagulant, and steroid use. Results: The cohort included 715 patients treated with ipilimumab and 22,070 controls. In the ipilimumab arm, 23.4% had metastatic disease, 9.5% had a history of autoimmune disease, and 2.2% had CCI≥2. The incidence rates of cardiac irAE among patients who received ipilimumab and among the control group were 23.3 and 13.6 per 1,000 person-years, respectively. We found that patients who received ipilimumab had a higher risk of cardiac irAE compared to controls (adjusted hazard ratio 1.87; 95%CI 1.50-2.32; p &lt; 0.001). In addition to ipilimumab treatment, other predictors of cardiac irAE included male gender, older age, patients with metastatic disease, history of autoimmune disease, hypertension, ESRD, anticoagulant use, and CCI≥2. The predictors of cardiac irAE were also consistent in the subgroup analysis of patients who received ipilimumab only. Conclusions: Patients who received ipilimumab with or without concomitant treatment for cutaneous melanoma had a higher risk for cardiac irAE. Predictors of cardiac irAE help tailor therapy according to patients’ risk profiles. </jats:p

Domain adaptation for segmentation of critical structures for prostate cancer therapy

Preoperative assessment of the proximity of critical structures to the tumors is crucial in avoiding unnecessary damage during prostate cancer treatment. A patient-specific 3D anatomical model of those structures, namely the neurovascular bundles (NVB) and the external urethral sphincters (EUS), can enable physicians to perform such assessments intuitively. As a crucial step to generate a patient-specific anatomical model from preoperative MRI in a clinical routine, we propose a multi-class automatic segmentation based on an anisotropic convolutional network. Our specific challenge is to train the network model on a unique source dataset only available at a single clinical site and deploy it to another target site without sharing the original images or labels. As network models trained on data from a single source suffer from quality loss due to the domain shift, we propose a semi-supervised domain adaptation (DA) method to refine the model’s performance in the target domain. Our DA method combines transfer learning and uncertainty guided self-learning based on deep ensembles. Experiments on the segmentation of the prostate, NVB, and EUS, show significant performance gain with the combination of those techniques compared to pure TL and the combination of TL with simple self-learning ( p < 0.005 for all structures using a Wilcoxon’s signed-rank test). Results on a different task and data (Pancreas CT segmentation) demonstrate our method’s generic application capabilities. Our method has the advantage that it does not require any further data from the source domain, unlike the majority of recent domain adaptation strategies. This makes our method suitable for clinical applications, where the sharing of patient data is restricted.OVGU-Publikationsfonds 202

Domain adaptation for segmentation of critical structures for prostate cancer therapy

AbstractPreoperative assessment of the proximity of critical structures to the tumors is crucial in avoiding unnecessary damage during prostate cancer treatment. A patient-specific 3D anatomical model of those structures, namely the neurovascular bundles (NVB) and the external urethral sphincters (EUS), can enable physicians to perform such assessments intuitively. As a crucial step to generate a patient-specific anatomical model from preoperative MRI in a clinical routine, we propose a multi-class automatic segmentation based on an anisotropic convolutional network. Our specific challenge is to train the network model on a unique source dataset only available at a single clinical site and deploy it to another target site without sharing the original images or labels. As network models trained on data from a single source suffer from quality loss due to the domain shift, we propose a semi-supervised domain adaptation (DA) method to refine the model’s performance in the target domain. Our DA method combines transfer learning and uncertainty guided self-learning based on deep ensembles. Experiments on the segmentation of the prostate, NVB, and EUS, show significant performance gain with the combination of those techniques compared to pure TL and the combination of TL with simple self-learning ({p}<0.005 p &lt; 0.005 for all structures using a Wilcoxon’s signed-rank test). Results on a different task and data (Pancreas CT segmentation) demonstrate our method’s generic application capabilities. Our method has the advantage that it does not require any further data from the source domain, unlike the majority of recent domain adaptation strategies. This makes our method suitable for clinical applications, where the sharing of patient data is restricted.</jats:p

Geographic Variability, Time Trends and Association of Preoperative Magnetic Resonance Imaging with Surgical Outcomes for Elderly United States Men with Prostate Cancer: A Surveillance, Epidemiology, and End Results-Medicare Analysis

PURPOSE: Our goal was to assess patterns of adoption and population-level outcomes of prostate magnetic resonance imaging (MRI) and association with surgical outcomes across a sample of U.S. elderly. MATERIALS AND METHODS: This population-based retrospective study used Surveillance Epidemiology, and End Results-Medicare linked data from 2003-2016 to identify men receiving prostatectomy for prostate cancer. We characterized the proportion of men receiving preoperative MRI in each year and in each hospital referral region (HRR). A 2-stage instrumental variable analysis was performed to assess the association of preoperative MRI with margin status, surgical complications and further cancer-directed therapies. RESULTS: A total of 19,369 men received prostatectomy in 72 HRRs; the mean age was 70.2 years (SD 3.2). The proportion of men receiving a preoperative MRI increased from 2.9% to 28.2% over the study period and ranged from 0.0% to 28.8% in the different HRRs. In our instrumental variable analysis, preoperative MRI was associated with lower odds of positive surgical margin (OR 0.84, 95% CI 0.72-0.97, p=0.01) lower odds of blood transfusions at 30 and 90 days (OR 0.56, 95% CI 0.38-0.83, p=0.003 and OR 0.58, 95% CI 0.41-0.84, p=0.004) but higher odds of further treatments (OR 1.49, 95% CI 1.32-1.70, p <0.001). CONCLUSIONS: Given that a minority of men receive presurgical MRIs with marked geographic variability, the association of MRI with lower odds of positive surgical margin suggests that efforts to support the dissemination of prostate MRI may improve surgical outcomes-but may come with a tendency for more resource-intensive cancer care overall

Impact of COVID-19 pandemic on ambulatory urologic oncology surgeries

INTRODUCTION: Robot-assisted laparoscopic prostatectomy (RALP) and transurethral resection of bladder tumor (TURBT) are two common surgeries for prostate and bladder cancer. We aim to assess the trends in the site of care for RALP and TURBT before and after the COVID outbreak. MATERIALS AND METHODS: We identified adults who underwent RALP and TURBT within the California Healthcare Cost and Utilization Project State Inpatient Database and the State Ambulatory Surgery Database between 2018 and 2020. Multivariable analysis and spline analysis with a knot at COVID outbreak were performed to investigate the time trend and factors associated with ambulatory RALP and TURBT. RESULTS: Among 17,386 RALPs, 6,774 (39.0%) were ambulatory. Among 25,070 TURBTs, 21,573 (86.0%) were ambulatory. Pre-COVID, 33.5% of RALP and 85.3% and TURBT were ambulatory, which increased to 53.8% and 88.0% post-COVID (both p \u3c 0.001). In multivariable model, RALP and TURBT performed after outbreak in March 2020 were more likely ambulatory (OR 2.31, p \u3c 0.0001; OR 1.25, p \u3c 0.0001). There was an overall increasing trend in use of ambulatory RALP both pre- and post-COVID, with no significant change of trend at the time of outbreak (p = 0.642). TURBT exhibited an increased shift towards ambulatory sites post-COVID (p \u3c 0.0001). CONCLUSIONS: We found a shift towards ambulatory RALP and TURBT following COVID outbreak. There was a large increase in ambulatory RALP post-COVID, but the trend of change was not significantly different pre- and post-COVID - possibly due to a pre-existing trend towards ambulatory RALP which predated the pandemic