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Carcinoma, Hepatocellular

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Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy.

Frontiers in immunology
BACKGROUND: Hepatocellular carcinoma (LIHC) poses a significant health challenge worldwide, primarily due to late-stage diagnosis and the limited effectiveness of current therapies. Cancer stem cells are known to play a role in tumor development, met...

Opioid growth factor receptor overexpression exerts anti-hepatocellular carcinoma effects by activating P16 and P21 to inhibit proliferation and migration of HepG2 cells.

Folia histochemica et cytobiologica
INTRODUCTION: Hepatocellular carcinoma (HCC) is the sixth most common type of cancer and the second leading cause of cancer death worldwide [19]. Opioid growth factor (OGF) has been shown to exhibit antitumour potential, binding to OGF receptor (OGFr...

Diagnostic Performance of Deep Learning Applications in Hepatocellular Carcinoma Detection Using Computed Tomography Imaging.

The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology
Hepatocellular carcinoma (HCC) is a prevalent cancer that significantly contributes to mortality globally, primarily due to its late diagnosis. Early detection is crucial yet challenging. This study leverages the potential of deep learning (DL) techn...

Machine learning and molecular subtyping reveal the impact of diverse patterns of cell death on the prognosis and treatment of hepatocellular carcinoma.

Computational biology and chemistry
Programmed cell death (PCD) is a significant factor in the progression of hepatocellular carcinoma (HCC) and might serve as a crucial marker for predicting HCC prognosis and therapy response. However, the classification of HCC based on diverse PCD pa...

Artificial intelligence-assisted platform performs high detection ability of hepatocellular carcinoma in CT images: an external clinical validation study.

BMC cancer
BACKGROUND: Accurate detection of hepatocellular carcinoma (HCC) in multiphasic contrast CT is essential for effective treatment and surgical planning. However, the variety of CT images, the misdiagnosis and missed diagnosis, and the inconsistent dia...

Cellular Senescence in Hepatocellular Carcinoma: Immune Microenvironment Insights via Machine Learning and In Vitro Experiments.

International journal of molecular sciences
Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune mic...

Multicategory matched learning for estimating optimal individualized treatment rules in observational studies with application to a hepatocellular carcinoma study.

Statistical methods in medical research
One primary goal of precision medicine is to estimate the individualized treatment rules that optimize patients' health outcomes based on individual characteristics. Health studies with multiple treatments are commonly seen in practice. However, most...

Machine learning models for predicting postoperative peritoneal metastasis after hepatocellular carcinoma rupture: a multicenter cohort study in China.

The oncologist
BACKGROUND: Peritoneal metastasis (PM) after the rupture of hepatocellular carcinoma (HCC) is a critical issue that negatively affects patient prognosis. Machine learning models have shown great potential in predicting clinical outcomes; however, the...

Automated ultrasonography of hepatocellular carcinoma using discrete wavelet transform based deep-learning neural network.

Medical image analysis
This study introduces HCC-Net, a novel wavelet-based approach for the accurate diagnosis of hepatocellular carcinoma (HCC) from abdominal ultrasound (US) images using artificial neural networks. The HCC-Net integrates the discrete wavelet transform (...