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Objectives: To evaluate the effectiveness of cryoablation compared to partial nephrectomy in patients with stage IA papillary and chromophobe renal cell carcinoma (pRCC; chRCC). Material and methods: The 2004–2016 National Cancer Database was queried for adult patients with stage IA pRCC or chRCC treated with cryoablation or partial nephrectomy. Patients receiving systemic therapy or radiotherapy, as well as those with bilateral RCC or prior malignant disease were excluded. Overall survival (OS) was assessed using Kaplan–Meier plots and Cox proportional hazard regression models. Nearest neighbor propensity matching (1:1 cryoablation:partial nephrectomy, stratified for pRCC and chRCC) was used to account for potential confounders. Results: A total of 11122 stage IA renal cell carcinoma patients were included (pRCC 8030; chRCC 3092). Cryoablation was performed in 607 (5.5%) patients, and partial nephrectomy in 10515 (94.5%) patients. A higher likelihood of cryoablation treatment was observed in older patients with non-private healthcare insurance, as well as in those with smaller diameter low-grade pRCC treated at non-academic centers in specific US geographic regions. After propensity score matching to account for confounders, there was no statistically significant difference in OS comparing cryoablation vs partial nephrectomy in patients with pRCC (HR = 1.3, 95% CI: 0.96–1.75, p = 0.09) and those with chRCC (HR = 1.38, 95% CI: 0.67–2.82, p = 0.38). Conclusion: After accounting for confounders, cryoablation, and partial nephrectomy demonstrated comparable OS in patients with stage IA papillary and chromophobe RCC. Cryoablation is a reasonable treatment alternative to partial nephrectomy for these histological RCC subtypes when radiologically suspected or diagnosed after biopsy. Critical relevance statement: Cryoablation might be considered as an upfront treatment alternative to partial nephrectomy in patients with papillary and chromophobe stage IA renal cell carcinoma, as both treatment approaches yield comparable oncological outcomes. Key Points: The utilization of cryoablation for stage IA papillary and chromophobe RCC increases. In the National Cancer Database, we found specific patterns of use of cryoablation. Cryoablation and partial nephrectomy demonstrate comparable outcomes after accounting for confounders. [ABSTRACT FROM AUTHOR]

Objectives: To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT). Material and methods: Patients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted. Following preprocessing and addressing the class imbalance, a ML algorithm based on extreme gradient boosting trees (XGB) was employed to predict renal tumor subtypes using 10-fold cross-validation. The evaluation was conducted using the multiclass area under the receiver operating characteristic curve (AUC). Algorithms were trained on data from one center and independently tested on data from the other center. Results: The training cohort comprised n = 297 patients (64.3% clear cell renal cell cancer [RCC], 13.5% papillary renal cell carcinoma (pRCC), 7.4% chromophobe RCC, 9.4% oncocytomas, and 5.4% angiomyolipomas (AML)), and the testing cohort n = 121 patients (56.2%/16.5%/3.3%/21.5%/2.5%). The XGB algorithm demonstrated a diagnostic performance of AUC = 0.81/0.64/0.8 for venous/arterial/combined contrast phase CT in the training cohort, and AUC = 0.75/0.67/0.75 in the independent testing cohort. In pairwise comparisons, the lowest diagnostic accuracy was evident for the identification of oncocytomas (AUC = 0.57–0.69), and the highest for the identification of AMLs (AUC = 0.9–0.94) Conclusion: Radiomic feature analyses can distinguish renal tumor subtypes on routinely acquired CTs, with oncocytomas being the hardest subtype to identify. Clinical relevance statement: Radiomic feature analyses yield robust results for renal tumor assessment on routine CTs. Although radiologists routinely rely on arterial phase CT for renal tumor assessment and operative planning, radiomic features derived from arterial phase did not improve the accuracy of renal tumor subtype identification in our cohort. Key Points: Radiomic feature analyses and ML algorithms can be utilized in routinely acquired CT studies from multiple imaging centers. Discrimination of renal tumor subtypes using CT-based radiomics and ML yields a moderate diagnostic performance (multiclass AUC = 0.75 in independent testing data). For renal tumor subtype discrimination using radiomic features, venous contrast phase CT yields the most pertinent imaging information, without evident diagnostic benefit of an added arterial contrast phase. [ABSTRACT FROM AUTHOR]

To assess epidemiology, clinical presentation, treatment and overall survival of adult patients with renal sarcomas, the 2004–2016 SEER and NCDB databases were queried for adult patients diagnosed with renal sarcoma, calculating average annual age-adjusted incidence rates (AAIR) and average annual percentage change (AAPC) as well as overall survival (OS). In n = 1279 included renal sarcoma patients, AAIR remained constant over the study period (average 0.53 cases/1million; AAPC = 0.7, p = 0.6). Leiomyosarcoma (AAIR 0.14 cases/1 million) and malignant rhabdoid tumors (0.06 cases/1 million) were most common. Sarcoma histiotypes demonstrated considerable heterogeneity regarding demographic and cancer-related variables. Patients presented with advanced local extent (T3 33.3%; T4 14.2%) or distant metastases (29.1%) and commonly underwent surgical resection (81.6%). Longer OS was independently associated with younger age, female sex, lower comorbidity index, low T stage, negative surgical margins, absence of tumor necrosis or distant metastases and leiomyosarcoma histiotype (multivariable p < 0.05 each). Treatment efficacy varied according to sarcoma histiotype (interaction p < 0.001). Accounting for 0.25% of renal malignancies, renal sarcomas include 43 histiotypes with distinct epidemiology, clinical presentation, outcomes and sensitivity to systemic therapy, thereby reflecting soft-tissue sarcoma behavior. Renal sarcoma treatment patterns follow recommendations by renal cancer guidelines with surgical resection as the cornerstone of therapy. [ABSTRACT FROM AUTHOR]

Copyright of Die Urologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to develop a standardized algorithm for gastroesophageal image acquisition and diagnostic assessment using real-time MRI. Patients with GERD symptoms undergoing real-time MRI of the esophagus and esophagogastric junction between 2015 and 2018 were included. A 10 ml bolus of pineapple juice served as an oral contrast agent. Patients performed Valsalva maneuver to provoke reflux and hiatal hernia. Systematic MRI assessment included visual presence of achalasia, fundoplication failure in patients with previous surgical fundoplication, gastroesophageal reflux, and hiatal hernia. A total of 184 patients (n = 92 female [50%], mean age 52.7 ± 15.8 years) completed MRI studies without adverse events at a mean examination time of 15 min. Gastroesophageal reflux was evident in n = 117 (63.6%), hiatal hernia in n = 95 (52.5%), and achalasia in 4 patients (2.2%). Hiatal hernia was observed more frequently in patients with reflux at rest (n = 67 vs. n = 6, p < 0.01) and during Valsalva maneuver (n = 87 vs. n = 8, p < 0.01). Real-time MRI visualized a morphologic correlate for recurring GERD symptoms in 20/22 patients (90%) after fundoplication procedure. In a large-scale single-center cohort of patients with GERD symptoms undergoing real-time MRI, visual correlates for clinical symptoms were evident in most cases. The proposed assessment algorithm could aid in wider-spread utilization of real-time MRI and provides a comprehensive approach to this novel imaging modality. [ABSTRACT FROM AUTHOR]

Background: There is growing evidence that partial nephrectomy (PN) and percutaneous cryoablation (PCA) yield comparable outcomes for patients with cT1a renal cell carcinoma (RCC), although the cost-effectiveness of both treatments still needs to be assessed. Purpose: To perform a cost-effectiveness analysis of PN and PCA for patients with cT1a RCC. Materials and methods: A decision analysis was created over a 5-year span from a healthcare payer's perspective computing expected costs and outcomes of PN and PCA in terms of quality-adjusted life-years (QALYs) and incremental cost-effectiveness (ICER). After each treatment, the following states were modelled using data from the recent literature: procedural complications, no evidence of disease (NED), local recurrence, metastases, and death from RCC- or non-RCC-related causes. Probabilistic and deterministic sensitivity analyses were performed. Results: PCA and PN yielded health benefits of 3.68 QALY and 3.67 QALY. Overall expected costs were $20,491 and $26,478 for PCA and PN. On probabilistic sensitivity analysis, PCA was more cost-effective than PN in 84.78% of Monte Carlo simulations. PCA was more cost-effective until its complication risk was at least 38% higher than PN. PCA was more cost-effective than PN when (i) PCAs annual local recurrence risk was < 3.5% higher than that of PN in absolute values; (ii) PCAs annual metastatic risk was < 1.0% higher than that of PN; or (iii) PCAs annual cancer-specific mortality risk < 0.65% higher than that of PN. PCA remained cost-effective until its procedural cost is above $13,875. Conclusion: PCA appears to be more cost-effective than PN for the treatment of cT1a RCC, although the currently available evidence is of limited quality. PCA may be the better treatment strategy in the majority of scenarios varying procedural complications, recurrence, metastatic risk, and RCC-mortality in clinically plausible ranges. Key Points: • For patients with cT1a RCCs, PCA yields a comparable health benefit at lower costs compared to PN, making PCA the dominant and therefore more cost-effective treatment strategy over PN. • PCA was more cost-effective than PN when (i) PCAs annual local recurrence risk was < 3.5% higher than PN in absolute values; (ii) PCAs annual metastatic risk was < 1.0% higher than PN; or (iii) PCAs annual cancer-specific mortality risk < 0.65% higher than PN. • PCA is more cost-effective than PN for the treatment of cT1a RCC, and it remained so in the majority of scenarios varying procedural complications, recurrence, metastatic risk, and RCC mortality. [ABSTRACT FROM AUTHOR]

Copyright of BHM Berg- und Hüttenmännische Monatshefte is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: To compare the use of coronary computed tomography angiography (CCTA) between academic and non-academic sites across Europe over the last decade. Methods: We analyzed a large multicenter registry (ESCR MR/CT Registry) of stable symptomatic patients who received CCTA 01/2010–01/2020 at 47 (22%) academic and 165 (78%) non-academic sites across 19 European countries. We compared image quality, radiation dose, contrast-media-related adverse events, patient characteristics, CCTA findings, and downstream testing between academic and non-academic sites. Results: Among 64,317 included patients (41% female; 60 ± 13 years), academic sites accounted for most cases in 2010–2014 (52%), while non-academic sites dominated in 2015–2020 (71%). Despite less contemporary technology, non-academic sites maintained low radiation doses (4.76 [2.46–6.85] mSv) with a 30% decline of high-dose scans (> 7 mSv) over time. Academic and non-academic sites both reported diagnostic image quality in 98% of cases and low rate of scan-related adverse events (0.4%). Academic and non-academic sites examined similar patient populations (41% females both; age: 61 ± 14 vs. 60 ± 12 years; pretest probability for obstructive CAD: low 21% vs. 23%, intermediate 73% vs. 72%, high 6% both, CAD prevalence on CCTA: 40% vs. 41%). Nevertheless, non-academic sites referred more patients to non-invasive ischemia testing (6.5% vs. 4.2%) and invasive coronary angiography/surgery (8.5% vs. 5.6%). Conclusions: Non-academic and academic sites provide safe, high-quality CCTA across Europe, essential to successfully implement the recently updated guidelines for the diagnosis and management of chronic coronary syndromes. However, despite examining similar populations with comparable CAD prevalence, non-academic sites tend to refer more patients to downstream testing. Key Points: • Smaller non-academic providers increasingly use CCTA to rule out obstructive coronary artery disease. • Non-academic and academic sites provide comparably safe, high-quality CCTA across Europe. • Compared to academic sites, non-academic sites tend to refer more patients to downstream testing. [ABSTRACT FROM AUTHOR]

Purpose: To assess the esophagogastric junction (EGJ) on real-time MRI and compare imaging parameters to EGJ morphology on high-resolution manometry (HRM). Methods: A total of 105 of 117 eligible patients who underwent real-time MRI and high-resolution manometry for GERD-like symptoms between 2015 and 2018 at a single center were retrospectively evaluated (male n = 57; female n = 48; mean age 52.5 ± 15.4 years). Real-time MRI was performed at a median investigation time of 15 min (1 frame/40 ms). On HRM, EGJ morphology was assessed according to the Chicago classification of esophageal motility disorders. Real-time MRI was performed at 3 T using highly undersampled radial fast low-angle shot acquisitions with NLINV image reconstruction. A 10 mL pineapple juice bolus served as oral contrast agent at supine position. Real-time MRI films of the EGJ were acquired during swallowing events and during Valsalva maneuver. Anatomic and functional MRI parameters were compared to EGJ morphology on HRM. Results: On HRM, n = 42 patients presented with EGJ type I (40.0%), n = 33 with EGJ type II (31.4%), and n = 30 with EGJ type III (28.6%). On real-time MRI, hiatal hernia was more common in patients with EGJ type III (66.7%) than in patients with EGJ type I (26.2%) and EGJ type II (30.3%; p < 0.001). Sliding hiatal hernia was more frequent in patients with EGJ type II (33.3%) than in patients with EGJ type III (16.7%) and EGJ type I (7.1%; p = 0.017). The mean esophagus–fundus angle of patients was 85 ± 31° at rest and increased to 101 ± 36° during Valsalva maneuver. Conclusion: Real-time MRI is a non-invasive imaging method for assessment of the esophagogastric junction. Real-time MRI can visualize dynamic changes of the EGJ during swallowing events. [ABSTRACT FROM AUTHOR]

Objectives: To assess imaging features of primary renal sarcomas in order to better discriminate them from non-sarcoma renal tumors. Methods: Adult patients diagnosed with renal sarcomas from 1995 to 2018 were included from 11 European tertiary referral centers (Germany, Belgium, Turkey). Renal sarcomas were 1:4 compared to patients with non-sarcoma renal tumors. CT/MRI findings were assessed using 21 predefined imaging features. A random forest model was trained to predict "renal sarcoma vs. non-sarcoma renal tumors" based on demographics and imaging features. Results: n = 34 renal sarcomas were included and compared to n = 136 non-sarcoma renal tumors. Renal sarcomas manifested in younger patients (median 55 vs. 67 years, p < 0.01) and were more complex (high RENAL score complexity 79.4% vs. 25.7%, p < 0.01). Renal sarcomas were larger (median diameter 108 vs. 43 mm, p < 0.01) with irregular shape and ill-defined margins, and more frequently demonstrated invasion of the renal vein or inferior vena cava, tumor necrosis, direct invasion of adjacent organs, and contact to renal artery or vein, compared to non-sarcoma renal tumors (p < 0.05, each). The random forest algorithm yielded a median AUC = 93.8% to predict renal sarcoma histology, with sensitivity, specificity, and positive predictive value of 90.4%, 76.5%, and 93.9%, respectively. Tumor diameter and RENAL score were the most relevant imaging features for renal sarcoma identification. Conclusion: Renal sarcomas are rare tumors commonly manifesting as large masses in young patients. A random forest model using demographics and imaging features shows good diagnostic accuracy for discrimination of renal sarcomas from non-sarcoma renal tumors, which might aid in clinical decision-making. Key Points: • Renal sarcomas commonly manifest in younger patients as large, complex renal masses. • Compared to non-sarcoma renal tumors, renal sarcomas more frequently demonstrated invasion of the renal vein or inferior vena cava, tumor necrosis, direct invasion of adjacent organs, and contact to renal artery or vein. • Using demographics and standardized imaging features, a random forest showed excellent diagnostic performance for discrimination of sarcoma vs. non-sarcoma renal tumors (AUC = 93.8%, sensitivity = 90.4%, specificity = 76.5%, and PPV = 93.9%). [ABSTRACT FROM AUTHOR]

Purpose: To assess the diagnostic potential of real-time MRI for assessment of hiatal hernias in patients with GERD-like symptoms compared to endoscopy.Material and Methods: One hundred eight patients with GERD-like symptoms were included in this observational cohort study between 2015 and 2017. Real-time MRI was performed at 3.0 Tesla with temporal resolution of 40 ms, dynamically visualizing the esophageal transport of a pineapple juice bolus, its passage through the gastroesophageal junction, and functional responses during Valsalva maneuver. Hernia detection on MRI and endoscopy was calculated using contingency tables with diagnosis of hernia on either modality as reference.Results: Of 108 patients, 107 underwent successful MRI without adverse events; 1 examination was aborted to inability to swallow pineapple juice in supine position. No perforation or acute bleeding occurred during endoscopy. Median examination time was 15 min. Eighty-five patients (79.4%) were diagnosed with hiatal hernia on either real-time MRI or endoscopy. Forty-six hernias were visible on both modalities. Seventeen hernias were evident exclusively on MRI, and 22 exclusively on endoscopy. Sixteen of the 63 MRI-detected hernias (25.4%) were detectable only during Valsalva maneuver, which were smaller compared to hernias at rest (median - 13.5 vs - 33.0 mm, p < 0.001). Diagnostic accuracy for hernia detection was comparable for MRI and endoscopy (sensitivity 74% vs 80%, p = 0.4223; specificity 100% vs 100%, p > 0.99).Conclusion: Real-time MRI is a fast and safe modality for assessment of the gastroesophageal junction, without radiation exposure or administration of gadolinium-based contrast media. Although MRI and endoscopy yield comparable diagnostic accuracy, dynamic MRI sequences are able to visualize hiatal hernias that were occult on static MRI sequences or endoscopy in a relevant number of cases.Key Points: • Real-time MRI is a safe and fast imaging modality for examination of the gastroesophageal junction, combining anatomical and functional information for enhanced detection of hiatal hernias. • Real-time MRI and endoscopy yield comparably high diagnostic accuracy: real-time MRI visualizes hiatal hernias that were occult on endoscopy in a relevant number of patients; however, several hiatal hernias detected on endoscopy were occult on real-time MRI. • There is clinical potential of real-time MR imaging in patients with GERD-like symptoms and equivocal findings on endoscopy or pH-metry, for anatomical visualization in patients planned for surgical intervention, or those with suspected fundoplication failures. [ABSTRACT FROM AUTHOR]

Purpose: To assess the diagnostic potential of dynamic real-time MRI for fundoplication failure in patients with persistent or recurrent GERD-like (gastroesophageal reflux disease) complaints.Material and Methods: Twenty-two consecutive patients (male n = 11; female n = 11; median age 59 years) with recurrent or persistent GERD-like symptom after fundoplication were enrolled between 2015 and 2017. Median duration of GERD-like symptoms was 21 months. Real-time MRI (3 Tesla) was performed at 40 ms temporal resolution using undersampled radial fast low-angle shot acquisitions with nonlinear inverse image reconstruction. MRI movies dynamically visualized bolus transit of pineapple juice through the gastroesophageal junction, position of the fundoplication wrap and recurring hernia or reflux during Valsalva maneuver. MRI results were compared to endoscopic findings.Results: Real-time MRI was successfully completed in all patients without adverse events (average examination time 15 min). Morphological correlates for GERD-like symptoms were evident in 20 patients (90.1%) with gastric reflux in 19 cases. Nine patients (40.1%) had wrap disruption and recurrent gastric hernia. Wrap migration or telescoping hernia was detected in nine patients (40.1%). One patient presented with continued reflux despite intact fundoplication wrap. Esophageal dysmotility with delayed bolus passage was observed in one case. On endoscopy, gastric hernia or wrap disruption was diagnosed in seven cases, and esophagitis or Barret's metaplasia in nine cases.Conclusion: Real-time MRI is a fast and safe modality for dynamic imaging after fundoplication, without radiation exposure or administration of gadolinium-based contrast media. In a relevant number of cases, real-time MRI reveals correlates for GERD-like symptoms.Key Points: • Real-time MRI reliably visualizes the gastroesophageal junction after fundoplication surgery. • Patients with recurring GERD-like symptoms have a high rate of morphological failure patterns that can be identified by real-time MRI. • Dynamic assessment of gastroesophageal junction by real-time MRI is a perspective diagnostic tool for detection of fundoplication failure. [ABSTRACT FROM AUTHOR]

Objectives: To assess the incidence of acute adverse events (AAEs) in gadolinium-enhanced cardiac magnetic resonance (CMR) imaging.Methods: Gadolinium-based contrast agent (GBCA)-enhanced CMR data from the multinational, multicenter European Society of Cardiovascular Radiology MRCT Registry was included. AAE severity was classified according to the American College of Radiology Manual on Contrast Media (mild, moderate, severe). Multivariable generalized linear mixed effect models were used to assess the likelihood of AAEs in various GBCA, adjusting for pharmacological stressor, main indications (i.e., suspected or known coronary artery disease or myocarditis), age, sex, and submitting center as a random effect.Results: In the study population of 72,839 GBCA-enhanced CMRs, a total of 260 AAEs were reported (0.36%), with a minority of severe AAEs (n = 24, 0.033%). Allergic-like AAEs were less likely than physiologic AAEs (29% versus 71%). Patients without pharmacological stress imaging had a lower AAE rate (0.22%) compared to stress imaging (0.75%), with the highest AAE rates for regadenoson (2.95%). AAE rates also varied by GBCA subtype (overall p < 0.001). There was significant interaction between GBCA and pharmacological stressor (interaction p = 0.025), with AAE rates ranging between 0 and 10% for certain GBCA/stressor combinations. There was further marginal evidence that higher GBCA volume was associated with higher AAE incidence (OR = 1.02, p = 0.05).Conclusion: GBCA-enhanced CMR imaging demonstrates low AAE rates comparable to those of other body regions. AAE likelihood correlates with GBCA subtype, pharmacological stressor, and imaging indication. Intravenous fluid administration in patients with cardiac impairment might contribute to these findings.Key Points: • Acute adverse event rates in cardiac magnetic resonance (CMR) imaging with gadolinium-based contrast agents (GBCAs) are low (0.36%), especially for severe adverse events (0.033%). • Mild and moderate adverse events are more frequent during stress CMR imaging. • Physiologic AAEs are more common than allergic AAEs in CMR imaging. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to evaluate socioeconomic discrepancies in current treatment approaches and survival trends among patients with intrahepatic cholangiocarcinoma (ICC). Methods: The 2004–2015 National Cancer Database was retrospectively analyzed for histopathologically proven ICC. Treatment predictors were evaluated using multinomial logistic regression and overall survival via multivariable Cox models. Results: Overall, 12,837 ICC patients were included. Multiple factors influenced treatment allocation, including age, education, comorbidities, cancer stage, grade, treatment center, and US state region (multivariable p < 0.05). The highest surgery rates were observed in the Middle Atlantic (28.7%) and lowest rates were observed in the Mountain States (18.4%). Decreased ICC treatment likelihood was observed for male African Americans with Medicaid insurance and those with low income (multivariable p < 0.05). Socioeconomic treatment discrepancies translated into decreased overall survival for patients of male sex (vs. female; hazard ratio [HR] 1.21, 95% confidence interval [CI] 1.16–1.26, p < 0.001), with low income (< $37,999 vs. ≥ $63,000 annually; HR 1.07, 95% CI 1.01–1.14, p = 0.032), and with Medicaid insurance (vs. private insurance; HR 1.13, 95% CI 1.04–1.23, p = 0.006). Both surgical and non-surgical ICC management showed increased survival compared with no treatment, with the longest survival for surgery (5-year overall survival for surgery, 33.5%; interventional oncology, 11.8%; radiation oncology/chemotherapy, 4.4%; no treatment, 3.3%). Among non-surgically treated patients, interventional oncology yielded the longest survival versus radiation oncology/chemotherapy (HR 0.73, 95% CI 0.65–0.82, p < 0.001). Conclusions: ICC treatment allocation and outcome demonstrated a marked variation depending on socioeconomic status, demography, cancer factors, and US geography. Healthcare providers should address these discrepancies by providing surgery and interventional oncology as first-line treatment to all eligible patients, with special attention to the vulnerable populations identified in this study. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate whether post-contrast cone-beam breast CT (CBBCT) alone is comparable to the current standard of combined pre- and post-contrast CBBCT regarding diagnostic accuracy and superior regarding radiation exposure.Material and Methods: This study included 49 women (61 breasts) with median age 57.9 years and BI-RADS 4/5 lesions diagnosed on mammography/ultrasound in density type c/d breasts. Two radiologists rated post-contrast CBBCT and pre- and post-contrast CBBCT with subtraction images on the BI-RADS scale separately for calculation of inter- and intra-observer agreement and in consensus for diagnostic accuracy assessment. Sensitivity, specificity, and area under the curve (AUC) were compared via McNemar test and DeLong method, respectively. Subtraction imaging misregistration were measured from 1 (no artifacts) to 4 (artifacts with width > 4 mm).Results: A total of 100 lesion (51 malignant; 6 high risk; 43 benign) were included. AUC, sensitivity, and specificity showed no significant differences comparing post-contrast CBBCT alone versus pre- and post-contrast CBBCT (AUC 0.84 vs. 0.83, p = 0.643; sensitivity 0.89 vs. 0.85, p = 0.158; specificity 0.73 vs. 0.76, p = 0.655). Inter- and intra-observer agreement was excellent (intra-class correlation coefficient ICC = 0.76, ICC = 0.83, respectively). Radiation dose was significantly lower for post-contrast CBBCT alone versus pre- and post-contrast CBBCT (median average glandular radiation dose 5.9 mGy vs. 11.7 mGy, p < 0.001). High-degree misregistrations were evident in the majority of subtraction images (level 1/2/3/4 16.9%/27.1%/16.9%/39%), in particular for bilateral exams (3.2%/29.2%/8.3%/58.3%).Conclusion: Diagnostic accuracy of post-contrast CBBCT alone is comparable to pre- and post-contrast CBBCT in type c/d breasts, while yielding a significant twofold radiation dose reduction.Key Points: • The diagnostic accuracy of post-contrast CBBCT alone is comparable to dual acquisition of pre- and post-contrast CBBCT. • Acquisition of the post-contrast CBBCT scan alone reduces radiation exposure compared to pre- and post-contrast CBBCT, thus countering one of the main limitations of CBBCT. • High-degree misregistration artifacts limit the interpretation of subtraction images from pre- and post-contrast CBBCT studies. [ABSTRACT FROM AUTHOR]

Purpose: To compare utilization and effectiveness of radiofrequency ablation (RFA) and surgical resection for hepatocellular carcinoma (HCC).Methods: The 2004-2015 United States National Cancer Database was queried for HCC patients treated by RFA and surgical resection. Patients were 1:1 propensity score matched. Duration of hospital stay, unplanned readmission rates, and overall survival (OS) were compared in the matched cohort via multivariable regression models.Results: Eighteen thousand two hundred ninety-six patients were included (RFA, n = 8211; surgical resection, n = 10,085). RFA was more likely in young male whites with high degree of hepatic fibrosis, high bilirubin levels, high INR, and multifocal HCC; resection was more likely in those with private insurance, high income, high cancer grade and stage, and larger HCC. RFA rates varied between 32.3% (East South Central) and 58.5% (New England). Post-treatment outcomes were superior for RFA versus resection regarding duration of hospital stay (median 1 vs. 5d, p < 0.001), 30-day unplanned hospital readmission rates (3.1% vs. 4.5%, p < 0.001), and 30-/90-day mortality (0% vs. 4.6%/8%, p < 0.001). Overall survival was comparable for RFA and resection for severe hepatic fibrosis/cirrhosis (5-year OS 37.3% vs. 39.4%, p = 0.07), for patients > 65 years old (5-year OS 21.9% vs. 26.5%, p = 0.47), and for HCC < 15 mm (5-year OS 49.7% vs. 52.3%, p = 0.78). OS in the full cohort was superior for surgical resection (5-year OS 29.9% vs. 45.7%, p < 0.01).Conclusion: RFA for HCC shows substantial variation by geography, socioeconomic factors, liver function, and tumor extent. RFA offers superior post-treatment outcomes versus surgical resection and may be an alternative for older patients with cirrhosis and/or small HCC.Key Points: • Duration of hospital stay, unplanned readmissions, and 30-/90-day mortality are lower for RFA versus surgical resection. • RFA and surgical resection show similar survival in severe hepatic fibrosis. • In HCC < 15 mm, RFA and surgical resection yield similar survival. [ABSTRACT FROM AUTHOR]

Purpose: To review the published evidence on cone-beam breast computed tomography (CBBCT) and summarize its diagnostic accuracy for breast lesion assessment.Materials and Methods: A systematic literature search was conducted using the EMBASE, MEDLINE and CENTRAL libraries. Studies were included if reporting sensitivity and specificity for discrimination of benign and malignant breast lesions via breast CT. Sensitivity and specificity were jointly modeled using a bivariate approach calculating summary areas under the receiver-operating characteristics curve (AUC). All analyses were separately performed for non-contrast and contrast-enhanced CBBCT (NC-CBBCT, CE-CBBCT).Results: A total of 362 studies were screened, of which 6 with 559 patients were included. All studies were conducted between 2015 and 2018 and evaluated female participants. Four of six studies included dense and very dense breasts with a high proportion of microcalcifications. For NC-CBBCT, pooled sensitivity was 0.789 (95% CI: 0.66-0.89) and pooled specificity was 0.697 (95% CI: 0.471-0.851), both showing considerable significant between-study heterogeneity (I2 = 89.4%, I2 = 94.7%, both p < 0.001). Partial AUC for NC-CBBCT was 0.817. For CE-CBBCT, pooled sensitivity was 0.899 (95% CI: 0.785-0.956) and pooled specificity was 0.788 (95% CI: 0.709-0.85), both exhibiting non-significant moderate between-study heterogeneity (I2 = 57.3%, p = 0.0527; I2 = 53.1%, p = 0.0738). Partial AUC for CE-CBBCT was 0.869.Conclusion: The evidence available for CBBCT tends to show superior diagnostic performance for CE-CBBCT over NC-CBBCT regarding sensitivity, specificity and partial AUC. Diagnostic accuracy of CE-CBBCT was numerically comparable to that of breast MRI with meta-analyses reporting sensitivity of 0.9 and specificity of 0.72.Key Points: • CE-CBBCT rather than NC-CBBCT should be used for assessment of breast lesions for its higher diagnostic accuracy. • CE-CBBCT diagnostic performance was comparable to published results on breast MRI, thus qualifying CE-CBBCT as a potential imaging alternative for patients with MRI contraindications. [ABSTRACT FROM AUTHOR]

Purpose: To compare partial nephrectomy (PN), radiofrequency ablation (RFA), cryoablation (CRA) and microwave ablation (MWA) regarding oncologic, perioperative and functional outcomes.Material and Methods: The MEDLINE, EMBASE and COCHRANE libraries were searched for studies comparing PN, RFA, CRA or MWA and reporting on any-cause or cancer-specific mortality, local recurrence, complications or renal function. Network meta-analyses were performed.Results: Forty-seven studies with 24,077 patients were included. Patients receiving RFA, CRA or MWA were older and had more comorbidities compared with PN. All-cause mortality was higher for CRA and RFA compared with PN (incidence rate ratio IRR = 2.58, IRR = 2.58, p < 0.001, respectively). No significant differences in cancer-specific mortality were evident. Local recurrence was higher for CRA, RFA and MWA compared with PN (IRR = 4.13, IRR = 1.79, IRR = 2.52, p < 0.05 respectively). A decline in renal function was less pronounced after RFA versus PN, CRA and MWA (mean difference in GFR MD = 6.49; MD = 5.82; MD = 10.89, p < 0.05 respectively).Conclusion: Higher overall survival and local control of PN compared with ablative therapies did not translate into significantly better cancer-specific mortality. Most studies carried a high risk of bias by selecting younger and healthier patients for PN, which may drive superior survival and local control. Physicians should be aware of the lack of high-quality evidence and the potential benefits of ablative techniques for certain patients, including a superior complication profile and renal function preservation.Key Points: • Patients selected for ablation of small renal masses are older and have more comorbidities compared with those undergoing partial nephrectomy. • Partial nephrectomy yields lower all-cause mortality, which is probably biased by patient selection and does not translate into prolonged cancer-free survival. • The decline of renal function is smallest after radiofrequency ablation for small renal masses. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the diagnostic accuracy of contrast-enhanced (CE) cone-beam breast computed tomography (CBBCT) in dense breast tissue and compare it to non-contrast (NC) CBBCT, mammography (MG) and magnetic resonance imaging (MRI).Methods: This prospective institutional review board-approved study included 41 women (52 breasts) with American College of Radiology (ACR) density types c or d and Breast Imaging Reporting and Data System (BI-RADS) 4 or 5 assessments in MG or ultrasound (US). Imaging modalities were independently evaluated by two blinded readers.Results: A total of 100 lesions (51 malignant, 6 high-risk, and 43 benign) were identified. For readers 1/2, respectively, and p values comparing CE-CBBCT to other modalities: diagnostic accuracy (AUC) for CE-CBBCT was 0.83/0.77, for MRI 0.88/0.89 (p = 0.2272/0.002), for NC-CBBCT 0.73/0.66 (p = 0.038/ 0.0186) and for MG 0.69/0.64 (p = 0.081/0.0207). CE-CBBCT sensitivity (0.88/0.78) was 37-39% higher in comparison to MG (0.49/0.41, p < 0.001 both) but inferior to MRI (0.98/0.96, p = 0.0253/0.0027). CE-CBBCT specificity (0.71/0.71) was numerically higher compared to MRI (0.61/0.69, p = 0.0956/0.7389).Conclusions: CBBCT diagnostic performance varied with the respective reader and experience. CE-CBBCT improved AUC and sensitivity in comparison to MG and NC-CBBCT, and was comparable to MRI in dense breast tissue. In tendency, specificity was higher for CE-CBBCT than MRI.Key Points: • CE-CBBCT diagnostic accuracy (AUC) was comparable to MRI in dense breasts. • CE-CBBCT improved sensitivity and AUC in comparison to MG and NC-CBBCT. • CE-CBBCT has inferior sensitivity but higher specificity than MRI. • CE-CBBCT is a potential imaging alternative for patients with MRI contraindications. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate survival of patients with localized T1a clear cell renal cell carcinoma (ccRCC) who received cryosurgery or thermal ablation compared to deferred therapy.Materials and Methods: We included 733 patients with histopathologically confirmed localized T1a ccRCC who either received cryosurgery (n = 315) or thermal ablation (n = 155), as well as patients who deferred therapy (n = 263) from the 2000-2013 Surveillance, Epidemiology, and End Results Program urinary cancer file. Cox proportional hazard models were used to compare cancer-specific survival (CSS) across subgroups. Sensitivity analyses were conducted to assess potential unmeasured confounding by comorbidities.Results: Patients treated with cryosurgery and thermal ablation had a statistically significant CSS benefit compared to those who deferred therapy (cryosurgery HR 0.25, 95% CI 0.14-0.45, p < 0.001; thermal ablation HR 0.27, 95% CI 0.13-0.55, p < 0.001, after adjustment for age at diagnosis, tumor grade, and size). There was no significant difference in CSS comparing cryosurgery to thermal ablation (HR 1.03, 95% CI 0.45-2.3, p = 0.95, after adjustment for age at diagnosis, tumor grade, and size). These results proved robust upon sensitivity analyses: After adjustment for comorbidities with varying prevalence assumptions, the corrected hazard ratio (cHR) of cryosurgery versus deferred therapy ranged between HR 0.09 and 0.68.Conclusion: Local ablative techniques provide relevant survival benefit and are preferable alternatives over deferred therapy. Cryosurgery and thermal ablation yield comparable outcomes.Level Of Evidence: 2b according to the Oxford Centre for evidence-based medicine levels of evidence. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the impact of breast density on the diagnostic accuracy of non-contrast cone-beam breast computed tomography (CBBCT) in comparison to mammography for the detection of breast masses.Methods: A retrospective study was conducted from August 2015 to July 2016. Fifty-nine patients (65 breasts, 112 lesions) with BI-RADS, 5th edition 4 or 5 assessment in mammography and/or ultrasound of the breast received an additional non-contrast CBBCT. Independent double blind reading by two radiologists was performed for mammography and CBBCT imaging. Sensitivity, specificity and AUC were compared between the modalities.Results: Breast lesions were histologically examined in 85 of 112 lesions (76%). The overall sensitivity for CBBCT (reader 1: 91%, reader 2: 88%) was higher than in mammography (both: 68%, p<0.001), and also for the high-density group (p<0.05). The specificity and AUC was higher for mammography in comparison to CBBCT (p<0.05 and p<0.001). The interobserver agreement (ICC) between the readers was 90% (95% CI: 86-93%) for mammography and 87% (95% CI: 82-91%) for CBBCT.Conclusions: Compared with two-view mammography, non-contrast CBBCT has higher sensitivity, lower specificity, and lower AUC for breast mass detection in both high and low density breasts.Key Points: • Overall sensitivity for non-contrast CBBCT ranged between 88%-91%. • Sensitivity was higher for CBBCT than mammography in both density types (p<0.001). • Specificity was higher for mammography than CBBCT in both density types (p<0.05). • AUC was larger for mammography than CBBCT in both density types (p<0.001). [ABSTRACT FROM AUTHOR]

Homogeneous as well as heterogeneous photocatalysts that are able to oxidize the azide anion with low competitive singlet oxygen quantum yields are used to generate azidyl radicals. These radicals add to electron-rich as well as electron-poor (Michael acceptors) alkenes, and carbon radicals are formed regioselectively. Trapping with triplet oxygen (type I photooxygenation) is diffusion controlled, and the initially formed peroxy radicals are reduced with regeneration of the photocatalyst. Fluorescence quenching studies reveal rapid photoinduced electron transfer in the first catalysis step. The lack of rearrangement products in the bicyclic terpene series (pinenes, limonene) accounts for rapid subsequent oxygen trapping and back electron transfer steps. The 1,2-azidohydroperoxidation enables synthesis of 1,2-azidoalcohols and 1,2-aminoalcohols by different reduction protocols. Substrate modification and combination of type II photooxygenation with electron transfer photocatalysis allows the synthesis of 1-amino-2,3-diols and 2-amino-1,3-diols. [ABSTRACT FROM AUTHOR]

Reply to Letter to the Editor: How to document adverse reactions induced by gadolinium-based contrast agents? The ACR recognizes the ambiguity in current AAE reporting, including the terms "allergy-like", "anaphylactoid", and "idiosyncratic" for AAEs that "manifest similarly to true allergic reactions seen with other drugs and allergens", although an antigen-antibody response might not always be identified. However, rephrasing "allergy-like" and "physiologic" AAEs as "type A/B reactions" is not recommended by ACR and might be less clear in clinical practice, AAE management, and communication with non-radiological colleagues. [Extracted from the article]

Objectives: To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated confidence to promote clinical applicability.The training dataset included solid renal tumor patients from two tertiary centers undergoing surgical resection and receiving CT in the corticomedullary or nephrogenic contrast media (CM) phase. Manual tumor segmentation was performed on all axial CT slices serving as reference standard for automatic segmentations. Independent testing was performed on the publicly available KiTS 2019 dataset. Ensembles of neural networks (ENN, DeepLabV3) were used for automatic renal tumor segmentation, and their performance was quantified with DICE score. ENN average foreground entropy measured segmentation confidence (binary: successful segmentation with DICE score > 0.8 versus inadequate segmentation ≤ 0.8).N = 639/n = 210 patients were included in the training and independent test dataset. Datasets were comparable regarding age and sex (p > 0.05), while renal tumors in the training dataset were larger and more frequently benign (p < 0.01). In the internal test dataset, the ENN model yielded a median DICE score = 0.84 (IQR: 0.62–0.97, corticomedullary) and 0.86 (IQR: 0.77–0.96, nephrogenic CM phase), and the segmentation confidence an AUC = 0.89 (sensitivity = 0.86; specificity = 0.77). In the independent test dataset, the ENN model achieved a median DICE score = 0.84 (IQR: 0.71–0.97, corticomedullary CM phase); and segmentation confidence an accuracy = 0.84 (sensitivity = 0.86 and specificity = 0.81). ENN segmentations were visualized with color-coded voxelwise tumor probabilities and thresholds superimposed on clinical CT images.ENN-based renal tumor segmentation robustly performs in external test data and might aid in renal tumor classification and treatment planning.Ensembles of neural networks (ENN) models could automatically segment renal tumors on routine CTs, enabling and standardizing downstream image analyses and treatment planning. Providing confidence measures and segmentation overlays on images can lower the threshold for clinical ENN implementation.Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds.ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset.ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds.ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset.ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.Materials and methods: To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated confidence to promote clinical applicability.The training dataset included solid renal tumor patients from two tertiary centers undergoing surgical resection and receiving CT in the corticomedullary or nephrogenic contrast media (CM) phase. Manual tumor segmentation was performed on all axial CT slices serving as reference standard for automatic segmentations. Independent testing was performed on the publicly available KiTS 2019 dataset. Ensembles of neural networks (ENN, DeepLabV3) were used for automatic renal tumor segmentation, and their performance was quantified with DICE score. ENN average foreground entropy measured segmentation confidence (binary: successful segmentation with DICE score > 0.8 versus inadequate segmentation ≤ 0.8).N = 639/n = 210 patients were included in the training and independent test dataset. Datasets were comparable regarding age and sex (p > 0.05), while renal tumors in the training dataset were larger and more frequently benign (p < 0.01). In the internal test dataset, the ENN model yielded a median DICE score = 0.84 (IQR: 0.62–0.97, corticomedullary) and 0.86 (IQR: 0.77–0.96, nephrogenic CM phase), and the segmentation confidence an AUC = 0.89 (sensitivity = 0.86; specificity = 0.77). In the independent test dataset, the ENN model achieved a median DICE score = 0.84 (IQR: 0.71–0.97, corticomedullary CM phase); and segmentation confidence an accuracy = 0.84 (sensitivity = 0.86 and specificity = 0.81). ENN segmentations were visualized with color-coded voxelwise tumor probabilities and thresholds superimposed on clinical CT images.ENN-based renal tumor segmentation robustly performs in external test data and might aid in renal tumor classification and treatment planning.Ensembles of neural networks (ENN) models could automatically segment renal tumors on routine CTs, enabling and standardizing downstream image analyses and treatment planning. Providing confidence measures and segmentation overlays on images can lower the threshold for clinical ENN implementation.Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds.ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset.ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds.ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset.ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.Results: To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated confidence to promote clinical applicability.The training dataset included solid renal tumor patients from two tertiary centers undergoing surgical resection and receiving CT in the corticomedullary or nephrogenic contrast media (CM) phase. Manual tumor segmentation was performed on all axial CT slices serving as reference standard for automatic segmentations. Independent testing was performed on the publicly available KiTS 2019 dataset. Ensembles of neural networks (ENN, DeepLabV3) were used for automatic renal tumor segmentation, and their performance was quantified with DICE score. ENN average foreground entropy measured segmentation confidence (binary: successful segmentation with DICE score > 0.8 versus inadequate segmentation ≤ 0.8).N = 639/n = 210 patients were included in the training and independent test dataset. Datasets were comparable regarding age and sex (p > 0.05), while renal tumors in the training dataset were larger and more frequently benign (p < 0.01). In the internal test dataset, the ENN model yielded a median DICE score = 0.84 (IQR: 0.62–0.97, corticomedullary) and 0.86 (IQR: 0.77–0.96, nephrogenic CM phase), and the segmentation confidence an AUC = 0.89 (sensitivity = 0.86; specificity = 0.77). In the independent test dataset, the ENN model achieved a median DICE score = 0.84 (IQR: 0.71–0.97, corticomedullary CM phase); and segmentation confidence an accuracy = 0.84 (sensitivity = 0.86 and specificity = 0.81). ENN segmentations were visualized with color-coded voxelwise tumor probabilities and thresholds superimposed on clinical CT images.ENN-based renal tumor segmentation robustly performs in external test data and might aid in renal tumor classification and treatment planning.Ensembles of neural networks (ENN) models could automatically segment renal tumors on routine CTs, enabling and standardizing downstream image analyses and treatment planning. Providing confidence measures and segmentation overlays on images can lower the threshold for clinical ENN implementation.Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds.ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset.ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds.ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset.ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.Conclusions: To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated confidence to promote clinical applicability.The training dataset included solid renal tumor patients from two tertiary centers undergoing surgical resection and receiving CT in the corticomedullary or nephrogenic contrast media (CM) phase. Manual tumor segmentation was performed on all axial CT slices serving as reference standard for automatic segmentations. Independent testing was performed on the publicly available KiTS 2019 dataset. Ensembles of neural networks (ENN, DeepLabV3) were used for automatic renal tumor segmentation, and their performance was quantified with DICE score. ENN average foreground entropy measured segmentation confidence (binary: successful segmentation with DICE score > 0.8 versus inadequate segmentation ≤ 0.8).N = 639/n = 210 patients were included in the training and independent test dataset. Datasets were comparable regarding age and sex (p > 0.05), while renal tumors in the training dataset were larger and more frequently benign (p < 0.01). In the internal test dataset, the ENN model yielded a median DICE score = 0.84 (IQR: 0.62–0.97, corticomedullary) and 0.86 (IQR: 0.77–0.96, nephrogenic CM phase), and the segmentation confidence an AUC = 0.89 (sensitivity = 0.86; specificity = 0.77). In the independent test dataset, the ENN model achieved a median DICE score = 0.84 (IQR: 0.71–0.97, corticomedullary CM phase); and segmentation confidence an accuracy = 0.84 (sensitivity = 0.86 and specificity = 0.81). ENN segmentations were visualized with color-coded voxelwise tumor probabilities and thresholds superimposed on clinical CT images.ENN-based renal tumor segmentation robustly performs in external test data and might aid in renal tumor classification and treatment planning.Ensembles of neural networks (ENN) models could automatically segment renal tumors on routine CTs, enabling and standardizing downstream image analyses and treatment planning. Providing confidence measures and segmentation overlays on images can lower the threshold for clinical ENN implementation.Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds.ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset.ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds.ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset.ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.Clinical relevance statement: To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated confidence to promote clinical applicability.The training dataset included solid renal tumor patients from two tertiary centers undergoing surgical resection and receiving CT in the corticomedullary or nephrogenic contrast media (CM) phase. Manual tumor segmentation was performed on all axial CT slices serving as reference standard for automatic segmentations. Independent testing was performed on the publicly available KiTS 2019 dataset. Ensembles of neural networks (ENN, DeepLabV3) were used for automatic renal tumor segmentation, and their performance was quantified with DICE score. ENN average foreground entropy measured segmentation confidence (binary: successful segmentation with DICE score > 0.8 versus inadequate segmentation ≤ 0.8).N = 639/n = 210 patients were included in the training and independent test dataset. Datasets were comparable regarding age and sex (p > 0.05), while renal tumors in the training dataset were larger and more frequently benign (p < 0.01). In the internal test dataset, the ENN model yielded a median DICE score = 0.84 (IQR: 0.62–0.97, corticomedullary) and 0.86 (IQR: 0.77–0.96, nephrogenic CM phase), and the segmentation confidence an AUC = 0.89 (sensitivity = 0.86; specificity = 0.77). In the independent test dataset, the ENN model achieved a median DICE score = 0.84 (IQR: 0.71–0.97, corticomedullary CM phase); and segmentation confidence an accuracy = 0.84 (sensitivity = 0.86 and specificity = 0.81). ENN segmentations were visualized with color-coded voxelwise tumor probabilities and thresholds superimposed on clinical CT images.ENN-based renal tumor segmentation robustly performs in external test data and might aid in renal tumor classification and treatment planning.Ensembles of neural networks (ENN) models could automatically segment renal tumors on routine CTs, enabling and standardizing downstream image analyses and treatment planning. Providing confidence measures and segmentation overlays on images can lower the threshold for clinical ENN implementation.Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds.ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset.ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds.ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset.ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.Key Points: To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated confidence to promote clinical applicability.The training dataset included solid renal tumor patients from two tertiary centers undergoing surgical resection and receiving CT in the corticomedullary or nephrogenic contrast media (CM) phase. Manual tumor segmentation was performed on all axial CT slices serving as reference standard for automatic segmentations. Independent testing was performed on the publicly available KiTS 2019 dataset. Ensembles of neural networks (ENN, DeepLabV3) were used for automatic renal tumor segmentation, and their performance was quantified with DICE score. ENN average foreground entropy measured segmentation confidence (binary: successful segmentation with DICE score > 0.8 versus inadequate segmentation ≤ 0.8).N = 639/n = 210 patients were included in the training and independent test dataset. Datasets were comparable regarding age and sex (p > 0.05), while renal tumors in the training dataset were larger and more frequently benign (p < 0.01). In the internal test dataset, the ENN model yielded a median DICE score = 0.84 (IQR: 0.62–0.97, corticomedullary) and 0.86 (IQR: 0.77–0.96, nephrogenic CM phase), and the segmentation confidence an AUC = 0.89 (sensitivity = 0.86; specificity = 0.77). In the independent test dataset, the ENN model achieved a median DICE score = 0.84 (IQR: 0.71–0.97, corticomedullary CM phase); and segmentation confidence an accuracy = 0.84 (sensitivity = 0.86 and specificity = 0.81). ENN segmentations were visualized with color-coded voxelwise tumor probabilities and thresholds superimposed on clinical CT images.ENN-based renal tumor segmentation robustly performs in external test data and might aid in renal tumor classification and treatment planning.Ensembles of neural networks (ENN) models could automatically segment renal tumors on routine CTs, enabling and standardizing downstream image analyses and treatment planning. Providing confidence measures and segmentation overlays on images can lower the threshold for clinical ENN implementation.Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds.ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset.ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds.ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset.ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations. [ABSTRACT FROM AUTHOR]