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The use of percutaneous and bronchoscopic microwave ablation to treat both primary and secondary lung tumors has been growing recently. These ablation systems are typically accompanied by an ablation planning system to optimize the treatment outcome by ensuring adequate margin in the expected ablation zone during the planning phase. The planning system utilizes pre-operative CT scan to identify the tumor and recommend microwave probe position. Radiomics is a process of converting medical images into higher-dimensional data and subsequent mining of data to reveal underlying pathophysiology for enhancing clinical decision support making. Radiomics analysis have shown promises in capturing distinct tumor characteristics and predicting prognosis of the tumor. Here, we present a new method to predict microwave ablation zones by supplementing a bioheat transfer model of microwave tissue ablation with microwave sensitive radiomics features. We hypothesize that supplementing traditional bioheat transfer modeling with microwave sensitive radiomics features will generate a more accurate and personalized ablation prediction that will lead to better treatment outcome. Inputs to the bioheat transfer modeling approach include the geometry of the target tumor, physical characteristics of the tissue, and dimensions of the microwave ablation applicator. The radiomics algorithm extracts characteristics of the targeted tumor’s size and shape, as well as texture characteristics, from pre-operative CT images. We employed cascaded segmentation based on RetinaNet and U-Net to obtain a tumor’s size and shape. Then, a segmented tumor is employed for texture analysis through a set of regression convolutional neural networks. These tumor characteristics are employed as radiomics features for more accurate dose prediction and margin for microwave ablation treatment. We present the preliminary results of a study using images from clinical lung tumor cases to predict ablation treatment outcome, with patient-specific tissue biophysical properties based on radiomics features.
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