WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data.

The WAW-TACE dataset contains baseline multiphase abdominal CT images from 233 treatment-naive patients with hepatocellular carcinoma treated with transarterial chemoembolization and includes 377 handcrafted liver tumor masks, automated segmentations of multiple internal organs, extracted radiomics features, and corresponding extensive clinical data. The dataset can be accessed at .

Thin-film-based optical fiber interferometric sensor on the fiber tip for endovascular surgical procedures.

Objective: Endovascular surgery requires accurate measurement of parameters such as pressure, temperature, and biomarkers within vessels for real-time tissue response monitoring and ensuring targeted therapeutic interventions. However, the availability of small tip-based sensors capable of precise application, for example, navigating an aneurysm's lumen, is limited. With their capabilities for real-time analysis, flexibility, and biocompatibility, optical fiber sensors (OFS) hold promise in addressing this need.

Double-layer optical fiber interferometer with bio-layer-modified reflector for label-free biosensing of inflammatory proteins.

This work discusses label-free biosensing application of a double-layer optical fiber interferometer where the second layer tailors the reflection conditions at the external plain and supports changes in reflected optical spectrum when a bio-layer binds to it. The double-layer nanostructure consists of precisely tailored thin films, i.e.

A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation.

Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machine learning approach utilizing radiomics features from multiple organ volumes of interest (VOIs) to predict TACE outcomes for 252 HCC patients. Unlike conventional radiomics models requiring laborious manual segmentation limited to tumoral regions, our approach captures information comprehensively across various VOIs using a fully automated, pretrained deep learning model applied to pre-TACE CT images.