Ablative Margins of Colorectal Liver Metastases Using Deformable CT Image Registration and Autosegmentation

Area of Science:

• Radiology
• Medical Imaging
• Oncology

Background:

• Confirming complete ablation with adequate margins is crucial for local tumor control in colorectal liver metastasis (CLM) ablation.
• Current image-based methods lack consideration for patient and ablation-related biomechanical deformation, creating an unmet need.
• Accurate assessment of the minimal ablative margin (MAM) is essential for predicting treatment outcomes.

Purpose of the Study:

• To evaluate a biomechanical deformable image registration (DIR) method for quantifying the 3D minimal ablative margin (MAM) after CT-guided CLM ablation.
• To assess the association between MAM and local disease progression following CLM ablation.
• To spatially localize sites of local disease progression relative to the ablation zone.

Main Methods:

• Retrospective analysis of 213 CLMs in 124 patients treated with CT-guided ablation.
• Application of a biomechanical DIR method with AI-based autosegmentation for MAM quantification.
• Multivariable Fine-Gray subdistribution hazard model used to evaluate factors associated with local disease progression.

Main Results:

• A minimal ablative margin (MAM) of 0 mm was associated with a 72% 2-year local disease progression rate.
• An MAM between 0 and 5 mm had a 12% 2-year local disease progression rate.
• No local disease progression was observed with an MAM of 5 mm or greater; MAM of 0 mm was independently associated with local disease progression (SHR, 23.3).

Conclusions:

• Biomechanical DIR and AI autosegmentation enable effective identification and spatial localization of CLMs at risk for local progression post-ablation.
• A minimal ablative margin (MAM) of 5 mm or greater is identified as the optimal endpoint for preventing local disease progression.
• This method provides crucial insights for improving local tumor control strategies in CLM ablation.