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Incidence of lung nodules has surged due to improved lung cancer screening programs. Although localized microwave ablation (MWA) has been shown to be a minimally invasive safe, effective, cost and time efficient treatment for non-surgical candidates, it remains an underutilized curative modality for early-stage lung cancer patients, due in part to professed superiority of radiation for local tumor control, or prevention of local tumor progression (LTP). Identification of lesions that may be more amenable to effective MWA treatment may lead to improved outcomes. To aid physicians in optimizing patient selection, we developed a machine learning model to predict LTP after MWA treatment. Our model utilizes specialized 3D three-channel data: pre-ablation CT data (channel 1), post-ablation CT data depicting the resulting ablation zone (channel 2) and overlapping data of the tumor and ablation zone (channel 3). By spatially registering pre- and post-ablation CTs, we establish a clear spatial relationship between the tumor and ablation zone. Our neural network, trained on 55 MWA-treated lung-cancer patients, achieved a C-statistic (AUC) of 0.849 compared to 0.78 of prior approaches in 5-fold cross-validation. Notably, this performance was achieved without incorporating tabular features such as cancer type or ablation margin, highlighting strengths of the specialized 3D three-channels images. Combined with our past work, where we demonstrated the potential for accurate prediction of ablation zone boundaries during procedure planning, our research presents promising preliminary results for assisting physicians in predicting LTP following localized MWA treatment. The ability to identify good responders to MWA may provide a tool for patient selection, enhance patient outcome, and expand the utilization of this safe, effective treatment option.
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