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Liver Neoplasms

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Deep STI: Deep Stochastic Time-series Imputation on Electronic Health Records.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Electronic Health Records (EHRs) are a cornerstone of modern healthcare analytics, offering rich datasets for various disease analyses through advanced deep learning algorithms. However, the pervasive issue of missing values in EHRs significantly ham...

Multi-dataset Collaborative Learning for Liver Tumor Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic segmentation of biomedical images has emerged due to its potential in improving real-world clinical processes and has achieved great success in recent years thanks to the development of deep learning. However, it is the limited availability...

TTGA U-Net: Two-stage two-stream graph attention U-Net for hepatic vessel connectivity enhancement.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate segmentation of hepatic vessels is pivotal for guiding preoperative planning in ablation surgery utilizing CT images. While non-contrast CT images often lack observable vessels, we focus on segmenting hepatic vessels within preoperative MR i...

Predicting hepatocellular carcinoma survival with artificial intelligence.

Scientific reports
Despite the extensive research on hepatocellular carcinoma (HCC) exploring various treatment strategies, the survival outcomes have remained unsatisfactory. The aim of this research was to evaluate the ability of machine learning (ML) methods in pred...

Recurrence patterns and prediction of survival after recurrence for gallbladder cancer.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
BACKGROUND: Gallbladder cancer (GBC) is associated with a poor prognosis. Recurrence patterns and their effect on survival remain ill-defined. This study aimed to analyze recurrence patterns and develop a machine learning (ML) model to predict surviv...

A Bi-modal Temporal Segmentation Network for Automated Segmentation of Focal Liver Lesions in Dynamic Contrast-enhanced Ultrasound.

Ultrasound in medicine & biology
OBJECTIVE: To develop and validate an automated deep learning-based model for focal liver lesion (FLL) segmentation in a dynamic contrast-enhanced ultrasound (CEUS) video.

Opportunistic Detection of Hepatocellular Carcinoma Using Noncontrast CT and Deep Learning Artificial Intelligence.

Journal of the American College of Radiology : JACR
OBJECTIVE: Hepatocellular carcinoma (HCC) poses a heavy global disease burden; early diagnosis is critical to improve outcomes. Opportunistic screening-the use of imaging data acquired for other clinical indications for disease detection-as well as t...

Machine learning analysis identified NNMT as a potential therapeutic target for hepatocellular carcinoma based on PCD-related genes.

Scientific reports
Programmed cell death (PCD) plays a critical role in cancer biology, influencing tumor progression and treatment response. This study aims to investigate the role of PCD-related genes in hepatocellular carcinoma (HCC), identifying potential prognosti...

MRI-Based Topology Deep Learning Model for Noninvasive Prediction of Microvascular Invasion and Assisting Prognostic Stratification in HCC.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND & AIMS: Microvascular invasion (MVI) is associated with poor prognosis in hepatocellular carcinoma (HCC). Topology may improve the predictive performance and interpretability of deep learning (DL). We aimed to develop and externally valida...