Kidney cancer affects 65,000 new patients every. As computerized tomography became ubiquitous, the number of small, incidentally detected renal masses increased. About 6,000 benign cases are misclassified radiographically as malignant and removed surgically. Raman spectroscopy (RS) has been widely demonstrated for disease discrimination, however intense near-infrared auto-fluorescence of certain tissues (e.g kidney) can present serious challenges to bulk tissue diagnosis. A 1064nm excitation dispersive detection RS system demonstrated the ability to collect spectra with superior quality in tissues with strong auto-fluorescence. Our objective is to develop a 1064 nm dispersive detection RS system capable of differentiating normal and malignant renal tissue. We will report on the design and development of a clinical system for use in nephron sparing surgeries. We will present pilot data that has been collected from normal and malignant ex vivo kidney specimens using a benchtop RS system. A total of 93 measurements were collected from 12 specimens (6 Renal Cell Carcinoma, 6 Normal ). Spectral classification was performed using sparse multinomial logistic regression (SMLR). Correct classification by SMLR was obtained in 78% of the trials with sensitivity and specificity of 82% and 75% respectively. We will present the association of spectral features with biological indicators of healthy and diseased kidney tissue. Our findings indicate that 1064nm RS is a promising technique for differentiation of normal and malignant renal tissue. This indicates the potential for accurately separating healthy and cancerous tissues and suggests implications for utilizing RS for optical biopsy and surgical guidance in nephron sparing surgery.