AIMC Topic: Carcinoma, Hepatocellular

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Constructing a neural network model based on tumor-infiltrating lymphocytes (TILs) to predict the survival of hepatocellular carcinoma patients.

PeerJ
BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide, and early pathological diagnosis is crucial for formulating treatment plans. Despite the widespread attention to pathology in the treatment of HCC patients,...

Comprehensive assessment of the role of spectral data pre-processing in spectroscopy-based liquid biopsy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Spectroscopic data often contain artifacts or noise related to the sample characteristics, instrumental variations, or experimental design flaws. Therefore, classifying the raw data is not recommended and might lead to biased results. Nevertheless, m...

Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study.

International journal of molecular sciences
Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to...

Machine learning based radiomic models outperform clinical biomarkers in predicting outcomes after immunotherapy for hepatocellular carcinoma.

Journal of hepatology
BACKGROUND & AIMS: Atezolizumab plus bevacizumab (A/B) is a first-line therapy for unresectable hepatocellular carcinoma (HCC). Only a small proportion of patients respond to treatment. This study integrated radiomic and clinical data derived from ro...

Transformer-based deep learning for accurate detection of multiple base modifications using single molecule real-time sequencing.

Communications biology
We had previously reported a convolutional neural network (CNN) based approach, called the holistic kinetic model (HK model 1), for detecting 5-methylcytosine (5mC) by single molecule real-time sequencing (Pacific Biosciences). In this study, we cons...

Integrated multi-omics analysis and machine learning refine molecular subtypes and clinical outcome for hepatocellular carcinoma.

Hereditas
The high morbidity and mortality of hepatocellular carcinoma (HCC) impose a substantial economic burden on patients' families and society, and the majority of HCC patients are detected at advanced stages and experience poor therapeutic outcomes, wher...

Integrating single-cell RNA sequencing, WGCNA, and machine learning to identify key biomarkers in hepatocellular carcinoma.

Scientific reports
The microarray and single-cell RNA-sequencing (scRNA-seq) datasets of hepatocellular carcinoma (HCC) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (W...

Construction of an artificially intelligent model for accurate detection of HCC by integrating clinical, radiological, and peripheral immunological features.

International journal of surgery (London, England)
BACKGROUND: Integrating comprehensive information on hepatocellular carcinoma (HCC) is essential to improve its early detection. We aimed to develop a model with multimodal features (MMF) using artificial intelligence (AI) approaches to enhance the p...

Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study.

BMC medical imaging
BACKGROUND: Microvascular invasion (MVI) is an important risk factor for early postoperative recurrence of hepatocellular carcinoma (HCC). Based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance ...