Discrimination of malignant and normal kidney tissue with short wave infrared dispersive Raman spectroscopy

Miki Haifler, Isaac Pence, Yu Sun, Alexander Kutikov, Robert G. Uzzo, Anita Mahadevan-Jansen, Chetan A. Patil

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Renal mass biopsy is still controversial due to imperfect accuracy. Raman spectroscopy (RS) demonstrated promise as an in vivo real-time, nondestructive diagnostic tool in many malignancies. Short wave infrared (SWIR) RS has the potential to improve on previous RS systems for renal mass diagnosis. The aim of this study is to evaluate a SWIR RS system in differentiating normal and malignant renal samples. Measurements were acquired using a benchtop RS system with excitation wavelength at 1064 nm and an InGaAs array detector. Processed spectra were classified with a Bayesian machine learning algorithm, sparse multinomial logistic regression. Sensitivity and receiver operating characteristic curve analyses evaluated the classifier accuracy. Accuracy of the classifier was 92.5% with sensitivity and specificity of 95.8% and 88.8%, respectively. For posterior probability of malignant class assignment, the area under the ROC curve is 0.94 (95% confidence interval: 0.89-0.99, P <.001). SWIR RS accurately differentiated normal and malignant kidney tumors. RS has the potential to be used as a diagnostic tool in kidney cancer.

Original languageEnglish (US)
Article numbere201700188
JournalJournal of Biophotonics
Issue number6
StatePublished - Jun 2018
Externally publishedYes


  • biopsy
  • Raman spectroscopy
  • renal cell carcinoma
  • tissue diagnosis

ASJC Scopus subject areas

  • Chemistry(all)
  • Materials Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Engineering(all)
  • Physics and Astronomy(all)


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