Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. Th...
INTRODUCTION: This study aims to use machine learning to conduct in-depth analysis of key factors affecting the recurrence of HCC patients with high preoperative systemic immune-inflammation index (SII) levels after receiving ablation treatment, and ...
The effectiveness of ultrasonography (USG) in liver cancer screening is partly constrained by the operator's expertise. We aimed to develop and evaluate an AI-assisted system for detecting and classifying focal liver lesions (FLLs) from USG images. T...
BACKGROUND & AIMS: The risk of hepatocellular carcinoma (HCC) and hepatic decompensation persists after hepatitis B surface antigen (HBsAg) seroclearance. This study aimed to develop and validate a machine learning model to predict the risk of liver-...
Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. ...
Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
Aug 28, 2024
In cancer research, oncogenesis can be affected by modulating the deubiquitination pathway. Ubiquitination regulates proteins post-translationally in variety of physiological processes. The Otubain Subfamily includes OTUB1 (ovarian tumor-associated p...
This work proposed a liver cancer classification scheme that includes Preprocessing, Feature extraction, and classification stages. The source images are pre-processed using Gaussian filtering. For segmentation, this work proposes a LUV transformatio...
Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
Aug 26, 2024
PURPOSE: We sought to develop an artificial intelligence (AI)-based model to predict early recurrence (ER) after curative-intent resection of neuroendocrine liver metastases (NELMs).
BACKGROUND: Artificial intelligence-based models might improve patient selection for liver transplantation in hepatocellular carcinoma. The objective of the current study was to develop artificial intelligence-based deep learning models and determine...
INTRODUCTION: No study has yet investigated the minimum amount of data required for deep learning-based liver contouring. Therefore, this study aimed to investigate the feasibility of automated liver contouring using limited data.