AIMC Topic: Liver Neoplasms

Clear Filters Showing 701 to 710 of 838 articles

Towards robust multimodal ultrasound classification for liver tumor diagnosis: A generative approach to modality missingness.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In medical image analysis, combining multiple imaging modalities enhances diagnostic accuracy by providing complementary information. However, missing modalities are common in clinical settings, limiting the effectiveness of...

Predicting hepatocellular carcinoma response to TACE: A machine learning study based on 2.5D CT imaging and deep features analysis.

European journal of radiology
OBJECTIVES: Prior to the commencement of treatment, it is essential to establish an objective method for accurately predicting the prognosis of patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE). In this st...

Modality-Aware Distillation Network for Microvascular Invasion Prediction of Hepatocellar Carcinoma From MRI Images.

IEEE transactions on bio-medical engineering
Microvascular invasion (MVI) of hepatocellular carcinoma (HCC) is a crucial histopathologic prognostic factor associated with cancer recurrence after liver transplantation or hepatectomy. Recently, clinicoradiologic characteristics are combined with ...

Integration of Bulk RNA and Single-Cell Analyses Reveal Distinct Expression Patterns of Anoikis-Related Genes and the Immunosuppressive Role of NQO1 Macrophages in Hepatocellular Carcinoma.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Anoikis resistance plays a crucial role in the proliferation, metastasis, and invasion of hepatocellular carcinoma (HCC). However, the key genes involved remain to be identified. This study aimed to investigate the prognostic value and impact of anoi...

FasNet: a hybrid deep learning model with attention mechanisms and uncertainty estimation for liver tumor segmentation on LiTS17.

Scientific reports
Liver cancer, especially hepatocellular carcinoma (HCC), remains one of the most fatal cancers globally, emphasizing the critical need for accurate tumor segmentation to enable timely diagnosis and effective treatment planning. Traditional imaging te...

Deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancer.

BMC medical imaging
OBJECTIVES: To establish and validate deep learning (DL) models based on pre-treatment multiparametric magnetic resonance imaging (MRI) images of primary rectal cancer and basic clinical data for the prediction of synchronous liver metastases (SLM) i...

Patient Survival Prediction by Analyzing Pathological Images of Patients After Liver Transplantation.

Studies in health technology and informatics
Predicting whether a patient will develop cancer using nuclear features on pathological images is important for decision making regarding patient treatment after liver transplantation or hepatectomy. Unlike manual segmentation to extract nuclei parts...

A machine learning-based prediction model for colorectal liver metastasis.

Clinical and experimental medicine
Colorectal liver metastasis (CRLM) is a primary factor contributing to poor prognosis and metastasis in colorectal cancer (CRC) patients. This study aims to develop and validate a machine learning (ML)-based risk prediction model using conventional c...