The present study evaluated safety, efficacy, and prognostic factors for 90 Y-yttrium microsphere radioembolization of unresectable liver metastases from breast cancer. Methods: Eighty-one patients were treated with radioembolization. Acute toxicity was monitored through daily physical examination and serum tests until 3 d after radioembolization; late toxicity was evaluated until 12 wk after radioembolization. Overall survival and response according to 18 F-FDG PET (.30% decrease of tracer uptake) and CA15-3 serum level (any decline) were recorded. Pretherapeutic characteristics, including pretreatment history, liver function tests, and PET/CT parameters, were assessed by univariate and subsequent multivariate Cox regression for predicting patient survival. Results: A toxicity grade of 3 or more based on clinical symptoms, bilirubin, ulcer, pancreatitis, ascites, or radioembolization-induced liver disease occurred in 10% or less of patients. Two patients eventually died from radioembolization-induced liver disease. Sequential lobar treatment and absence of prior angiosuppressive therapy were both associated with a lower rate of serious adverse events. On the basis of PET/CA15-3 criteria, 52/61% of patients responded to treatment. Median overall survival after radioembolization was 35 wk (interquartile range, 41 wk). Pretherapeutic tumor burden of the liver greater than 50% or more (P , 0.001; hazard ratio, 5.67; 95% confidence interval, 2.41-13.34) and a transaminase toxicity grade of 2 or more (P 5 0.009; hazard ratio, 2.15; 95% confidence interval, 1.21-3.80) independently predicted short survival. Conclusion: Radioembolization for breast cancer liver metastases shows encouraging local response rates with low incidence of serious adverse events, especially in those patients with sequential lobar treatment or without prior angiosuppressive therapy. High hepatic tumor burden and liver transaminase levels at baseline indicate poor outcome.

Pre-therapeutic baseline bilirubin and CHE levels, extrahepatic disease and hepatic tumor burden are associated with patient survival after RE. Such parameters may be used to improve patient selection for RE of primary or metastatic liver tumors.

Our objective was to predict the outcome of Y radioembolization in patients with intrahepatic tumors from pretherapeutic baseline parameters and to identify predictive variables using a machine-learning approach based on random survival forests. In this retrospective study, 366 patients with primary ( = 92) or secondary ( = 274) liver tumors who had received Y radioembolization were analyzed. A random survival forest was trained to predict individual risk from baseline values of cholinesterase, bilirubin, type of primary tumor, age at radioembolization, hepatic tumor burden, presence of extrahepatic disease, and sex. The predictive importance of each baseline parameter was determined using the minimal-depth concept, and the partial dependency of predicted risk on the continuous variables bilirubin level and cholinesterase level was determined. Median overall survival was 11.4 mo (95% confidence interval, 9.7-14.2 mo), with 228 deaths occurring during the observation period. The random-survival-forest analysis identified baseline cholinesterase and bilirubin as the most important variables (forest-averaged lowest minimal depth, 1.2 and 1.5, respectively), followed by the type of primary tumor (1.7), age (2.4), tumor burden (2.8), and presence of extrahepatic disease (3.5). Sex had the highest forest-averaged minimal depth (5.5), indicating little predictive value. Baseline bilirubin levels above 1.5 mg/dL were associated with a steep increase in predicted mortality. Similarly, cholinesterase levels below 7.5 U predicted a strong increase in mortality. The trained random survival forest achieved a concordance index of 0.657, with an SE of 0.02, comparable to the concordance index of 0.652 and SE of 0.02 for a previously published Cox proportional hazards model. Random survival forests are a simple and straightforward machine-learning approach for prediction of overall survival. The predictive performance of the trained model was similar to a previously published Cox regression model. The model has revealed a strong predictive value for baseline cholinesterase and bilirubin levels with a highly nonlinear influence for each parameter.

CEUS is an useful method which can be additionally used to clinically differentiate between malignant and benign renal lesions. CEUS shows a comparable sensitivity, specificity, PPV and NPV to MRI. In daily clinical routine, patients with contraindications for other imaging modalities can particularly benefit using this method.

Trans-arterial radioembolization (TARE) is increasingly evaluated for unresectable intrahepatic cholangiocarcinoma (ICC). Not all ICC patients benefit equally well from TARE. Therefore, we sought to evaluate variables predicting progression-free survival (PFS) and overall survival (OS). Patients with non-resectable ICC underwent TARE and were treated with 90Y resin microspheres. Baseline characteristics, biochemical/clinical toxicities, and response were examined for impact on PFS and OS. A total of 103 treatments were administered to 73 patients without major complications or toxicity. Mean OS was 18.9 months (95% confidence intervals (CI); 13.9–23.9 months). Mean and median PFS were 10.1 months (95% CI; 7.9–12.2) and 6.4 months (95% CI; 5.20–7.61), respectively. Median OS and PFS were significantly prolonged in patients with baseline cholinesterase (CHE) ≥ 4.62 kU/L (OS: 14.0 vs. 5.5 months; PFS: 6.9 vs. 3.2 months; p < 0.001). Patients with a tumor burden ≤ 25% had a significantly longer OS (15.2 vs. 6.6 months; p = 0.036). Median PFS was significantly longer for patients with multiple TARE cycles (24.4 vs. 5.8 months; p = 0.04). TARE is a considerable and safe option for unresectable ICC. CA-19-9, CHE, and tumor burden have predictive value for survival in patients treated with TARE. Multiple TARE treatments might further improve survival; this has to be confirmed by further studies.

Purpose To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [18F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma. Material and methods At suspected tumor progression, MRI and [18F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [18F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier’s performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments. Results In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77–0.93 (sensitivity 0.91, 95% CI 0.81–0.97; specificity 0.71, 95% CI 0.44–0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72–0.9), 0.81 for [18F]-FET-PET (95% CI 0.7–0.89), and 0.81 for expert consensus (95% CI 0.7–0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [18F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities. Conclusion Automated, joint image analysis of [18F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas.

Ultrasound is a common and established imaging method for the initial characterization of renal lesions. The widespread used Bosniak classification (I-IV) classifies renal lesions in five individual groups using contrast-enhanced computer tomography (CE-CT), magnetic resonance imaging (MRI) and/or contrast-enhanced ultrasound (CEUS) imaging criteria. For complex pathologies, CEUS/MRI image fusion is a novel imaging technique for the differentiation of benign and malignant renal lesions. Compared to CE-CT and MRI alone, ultrasound image fusion offers the additional possibility of being a real-time imaging technique that can be used together with other cross-sectional imaging techniques.This article describes the newest possibilities of image fusion with CEUS and MRI in detection and characterization of unclear renal lesions.

• After multiple chemotherapies, many patients are still eligible for radioembolization (RE). • RE can achieve meaningful survival in patients with chemorefractory liver-predominant metastatic colorectal cancer (mCRC). • Tumour responsiveness to prior systemic treatments is a significant determinant of overall survival (OS) after RE. • Radioembolization in patients with a good performance status is generally well tolerated.