AIMC Topic: Liver Neoplasms

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A Novel Visual Model for Predicting Prognosis of Resected Hepatoblastoma: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to evaluate the application of a contrast-enhanced CT-based visual model in predicting postoperative prognosis in patients with hepatoblastoma (HB).

Identification of CACNB1 protein as an actionable therapeutic target for hepatocellular carcinoma via metabolic dysfunction analysis in liver diseases: An integrated bioinformatics and machine learning approach for precise therapy.

International journal of biological macromolecules
In addition to histological evaluation for nonalcoholic fatty liver disease (NAFLD), a comprehensive analysis of the metabolic landscape is urgently needed to categorize patients into distinct subgroups for precise treatment. In this study, a total o...

A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer.

Cell reports. Medicine
Liver tumors, whether primary or metastatic, significantly impact the outcomes of patients with cancer. Accurate identification and quantification are crucial for effective patient management, including precise diagnosis, prognosis, and therapy evalu...

Multimodal treatment of colorectal liver metastases: Where are we? Current strategies and future perspectives.

Bioscience trends
Despite the continued high prevalence of colorectal cancer in the Western world, recent years have witnessed a decline in its mortality rate, largely attributable to the sustained advancement of multimodal treatment modalities for metastatic patients...

Multitask learning model for predicting non-coding RNA-disease associations: Incorporating local and global context.

Methods (San Diego, Calif.)
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are crucial non-coding RNAs involved in various diseases. Understanding these interactions is vital for advancing diagnostic, preventive, and therapeutic strategies. Existing computational methods...

Denoised recurrence label-based deep learning for prediction of postoperative recurrence risk and sorafenib response in HCC.

BMC medicine
BACKGROUND: Pathological images of hepatocellular carcinoma (HCC) contain abundant tumor information that can be used to stratify patients. However, the links between histology images and the treatment response have not been fully unveiled.

Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma.

BMC cancer
OBJECTIVES: The potential of medical imaging to non-invasively assess intratumoral heterogeneity (ITH) is increasingly being recognized. This study aimed to investigate the value of the ITH-based deep learning model for preoperative prediction of his...

LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Hepatocellular carcinoma (HCC) is often diagnosed using gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI). Standardized reporting according to the Liver Imaging Reporting and Data System (LI-RADS) can improve Gd-MRI interp...

Explainable attention-enhanced heuristic paradigm for multi-view prognostic risk score development in hepatocellular carcinoma.

Hepatology international
PURPOSE: Existing prognostic staging systems depend on expensive manual extraction by pathologists, potentially overlooking latent patterns critical for prognosis, or use black-box deep learning models, limiting clinical acceptance. This study introd...

Liver lesion segmentation in ultrasound: A benchmark and a baseline network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate liver lesion segmentation in ultrasound is a challenging task due to high speckle noise, ambiguous lesion boundaries, and inhomogeneous intensity distribution inside the lesion regions. This work first collected and annotated a dataset for l...