Primary liver cancer, mainly including hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), represents the third leading cause of cancer-related mortality. Enhancing the precision of liver cancer diagnosis and providing early therapeutic efficacy and prognostic evaluation during clinical decision-making hold significant clinical importance. Ultrasound is the preferred imaging modality for liver cancer screening. Contrast-enhanced ultrasound (CEUS) can dynamically visualize the microvascular perfusion of liver cancer lesions. Liver elastography has become a commonly used clinical assessment tool for cirrhosis. Photoacoustic imaging (PAI), an emerging non-invasive functional imaging technique, enables visualization of specific molecules through their spectroscopic characteristics at designated wavelengths.
The objectives of this study include: (1) Conducting an observational investigation combining CEUS, elastography, and superb microvascular imaging (SMI) to collect imaging data; (2) Preserving tumor specimens from participants to investigate heterogeneous protein characteristics of primary liver cancer organoids using PAI; (3) Analyzing peripheral venous blood samples to study transcriptomic profiles. Artificial intelligence (AI) technology will be employed to establish models integrating ultrasound radiomics with tumor multi-omics characteristics, aiming to provide novel strategies for precision diagnosis and treatment of liver cancer.
Key questions：(1) How to develop a multimodal imaging model combining CEUS, elastography, and SMI for predicting differentiation of liver cancer, microvascular invasion (MVI) and prognosis; (2) Whether PAI can identify heterogeneous proteins in liver cancer organoids through specific spectral recognition; (3) Whether AI can integrate multi-dimensional data to establish models based on ultrasound radiomics and multi-omics features.