An Exploratory Study on Developing an Integrated Approach Combining Multimodal Imaging and Multi-omics Characterization of Tumor Heterogeneity for Precision Diagnosis and Treatment Optimization in Liver Cancer.

Condition(s) studied

Investigational drug(s) / intervention(s)

Multi-modal ultrasonic imaging system for liver cancer: Conventional ultrasound, SMI, shear wave /strain elastography, and CEUS will be conducted for HCC/ICC patients. All above the imaging examinations will be conducted at 1/2/3/4/5/6, 9/12/15/18/21/24, and 30/36/42/48/54/60 months after the postoperative/post-conversion therapy.

Study summary

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.

Eligibility

Primary outcome measure(s)

• tumor progression or follow-up failure. — From date of enrollment until the date of first documented tumor progression, assessed up to 5 years
  Tumor progression criteria: 1. Target lesions: Based on the smallest sum of the longest diameters recorded in the study, progression is defined as: a) A ≥ 20% increase in the sum of the longest diameters of target lesions, with an absolute increase of at least 5 mm, or b) The appearance of new lesions. 2. Non-target lesions: Clear progression of existing non-target lesions and/or the emergence of one or more new lesions.

Trial sites (1)

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