Etay Ziv, MD, PhD

Surgical removal of the tumor is the standard treatment for early-stage lung cancer. Lung ablation, a minimally invasive procedure that destroys tumor tissue by either heat or cooling, is an alternative for patients who are not surgical candidates. A major limitation for lung ablation is that there is no way to confirm whether tumor was completely eradicated. The success of lung ablation is determined by creating a large enough ablation area that extends a minimum margin beyond the tumor. However, identifying the extent of the ablation is challenging because of inflammation in the treatment area, which results in incomplete treatment and local recurrence (LR). We have developed computational tools to identify the extent of the treatment based on imaging done during the procedure, and to measure the margin of the ablation based on follow-up imaging. We plan to validate these tools to identify regions at high risk of LR during the procedure or on follow-up imaging. This will allow us to modify the treatment during the procedure, thereby reducing LR and improving outcomes.

Update: We have been able to make good progress on our aims. First, we expanded our database from the original 100 to 800 lung ablations and plan to add another 500 in the coming months. We have also developed a specialized algorithm for lung ablation that helps us accurately measure treatment margins. By applying this to early post-treatment scans, we can now identify which lung ablations are at higher risk of recurring. We are developing a user interface so that the algorithms can be used intra-operatively by the clinician.