Pre-operative lung ablation prediction using deep learning

Krishna Nand Keshava Murthy, Carsten Eickhoff, Etay Ziv

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, deeplearning 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 follow-up CT post-treatment.
Novel deformable image registration optimized for ablation scans and an applicator-centric co-ordinate system for data
analysis were applied. Our prediction model was based on the U-net architecture. The registrations were evaluated using
target registration error (TRE) and predictions using Bland-Altman plots, Dice co-efficient, precision, and recall, compared
against the applicator vendor’s estimates.
Results The data included 113 unique ablations from 72 patients (median age 57, interquartile range (IQR) (49–67);
41 women). We obtained a TRE ≤ 2 mm on 52 ablations. Our prediction had no bias from ground truth ablation volumes
(p = 0.169) unlike the vendor’s estimate (p < 0.001) and had smaller limits of agreement (p < 0.001). An 11% improvement
was achieved in the Dice score. The ability to account for patient-specific in-vivo anatomical effects due to vessels, chest
wall, heart, lung boundaries, and fissures was shown.
Conclusions We demonstrated a patient-specific deep-learning model to predict the ablation treatment effect prior to
the procedure, with the potential for improved planning, achieving complete treatments, and reduce tumor recurrence.
Clinical relevance statement Our method addresses the current lack of reliable tools to estimate ablation extents,
required for ensuring successful ablation treatments. The potential clinical implications include improved treatment
planning, ensuring complete treatments, and reducing tumor recurrence.