Synergistic role of multi-omics analysis combined with multimodal imaging in precision diagnosis and treatment of thoracic and abdominal tumors
Year: 2026
Type: article
Abstract: Thoracic and abdominal tumors, such as lung, breast, liver, and colorectal cancers, are characterized by high incidence and mortality rates, posing a significant threat to global health. Traditional diagnostic and therapeutic approaches, primarily based on histopathological assessment and single-modality imaging, often fail to capture tumor heterogeneity fully. In the era of precision medicine, the integration of multi-omics technologies (genomics, transcriptomics, proteomics, metabolomics) with multimodal imaging (computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)) has emerged as a promising strategy to bridge macroscopic imaging phenotypes and microscopic molecular characteristics. This review systematically explores the synergistic application of multi-omics analysis and multimodal imaging in diagnosing and treating thoracic and abdominal tumors. It highlights advances in imaging technologies (such as radiomics, artificial intelligence (AI)-assisted interpretation) and multi-omics approaches (such as liquid biopsy and single-cell sequencing), alongside their clinical applications in early diagnosis, molecular subtyping, treatment response evaluation, and prognosis prediction across thoracic-abdominal and other specialties such as liver cancer and cardiothoracic surgery. Despite challenges in data standardization, algorithm interpretability, and clinical translation, this integrated framework holds great potential for advancing personalized oncology. Future directions should focus on leveraging next-generation technologies (such as photon-counting CT and spatial transcriptomics), transitioning from correlative analyses to causal mechanistic studies, and translating integrated imaging-omics models into clinical decision support systems to guide personalized therapy.
Source: Current Radiopharmaceuticals
Authors: Bin Su, Junshan Li, Jie Zhang, Tao Deng, Zheng Fang, Yuanlong Wang
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7147182181