Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-24 @ 11:10 PM
Ignite Modification Date:
2025-12-24 @ 11:10 PM
NCT ID:
NCT06588569
Brief Summary:
This single-center, parallel-controlled clinical trial aims to establish a multi-mode ablation system for liver malignant tumors originating from the digestive system. The study will evaluate the safety and efficacy of multi-mode tumor ablation, utilizing a multi-mode imaging platform for ablation planning and immediate evaluation of intraoperative ablation effects. Additionally, the study will employ multi-omics and multi-mode imaging techniques to explore the spatiotemporal heterogeneity and immune escape mechanisms of liver metastases from gastrointestinal tumors, providing guidance for treatment strategy formulation and prognostic evaluation.
Detailed Description:
This is a single-center, parallel-controlled clinical trial. The study includes a total enrollment of 20 subjects. 10 patients with primary liver cancer will be divided into an experimental group and a control group (5 cases per group); 10 patients with Colorectal Cancer Liver Metastases will also be divided into the experimental group and the control group (5 cases per group). The purpose is to validate the safety and efficacy of multi-mode tumor ablation for liver malignant tumors, aiming to establish a multi-mode tumor treatment system and obtain multi-dimensional biomedical information from patients before, during and after ablation.
The study will employ an interdisciplinary approach, integrating statistics, machine learning and artificial intelligence, to establish a new technical system for rapid efficacy evaluation. Additionally, it will establish a multi-omics artificial intelligence-assisted diagnostic and evaluation system based on radiomics. The system will use artificial intelligence algorithms to automatically locate and identify lesions based on imaging guidance information and accurately predict individual prognoses and anti-tumor immune states through comprehensive preoperative, intraoperative, and postoperative evaluations, providing an important basis for treatment planning, intraoperative dose adjustment, and subsequent treatment strategies.
Study:
NCT06588569
Study Brief:
Protocol Section:
NCT06588569