Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Liver cancer remains a significant global health concern, ranking as the sixth most common malignancy and the third leading cause of cancer-related deaths worldwide. Medical imaging plays a vital role in managing liver tumors, particularly hepatocellular carcinoma (HCC) and metastatic lesions. However, the large volume and complexity of imaging data can make accurate and efficient interpretation challenging. Artificial intelligence (AI) is recognized as a promising tool to address these challenges. Therefore, this review aims to explore the recent advances in AI applications in liver tumor imaging, focusing on key areas such as image reconstruction, image quality enhancement, lesion detection, tumor characterization, segmentation, and radiomics. Among these, AI-based image reconstruction has already been widely integrated into clinical workflows, helping to enhance image quality while reducing radiation exposure. While the adoption of AI-assisted diagnostic tools in liver imaging has lagged behind other fields, such as chest imaging, recent developments are driving their increasing integration into clinical practice. In the future, AI is expected to play a central role in various aspects of liver cancer care, including comprehensive image analysis, treatment planning, response evaluation, and prognosis prediction. This review offers a comprehensive overview of the status and prospects of AI applications in liver tumor imaging.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00261-025-05059-8DOI Listing

Publication Analysis

Top Keywords

artificial intelligence
8
liver tumors
8
liver cancer
8
applications liver
8
liver tumor
8
tumor imaging
8
image reconstruction
8
image quality
8
imaging
7
liver
7

Similar Publications

Aims And Objective: The field of medical statistics has experienced significant advancements driven by integrating innovative statistical methodologies. This study aims to conduct a comprehensive analysis to explore current trends, influential research areas, and future directions in medical statistics.

Methods: This paper maps the evolution of statistical methods used in medical research based on 4,919 relevant publications retrieved from the Web of Science.

View Article and Find Full Text PDF

Objectives: Non-small cell lung cancer (NSCLC) is associated with poor prognosis, with 30% of patients diagnosed at an advanced stage. Mutations in the and genes are important prognostic factors for NSCLC, and targeted therapies can significantly improve survival in these patients. Although tissue biopsy remains the gold standard for detecting gene mutations, it has limitations, including invasiveness, sampling errors due to tumor heterogeneity, and poor reproducibility.

View Article and Find Full Text PDF

Artificial Intelligence in Contact Dermatitis: Current and Future Perspectives.

Dermatitis

September 2025

From the Department of Dermatology, Venereology and Leprology, All India Institute of Medical Sciences (AIIMS), Bhopal, India.

Contact dermatitis (CD), which includes both allergic CD and irritant CD, is a common inflammatory condition that can pose significant diagnostic challenges. Although patch testing is the gold standard for identifying causative allergens for allergic contact dermatitis (ACD), it is time-consuming, subjective, and requires expert interpretation. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning, have shown promise in improving the accuracy, efficiency, and accessibility of CD diagnosis and management.

View Article and Find Full Text PDF

Enhancement of the performance of lithium-ion batteries is a critical strategy for addressing the challenges associated with cost and raw materials. By doping boron (B), aluminum (Al), and aluminum/boron (Al/B) utilizing the sol-gel method, we demonstrate a substantial improvement in the cycling performance of Ni-rich lithium nickel manganese cobalt oxide (NMC) as an electrode. While the initial specific capacitance of the doped samples may be lower than that of the pristine NMC, these samples demonstrate a notable increase in specific capacitance during subsequent cycles, reaching a peak around the 10 cycle and nearing the highest specific capacitance observed in NMC cathodes.

View Article and Find Full Text PDF