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Liver Neoplasms

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Artificial intelligence in liver cancer - new tools for research and patient management.

Nature reviews. Gastroenterology & hepatology
Liver cancer has high incidence and mortality globally. Artificial intelligence (AI) has advanced rapidly, influencing cancer care. AI systems are already approved for clinical use in some tumour types (for example, colorectal cancer screening). Cruc...

Global contextual representation via graph-transformer fusion for hepatocellular carcinoma prognosis in whole-slide images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Current methods of digital pathological images typically employ small image patches to learn local representative features to overcome the issues of computationally heavy and memory limitations. However, the global contextual features are not fully c...

Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma.

Clinical and molecular hepatology
BACKGROUND/AIMS: The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC pa...

Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification.

European journal of radiology
OBJECTIVES: This study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk groups of prognoses following liver resection by applying an unsupervised radiomics machine-learning a...

Deep learning-based compressed SENSE improved diffusion-weighted image quality and liver cancer detection: A prospective study.

Magnetic resonance imaging
PURPOSE: To assess whether diffusion-weighted imaging (DWI) with Compressed SENSE (CS) and deep learning (DL-CS-DWI) can improve image quality and lesion detection in patients at risk for hepatocellular carcinoma (HCC).

A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features.

Computers in biology and medicine
BACKGROUND: Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effecti...

S2DA-Net: Spatial and spectral-learning double-branch aggregation network for liver tumor segmentation in CT images.

Computers in biology and medicine
Accurate liver tumor segmentation is crucial for aiding radiologists in hepatocellular carcinoma evaluation and surgical planning. While convolutional neural networks (CNNs) have been successful in medical image segmentation, they face challenges in ...

Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning.

European radiology
BACKGROUND: Accurate mortality risk quantification is crucial for the management of hepatocellular carcinoma (HCC); however, most scoring systems are subjective.

A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Automatic segmentation of hepatocellular carcinoma (HCC) on computed tomography (CT) scans is in urgent need to assist diagnosis and radiomics analysis. The aim of this study is to develop a deep learning based network to detect HCC from ...

Serum Fusion Transcripts to Assess the Risk of Hepatocellular Carcinoma and the Impact of Cancer Treatment through Machine Learning.

The American journal of pathology
Hepatocellular carcinoma (HCC) is one of the most fatal malignancies. Early diagnosis of HCC is crucial in reducing the risk for mortality. This study analyzed a panel of nine fusion transcripts in serum samples from 61 patients with HCC and 75 patie...