Introduction: Extrahepatic spread is reported as a prognostic factor in patients with advanced hepatocellular carcinoma (HCC) receiving systemic therapy. However, clinical studies have reported conflicting results for the clinical impact of the pattern of tumor progression during treatment and the role of new extrahepatic metastases in length of survival. Objective: To evaluate the impact of extrahepatic metastases on survival in patients with HCC treated with sorafenib or with a combination of sorafenib and selective internal radiation treatment (SIRT). Methods: SORAMIC is a randomized, controlled trial comprising diagnostic, local ablation, and palliative cohorts. In the palliative cohort, patients not eligible for transarterial chemoembolization (TACE) were randomized 11:10 to SIRT plus sorafenib (SIRT + sorafenib) or sorafenib alone. This exploratory subanalysis evaluated the impact of extrahepatic metastases on survival. Results: In the intent-to-treat cohort, 216 patients were randomized to SIRT + sorafenib and 208 to sorafenib alone. Seventeen patients with distant organ metastases (bone, n = 11; adrenal glands, n = 5; peritoneum, n = 1) and 262 without distant metastases at study entry were analyzed in this substudy. Patients with (Group A) and without (Group B) distant organ metastases at study entry presented with a median survival of 11.3 and 14.8 months, respectively (p = 0.2807). During follow-up of patients with no organ metastases at baseline, extrahepatic disease progression occurred in 50 patients (19.1%). No statistically significant difference in survival was observed between patients without extrahepatic progression and those with new extrahepatic disease during treatment (14.8 vs. 14.9 months; p = 0.6483). Development of new pulmonary metastases during treatment significantly shortened median survival (7.6 vs. 15.0 months, p = 0.0060). Conclusions: This subanalysis of the SORAMIC trial suggests that in patients with liver-dominant advanced HCC, metastases to distant organs with the exception of pulmonary metastases do not in general exert a negative impact on patient prognosis. The choice of palliative treatment should incorporate a personalized analysis of the pattern of tumor distribution.

Purpose: To explore the potential correlation between baseline interleukin (IL) values and overall survival or objective response in patients with hepatocellular carcinoma (HCC) receiving sorafenib.Methods: A subset of patients with HCC undergoing sorafenib monotherapy within a prospective multicenter phase II trial (SORAMIC, sorafenib treatment alone vs. combined with Y90 radioembolization) underwent baseline IL-6 and IL-8 assessment before treatment initiation. In this exploratory post hoc analysis, the best cut-off points for baseline IL-6 and IL-8 values predicting overall survival (OS) were evaluated, as well as correlation with the objective response.Results: Forty-seven patients (43 male) with a median OS of 13.8 months were analyzed. Cut-off values of 8.58 and 57.9 pg/mL most effectively predicted overall survival for IL-6 and IL-8, respectively. Patients with high IL-6 (HR, 4.1 [1.9–8.9],p < 0.001) and IL-8 (HR, 2.4 [1.2–4.7],p = 0.009) had significantly shorter overall survival than patients with low IL values. Multivariate analysis confirmed IL-6 (HR, 2.99 [1.22–7.3],p = 0.017) and IL-8 (HR, 2.19 [1.02–4.7],p = 0.044) as independent predictors of OS. Baseline IL-6 and IL-8 with respective cut-off values predicted objective response rates according to mRECIST in a subset of 42 patients with follow-up imaging available (IL-6, 46.6% vs. 19.2%,p = 0.007; IL-8, 50.0% vs. 17.4%,p = 0.011).Conclusion: IL-6 and IL-8 baseline values predicted outcomes of sorafenib-treated patients in this well-characterized prospective cohort of the SORAMIC trial. We suggest that the respective cut-off values might serve for validation in larger cohorts, potentially offering guidance for improved patient selection.

Summary: Aims: To investigate the prognostic value of baseline imaging features for overall survival (OS) and liver decompensation (LD) in patients with hepatocellular carcinoma (HCC). Design: Patients with advanced HCC from the SORAMIC trial were evaluated in this post hoc analysis. Several radiological imaging features were collected from baseline computed tomography (CT) and magnetic resonance imaging (MRI) imaging, besides clinical values. The prognostic value of these features for OS and LD (grade 2 bilirubin increase) was quantified with univariate Cox proportional hazard models and multivariate Least Absolute Shrinkage and Selection Operator (LASSO) regression. Results: Three hundred and seventy-six patients were included in this study. The treatment arm was not correlated with OS. LASSO showed satellite lesions, atypical HCC, peritumoral arterial enhancement, larger tumour size, higher albumin–bilirubin (ALBI) score, liver–spleen ratio <1.5, ascites, pleural effusion and higher bilirubin values were predictors of worse OS, and higher relative liver enhancement, smooth margin and capsule were associated with better OS. LASSO analysis for LD showed satellite lesions, peritumoral hypointensity in hepatobiliary phase, high ALBI score, higher bilirubin values and ascites were predictors of LD, while randomisation to sorafenib arm was associated with lower LD. Conclusions: Imaging features showing aggressive tumour biology and poor liver function, in addition to clinical parameters, can serve as imaging biomarkers for OS and LD in patients receiving sorafenib and selective internal radiation therapy for HCC.

The seminal vesicles are paired organs of the male reproductive tract, which produce and secrete seminal fluid. Although congenital anomalies of seminal vesicles are usually asymptomatic, they may lead to various urogenital symptoms, including infertility. Due to their embryologic relationship with other urogenital organs, congenital anomalies of seminal vesicles may accompany other urinary or genital anomalies. Congenital anomalies of seminal vesicles include agenesis, hypoplasia, duplication, fusion, and cyst. These anomalies can be diagnosed with various imaging techniques. The main purpose of this article is to summarise imaging findings and clinical importance of congenital anomalies of seminal vesicles with images of some rare and previously unreported anomalies.

Objectives: Optimizing a machine learning (ML) pipeline for radiomics analysis involves numerous choices in data set composition, preprocessing, and model selection. Objective identification of the optimal setup is complicated by correlated features, interdependency structures, and a multitude of available ML algorithms. Therefore, we present a radiomics-based benchmarking framework to optimize a comprehensive ML pipeline for the prediction of overall survival. This study is conducted on an image set of patients with hepatic metastases of colorectal cancer, for which radiomics features of the whole liver and of metastases from computed tomography images were calculated. A mixed model approach was used to find the optimal pipeline configuration and to identify the added prognostic value of radiomics features. Materials and Methods: In this study, a large-scale ML benchmark pipeline consisting of preprocessing, feature selection, dimensionality reduction, hyperparameter optimization, and training of different models was developed for radiomics-based survival analysis. Portal-venous computed tomography imaging data from a previous prospective randomized trial evaluating radioembolization of liver metastases of colorectal cancer were quantitatively accessible through a radiomics approach. One thousand two hundred eighteen radiomics features of hepatic metastases and the whole liver were calculated, and 19 clinical parameters (age, sex, laboratory values, and treatment) were available for each patient. Three ML algorithms—a regression model with elastic net regularization (glmnet), a random survival forest (RSF), and a gradient tree-boosting technique (xgboost)—were evaluated for 5 combinations of clinical data, tumor radiomics, and whole-liver features. Hyperparameter optimization and model evaluation were optimized toward the performance metric integrated Brier score via nested cross-validation. To address dependency structures in the benchmark setup, a mixed-model approach was developed to compare ML and data configurations and to identify the best-performing model. Results: Within our radiomics-based benchmark experiment, 60 ML pipeline variations were evaluated on clinical data and radiomics features from 491 patients. Descriptive analysis of the benchmark results showed a preference for RSF-based pipelines, especially for the combination of clinical data with radiomics features. This observation was supported by the quantitative analysis via a linear mixed model approach, computed to differentiate the effect of data sets and pipeline configurations on the resulting performance. This revealed the RSF pipelines to consistently perform similar or better than glmnet and xgboost. Further, for the RSF, there was no significantly better-performing pipeline composition regarding the sort of preprocessing or hyperparameter optimization. Conclusions: Our study introduces a benchmark framework for radiomics-based survival analysis, aimed at identifying the optimal settings with respect to different radiomics data sources and various ML pipeline variations, including preprocessing techniques and learning algorithms. A suitable analysis tool for the benchmark results is provided via a mixed model approach, which showed for our study on patients with intrahepatic liver metastases, that radiomics features captured the patients' clinical situation in a manner comparable to the provided information solely from clinical parameters. However, we did not observe a relevant additional prognostic value obtained by these radiomics features.

MRI guidance offers better lesion targeting for microwave ablation of liver lesions with higher soft-tissue contrast, as well as the possibility of real-time thermometry. This study aims to evaluate the correlation of real-time MR thermometry-predicted lesion volume with the ablation zone in postprocedural first-day images. This single-center retrospective analysis evaluated prospectively included patients who underwent MRI-guided microwave ablation with real-time thermometry between December 2020 and July 2023. All procedures were performed under general anesthesia on a 1.5 T MRI scanner. Real-time thermometry data were acquired using multi-slice gradient-echo echoplanar imaging sequences, and thermal dose maps (CEM43 of 240 min as a threshold) were created. The volume of tissue exposed to a lethal thermal dose in MR thermometry (thermal dose) was compared with the ablation zone volume in portal phase T1w MRI on the postprocedural first day using the Pearson correlation test, and visual quantitative assessment by radiologists was performed to evaluate the similarity of shapes and volumes. Out of 30 patients with 33 lesions with thermometry images, six (18.1%) lesions were excluded because of artifacts limiting interpretation of thermal dose volume. Twenty-four patients with 27 lesions (20 male, age 63.1 ± 9.1 years) were evaluated for the volume correlation. The volume of thermal dose-predicted lesions and the postprocedural first-day ablation zones showed a strong correlation (R = 0.89, p <0.001). Similarly, visual similarity of molecular resonance thermometry-predicted shape and the ablation zone shape was graded as perfect in 23 (85.1%) lesions. Real-time thermal dose-predicted volumes show very good correlation with the ablation zone volumes in images obtained 1 day after the procedure, which could reduce the local recurrence rates with the possibility of re-ablating lesions within the same procedure. Heat-based ablation is an established treatment for liver tumors; however, there is a considerable rate of incomplete treatment because of the lack of real-time visualization of the treated area during treatment. Our results show that MRI-guided ablation enables the visualization of the treatment area in real-time with high accuracy using a special technique of MR thermometry in patients with liver tumors.