Percutaneous liver ablation is a minimally invasive procedure to treat liver tumors. Postablation images are highly significant as they distinguish normal post-procedure changes from abnormalities, preventing unnecessary retreatment and confirming procedural quality. However, the cancer surveillance imaging reports after the procedure can be numerous and challenging to read. Moreover, annotated data is limited in this setting. In this study we used the cutting-edge large language model Llama 2 to automatically extract critical findings from real-world diagnostic imaging reports without the need of training a new information extraction model. This could potentially automate part of the outcome research and registry construction process, as well as decrease the number of studies needed to review for research purposes. A dataset of 87 full-text reports from 13 patients who underwent percutaneous thermal ablation for pancreatic liver metastases were used to benchmark the capability of Llama 2 for cancer progression finding extraction and classification. We asked Llama 2 to determine whether there is cancer progression within the given report and then classify progression findings into Local Tumor Progression (LTP), Intrahepatic Progression (IHP) and Extrahepatic Progression (EHP). Llama 2 achieved decent performance for detecting progression at study level. The precision is 0.91 and recall is 0.96, with specificity 0.84. However, the classification of progression into LTP, IHP and EHP still needs to be improved.
Microwave ablation (MWA) is an effective minimally invasive therapy for treating liver cancers, among various local cancer treatments. Computational studies are crucial in simulating MWA, offering insights that may be unreachable from experimental methods. This study investigated the complex relationships between blood perfusion rate and metabolic heat concerning MWA outcomes. 3D patient-specific finite element models are employed, shedding light on the interplay of these parameters and their impact on the efficacy of MWA procedures. Image data from five patients treated with MWA are chosen, creating detailed 3D models of the liver, tumor, and vasculature. Simulations are performed using a triaxial antenna operating at 2.45 GHz, with a standard ablation time of 10 minutes and an input power of 65 Watts. In addition, the microwave antenna mimics the clinical insertion path in each case. The simulation model encompasses the coupled electromagnetic field and bioheat transfer, comprehensively understanding the underlying dynamics. The simulations contain seven distinct blood perfusion rates, both with and without considering metabolic heat. This variation allows for a thorough exploration of their combined impact on tissue damage and tumor destruction throughout MWA therapy. These findings underscore the intricate interplay of factors influencing the outcomes of MWA procedures, emphasizing the importance of comprehensive modeling that incorporates various parameters for accurate predictions.
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