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Lookup NU author(s): Dr Jichun Li
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© 2017. Rolling mechanical imaging (RMI) is a novel technique towards the detection and quantification of malignant tissue in locations that are inaccessible to palpation during robotic minimally invasive surgery (MIS); the approach is shown to achieve results of higher precision than is possible using the human hand. Using a passive robotic manipulator, a lightweight and force sensitive wheeled probe is driven across the surface of tissue samples to collect continuous measurements of wheel-tissue dynamics. A color-coded map is then generated to visualize the stiffness distribution within the internal tissue structure. Having developed the RMI device in-house, we aim to compare the accuracy of this technique to commonly used methods of localizing prostate cancer in current practice: digital rectal exam (DRE), magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) biopsy. Final histology is the gold standard used for comparison. A total of 126 sites from 21 robotic-assisted radical prostatectomy specimens were examined. Analysis was performed for sensitivity, specificity, accuracy, and predictive value across all patient risk profiles (defined by PSA, Gleason score and pathological score). Of all techniques, pre-operative biopsy had the highest sensitivity (76.2%) and accuracy (64.3%) in the localization of tumor in the final specimen. However, RMI had a higher sensitivity (44.4%) and accuracy (57.9%) than both DRE (38.1% and 52.4%, respectively) and MRI (33.3% and 57.9%, respectively). These findings suggest a role for RMI towards MIS, where haptic feedback is lacking. While our approach has focused on urological tumors, RMI has potential applicability to other extirpative oncological procedures and to diagnostics (e.g., breast cancer screening).
Author(s): Li J, Liu H, Brown M, Kumar P, Challacombe BJ, Chandra A, Rottenberg G, Seneviratne LD, Althoefer K, Dasgupta P
Publication type: Article
Publication status: Published
Journal: Medical Engineering and Physics
Print publication date: 01/05/2017
Online publication date: 21/02/2017
Acceptance date: 31/01/2017
ISSN (print): 1350-4533
ISSN (electronic): 1873-4030
Publisher: Elsevier Ltd
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