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Abstract #2046

Leveraging Automation for Adaptive Planning in MRI-Guided Prostate Cancer Focal Cryoablation

Nayani Modugula1, Kemal Tuncali2,3, Clare Tempany2,3, Junichi Tokuda2,3, Nobuhiko Hata2,3, and Pedro Moreira2,3
1Brown University, Providence, RI, United States, 2Brigham and Women's Hospital, Boston, MA, United States, 3Harvard Medical School, Boston, MA, United States

Synopsis

Keywords: MR-Guided Interventions, MR-Guided Interventions

Motivation: Predicting freezing volume intraoperatively is essential for effective focal cryoablation of prostate cancer. The lack of a fast, accurate, and clinically viable method currently hinders the development of robust planning software for optimal cryo-needle placement.

Goal(s): To develop an automated method to predict final iceball formation as cryo-needles are inserted.

Approach: We use automated segmentation of needles and urethra from MRI images, which serves as input for a predictive model to estimate iceball formation.

Results: Tested on five retrospective cases, the software demonstrated feasibility, enabling rapid, one-click predictions within seconds.

Impact: Integrating automatic AI-based segmentation for rapid iceball prediction enables adaptive, real-time planning in cryoablation. This approach minimizes labor-intensive segmentation, allowing updates with each needle insertion to ensure comprehensive tumor ablation, enhancing precision, confidence, and efficiency in prostate cancer treatment.

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Keywords