Purpose: Postsustained virologic response (SVR) screening following clinical guidelines does not address individual risk of hepatocellular carcinoma (HCC). Our aim is to provide tailored screening for patients using machine learning to predict HCC incidence after SVR.
Methods: Using clinical data from 1,028 SVR patients, we developed an HCC prediction model using a random survival forest (RSF).
Background: This study aims to identify biomarkers for treatment response of atezolizumab plus bevacizumab (Atezo+Bev) in patients with hepatocellular carcinoma (HCC).
Methods: 96 patients who received Atezo+Bev or lenvatinib as a first-line systemic therapy were enrolled as the training group after propensity score matching (PSM), and 42 patients treated with Atezo+Bev were enrolled as the validation group. 17 serum cytokines were measured by Luminex multiplex assay at the start of treatment.
Highly sensitive and selective imaging of human-borne volatile organic compounds (VOCs) enables an intuitive understanding of their concentrations and release sites. While multi-VOC imaging methods have the potential to facilitate step-by-step metabolic tracking and improve disease screening accuracy, no such system currently exists. In this study, we achieved simultaneous imaging of ethanol (EtOH) and acetaldehyde (AcH), the starting molecule and an intermediate metabolite of alcohol metabolism, using a multiwavelength VOC imaging system.
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