Patient-specific prediction of 3D ablation zones via oncological feature-conditioned deep generative modeling: An in silico feasibility study
Abstract
BACKGROUND AND OBJECTIVE
Accurate prediction of the ablation zone is essential for effective radiofrequency ablation (RFA) therapy. However, current clinical guidelines often rely on oversimplified ellipsoidal assumptions based on homogeneous tissue properties, failing to reflect the patient-specific variability in cancer morphology. This study proposes a deep generative modeling framework conditioned on MRI-derived oncological features and electrode placement information to generate in silico 3D ablation zone predictions.
METHODS
Our model utilizes a U-Net-based generator within a conditional DCGAN framework to capture irregular, non-ellipsoidal thermal patterns arising from tissue-dependent thermal and electrical heterogeneity. Trained on synthetic ablation data generated from validated numerical simulations incorporating MRI-derived cancer morphology, it implicitly learns the influence of biophysical heterogeneity. The model accuracy was evaluated using intersection-over-union (IoU), precision, sensitivity, and specificity metrics and tested on unseen patient data.
RESULTS
The model achieved high accuracy with an IoU of 90.92±3.54% and generated predictions in under 0.5 s, suggesting potential for integration into clinical decision-support workflows. It also demonstrated strong generalization to unseen patient data.
CONCLUSION
This study demonstrates the feasibility of deep generative modeling conditioned on MRI-derived oncological features for generating in silico 3D…
Read the paper
Related studies
- Challenges and opportunities with drug repurposing: finding strategies to find alternative uses of therapeutics
- Challenges in Chagas Disease Drug Development
- T Cell Specificity: A Great Challenge in Chagas Disease
- Benznidazole treatment leads to DNA damage in Trypanosoma cruzi and the persistence of rare widely dispersed non-replicative amastigotes in mice
- Rapid Molecular Detection of Terbinafine-resistant Dermatophytes
- Identifying novel drugs with new modes of action for neglected tropical fungal skin diseases (fungal skinNTDs) using an Open Source Drug discovery approach
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.