  Radiofrequency ablation (RFA) is a widely used minimally invasive technique
for ablating solid tumors. Achieving precise personalized treatment
necessitates feedback information on in situ thermal effects induced by the RFA
procedure. While computer simulation facilitates the prediction of electrical
and thermal phenomena associated with RFA, its practical implementation in
clinical settings is hindered by high computational demands. In this paper, we
propose a physics-guided neural network model, named PhysRFANet, to enable
real-time prediction of thermal effect during RFA treatment. The networks,
designed for predicting temperature distribution and the corresponding ablation
lesion, were trained using biophysical computational models that integrated
electrostatics, bio-heat transfer, and cell necrosis, alongside magnetic
resonance (MR) images of breast cancer patients. Validation of the
computational model was performed through experiments on ex vivo bovine liver
tissue. Our model demonstrated a 96% Dice score in predicting the lesion volume
and an RMSE of 0.4854 for temperature distribution when tested with foreseen
tumor images. Notably, even with unforeseen images, it achieved a 93% Dice
score for the ablation lesion and an RMSE of 0.6783 for temperature
distribution. All networks were capable of inferring results within 10 ms. The
presented technique, applied to optimize the placement of the electrode for a
specific target region, holds significant promise in enhancing the safety and
efficacy of RFA treatments.
