Computerized Training of Cryosurgery: Prostate Geometric Modeling and Training Framework

• Main Author: Sehrawat, Anjali
• Format: Others
• Published: Research Showcase @ CMU 2015
• Online Access: http://repository.cmu.edu/dissertations/501; http://repository.cmu.edu/cgi/viewcontent.cgi?article=1501&context=dissertations

This study concerns medical training and education of cryosurgery—the destruction of cancerous tissue by freezing. Minimally invasive cryosurgery is performed by strategically placing an array of cryoprobes within a target region, in order to maximize freezing injury in the target region, while minimizing damage to its surrounding tissues. Cryoprobe placement has yet to be standardized, where cryosurgeons frequently base their practice on their own experience, recommendations made by cryodevice manufacturers, and accepted practices. Suboptimal cryoprobe layouts may leave untreated areas in the target region, lead to cryoinjury in the healthy surrounding tissues, require unnecessarily large numbers of cryoprobes, increase the duration of the surgical procedure, and increase the likelihood of post-cryosurgery complications, all of which affect the quality and cost of the medical treatment. While using prostate cryosurgery as a developmental model for surgical training, this study focuses on two key elements: (i) creating realistic prostate models for training, and (ii) developing training methods for the optimal cryoprobe layout. Tumor growth pattern in prostates at T3-stage cancer, representative of the cryosurgery candidate population, is characterized in order to identify tumor features that contribute to changes in the prostate shape. Extended free-form deformation (EFFD) is applied on a 3D prostate template geometry to create localized surface changes that resemble cancerous prostates, where key tumor features compiled serve as deformation criteria. The computational technique is demonstrated in three case studies by systematically selecting critical tumor features and deforming the prostate template contour until selected feature parameters are met. A proof-of-concept for a computerized cryosurgery tutoring system was developed for the simplified case of uniform insertion-depth—2D cryoprobe layout planning. The tutoring system lists geometrical constraints of cryoprobes placement, simulates cryoprobe insertion, displays a rendered shape of the prostate, enables distance measurements, simulates the corresponding thermal history, and evaluates the mismatch between the target region shape and a pre-selected planning isotherm. The quality of trainee planning is measured in comparison with computer-generated planning, created for each case study by previously developed planning algorithms. Two versions of the tutoring system have been tested in the current study using 23 surgical residents: (i) an unguided version, where the trainee can practice cases in unstructured sessions, and (ii) an intelligent tutoring system (ITS), which forces the trainee to follow specific steps, believed by the authors to potentially shorten the learning curve. Posttest results indicate that the ITS system maybe more beneficial than the non-ITS system, but the proof-of-concept is demonstrated with either system. Based on the observed effectiveness of the ITS prototype and the learning behaviors of surgical residents, the cryosurgery tutoring system design was modified and extended for the advanced-case of variable insertion-depth—3D cryoprobe layout planning. The objective of the tutoring system remains essentially the same—to develop a cryoprobe layout for a given number of cryoprobes, in order to maximize the match between the resulting frozen region and the target region. The proof-of-concept was demonstrated by measuring learning gains of 18 surgical residents after training on the system. Residents showed significant improvement in minimizing the mismatch between the target region and frozen region.