Fusing Ultrasound and Magnetic Resonance Imaging to Intelligently Plan Highly Conformal Ablation Thermal Field for Hepatocellular Carcinoma

Condition(s) studied

Investigational drug(s) / intervention(s)

Post-ablation MRI is used to evaluate whether the ablation area of the tumor is consistent with the highly conformal ablation thermal field provided by the AI model.: This study developed an AI model that can provide optimal highly conformal ablation thermal field for HCC patients using ultrasound and MRI. Post-ablation MRI is used to evaluate whether the ablation area of the tumor is consistent with the highly conformal ablation thermal field provided by the AI model. The patients were divided into: 1. Actual ablation zone of the tumor was consistent with the highly conformal ablation thermal field (consistent group); 2. Actual ablation zone of the tumor was inconsistent with the highly conformal ablation thermal field (inconsistent group).

Study summary

Thermal ablation is an important minimally invasive treatment for hepatocellular carcinoma (HCC), but local tumor progression (LTP) after ablation restricts the efficacy and status of ablation technology and seriously threatens patient survival. Insufficient coverage of thermal field is an important factor on the occurrence of LTP. Current thermal field planning relies on tumor contours and doctor experience, and the safety margin is uniform. Therefore, it cannot cope with the problem of insufficient coverage of thermal field caused by the different invasion capabilities of different tumors and different parts of the same tumor. This project intends to integratively analyze gray-scale ultrasound, contrast-enhanced ultrasound, magnetic resonance imaging and clinical information of HCC through deep canonical correlation analysis; summarize the prior knowledge of LTP risk factors in previous studies and perform conjoint analysis individual case data and common conclusions through knowledge graph; interpretatively predict the LTP risk and the high-risk LTP locations through link prediction; accurately predict the ablation safety margin required for different tumor parts through graph neural network, and achieve highly conformal thermal field planning based on different invasion capabilities to minimize the LTP risk of HCC. The project leverages tumor multi-modal imaging and prior knowledge as the entry point, performs highly conformal planning of the ablation thermal field through artificial intelligence technology, and provides a new method for precise ablation.

Eligibility

Primary outcome measure(s)

• Local tumor progression — 2 years
  After HCC ablation, tumor recurrence appears around the ablation area

Trial sites (1)

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