MP52-03 CLASSIFICATION OF KIDNEY TUMORS WITH 1064 NM DISPERSIVE RAMAN SPECTROSCOPY (2017)

You have accessJournal of UrologySurgical Technology & Simulation: Instrumentation & Technology I1 Apr 2017MP52-03 CLASSIFICATION OF KIDNEY TUMORS WITH 1064 NM DISPERSIVE RAMAN SPECTROSCOPY Miki Haifler, Isaac Pence, Benjamin Ristau, Andres Correa, shreyas Joshi, Richard Greenberg, David Chen, Marc Smaldone, Alexander Kutikov, Rosalia Viterbo, Robert Uzzo, Amnon Zisman, Anita Mahadevan-Jansen, and Chetan Patil Miki HaiflerMiki Haifler More articles by this author , Isaac PenceIsaac Pence More articles by this author , Benjamin RistauBenjamin Ristau More articles by this author , Andres CorreaAndres Correa More articles by this author , shreyas Joshishreyas Joshi More articles by this author , Richard GreenbergRichard Greenberg More articles by this author , David ChenDavid Chen More articles by this author , Marc SmaldoneMarc Smaldone More articles by this author , Alexander KutikovAlexander Kutikov More articles by this author , Rosalia ViterboRosalia Viterbo More articles by this author , Robert UzzoRobert Uzzo More articles by this author , Amnon ZismanAmnon Zisman More articles by this author , Anita Mahadevan-JansenAnita Mahadevan-Jansen More articles by this author , and Chetan PatilChetan Patil More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.1633AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The number of small, incidentally detected renal masses increase steadily. About 6,000 benign cases are misclassified radiographically as malignant and removed surgically yearly. Raman spectroscopy (RS) has been widely demonstrated for tissue characterization, however current implementations with either 785 or 830 nm near-infrared excitation have been ineffectual in tissues with intense auto-fluorescence such as the kidney. Recently, a RS system using 1064 nm light source was described which may have greater sensitivity for malignant and benign tissue discrimination due to decreased bulk tissue auto-fluorescence. Our aim was to evaluate the ability of 1064nm RS to distinguish normal and malignant renal tissue. METHODS Ex vivo specimens of Renal Cell Carcinoma and healthy human kidney were obtained from the Cooperative Human Tissue Network at Vanderbilt university. Measurements were made using of a benchtop dispersive 1064 nm Raman system. Multiple spectra were acquired from at least 5 physical locations across each specimen. A total of 93 measurements were used for the final analysis. The resulting spectra were put into a machine learning algorithm, sparse multinomial logistic regression (SMLR), to predict class membership of healthy and malignant tissues, and cross-validated using a leave-one-specimen out approach. Posterior probabilities of group classifications were extracted. Spectral bands that robustly differentiated between malignant and benign tissue were identified by the SMLR algorithm. A quantitative metric based on SMLR outputs called feature importance, defined as the product of the mean weight and frequency of usage of each feature, guided the association of spectral features with biological indicators of healthy and diseased Kidney tissue. RESULTS The SMLR algorithm identified 152 significant Raman spectral bands. most important features are depicted in figure 1. Correct classification by the SMLR algorithm was obtained in 93.33% of the trials with sensitivity, specificity, negative and positive predictive value of 93.2%, 88.6, 92.9% and 89.2% respectively. CONCLUSIONS RS can accurately differentiate normal and malignant renal tissue. This suggests implications for utilizing RS for optical biopsy and surgical guidance in nephron sparing surgery. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e704 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Miki Haifler More articles by this author Isaac Pence More articles by this author Benjamin Ristau More articles by this author Andres Correa More articles by this author shreyas Joshi More articles by this author Richard Greenberg More articles by this author David Chen More articles by this author Marc Smaldone More articles by this author Alexander Kutikov More articles by this author Rosalia Viterbo More articles by this author Robert Uzzo More articles by this author Amnon Zisman More articles by this author Anita Mahadevan-Jansen More articles by this author Chetan Patil More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...