Uveal melanoma is one of the most common primary intraocular malignancies that accounts for about 85% of all ocular melanomas. The pathophysiology of uveal melanoma is distinct from cutaneous melanoma and has separate tumor profiles. The management of uveal melanoma is largely dependent on the presence of metastases, which confers a poor prognosis with a one-year survival reaching only 15%. Although a better understanding of tumor biology has led to the development of novel pharmacologic agents, there is increasing demand for minimally invasive management of hepatic uveal melanoma metastases. Multiple studies have already summarized the systemic therapeutic options available for metastatic uveal melanoma. This review covers the current research for the most prevalent locoregional treatment options for metastatic uveal melanoma including percutaneous hepatic perfusion, immunoembolization, chemoembolization, thermal ablation, and radioembolization.

<!-- *** Custom HTML *** --> We assessed interventional radiologists’ task-based image quality preferences for two- and three-dimensional images obtained with a complementary metal–oxide semiconductor (CMOS) flat-panel detector versus a hydrogenated amorphous silicon (a-Si:H) flat-panel detector. CMOS and a-Si:H detectors were implemented on identical mobile C-arms to acquire radiographic, fluoroscopic, and cone-beam computed tomography (CBCT) images of cadavers undergoing simulated interventional procedures using low- and high-dose settings. Images from both systems were displayed side by side on calibrated, diagnostic-quality displays, and three interventional radiologists evaluated task performance relevant to each image and ranked their preferences based on visibility of pertinent anatomy and interventional devices. Overall, CMOS images were preferred in fluoroscopy (p = 0.002) and CBCT (p = 0.004), at low-dose settings (p = 0.001), and for tasks associated with high levels of spatial resolution [e.g., fine anatomical details (p = 0.006) and assessment of interventional devices (p = 0.015)]. No significant difference was found for fluoroscopic imaging tasks emphasizing temporal resolution (p = 0.072), for radiography tasks (p = 0.825), when using high-dose settings (p = 0.360), or tasks involving general anatomy (p = 0.174). The image quality preferences are consistent with reported technical advantages of CMOS regarding finer pixel size and reduced electronic noise.

Our objective was to develop a predictive model using a machine learning signature to identify patients at high risk of relapse or death after treatment for HPV-positive oropharyngeal carcinoma. Pre-treatment variables of 450 patients with HPV-positive oropharyngeal carcinoma treated with a curative intent comprised clinical items, imaging parameters and histological findings. The events considered were progression or residual disease after treatment, the recurrent disease after a disease-free interval and death. The endpoints were the prediction of events and progression-free survival. After feature Z-score normalisation and selection, random forest classifier models were trained. The best models were evaluated on recall, the F-score, and the ROC AUC metric. The clinical relevance of the best prediction model was evaluated using Kaplan-Meier analysis with a log-rank test. The best random forest model predicted the 5-year risk of relapse-free survival with a recall of 79.1%, an F1-score of 81.08%, and an AUC of the ROC curve of 0.89. The models performed poorly for the prediction of specific events of progression only, recurrence only or death only. The clinical relevance of the model was validated with a 5-year relapse-free survival of high-risk patients versus low-risk patients of 23.5% and 80%, respectively (p < 0.0001). Patients with HPV-driven oropharyngeal carcinoma at high risk of relapse-free survival could be identified with a predictive machine learning model using patient data before treatment.