Correction: Percutaneous hepatic melphalan perfusion: Single center experience of procedural characteristics, hemodynamic response, complications, and postoperative recovery.

[This corrects the article DOI: 10.1371/journal.pone.

Impact of IL-8 on survival after TARE in HCC: a comprehensive investigation and external validation from the SORAMIC trial.

Purpose: In the treatment of hepatocellular carcinoma (HCC) with transarterial radioembolization (TARE), identifying reliable biomarkers for predicting survival outcomes remains a critical challenge. We aimed to address this gap by investigating the significance of serum cytokines associated with inflammation as potential biomarkers for the selection of patients for TARE.

Methods: Our retrospective study involved 161 patients diagnosed with HCC who underwent Y90 radioembolization at our medical center between 2010 and 2020.

CT Texture Analysis of Perihilar Cholangiocarcinoma-Associations With Tumor Grading, Tumor Markers and Clinical Outcome.

Background: Texture analysis derived from computed tomography (CT) may provide clinically relevant imaging biomarkers associated with tumor histopathology. Perihilar cholangiocarcinoma is a malignant disease with an overall poor prognosis.

Aims: The present study sought to elucidate possible associations between texture features derived from CT images with grading, tumor markers, and survival in extrahepatic, perihilar cholangiocarcinomas tumors.

Interrater Variability of ML-Based CT-FFR in Patients without Obstructive CAD before TAVR: Influence of Image Quality, Coronary Artery Calcifications, and Location of Measurement.

CT-derived fractional flow reserve (CT-FFR) can improve the specificity of coronary CT-angiography (cCTA) for ruling out relevant coronary artery disease (CAD) prior to transcatheter aortic valve replacement (TAVR). However, little is known about the reproducibility of CT-FFR and the influence of diffuse coronary artery calcifications or segment location. The objective was to assess the reliability of machine-learning (ML)-based CT-FFR prior to TAVR in patients without obstructive CAD and to assess the influence of image quality, coronary artery calcium score (CAC), and the location of measurement within the coronary tree.