Pre-operative lung ablation prediction using deep learning.

ABSTRACT:

Objective

Microwave lung ablation (MWA) is a minimally invasive and inexpensive alternative cancer treatment for patients who are not candidates for surgery/radiotherapy. However, a major challenge for MWA is its relatively high tumor recurrence rates, due to incomplete treatment as a result of inaccurate planning. We introduce a patient-specific, deep-learning model to accurately predict post-treatment ablation zones to aid planning and enable effective treatments.

Materials and methods

Our IRB-approved retrospective study consisted of ablations with a single applicator/burn/vendor between 01/2015 and 01/2019. The input data included pre-procedure computerized tomography (CT), ablation power/time, and applicator position. The ground truth ablation zone was segmented from fo

SUBMITTER: Keshavamurthy KN 

PROVIDER: S-EPMC11519138 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature