Histopathology Feature Mining and Association with Hyperspectral Imaging for the Detection of Squamous Neoplasia

Guolan Lu, Dongsheng Wang, Xulei Qin, Susan Muller, James V. Little, Xu Wang, Amy Y. Chen, Georgia Chen, Baowei Fei

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Hyperspectral imaging (HSI) is a noninvasive optical modality that holds promise for early detection of tongue lesions. Spectral signatures generated by HSI contain important diagnostic information that can be used to predict the disease status of the examined biological tissue. However, the underlying pathophysiology for the spectral difference between normal and neoplastic tissue is not well understood. Here, we propose to leverage digital pathology and predictive modeling to select the most discriminative features from digitized histological images to differentiate tongue neoplasia from normal tissue, and then correlate these discriminative pathological features with corresponding spectral signatures of the neoplasia. We demonstrated the association between the histological features quantifying the architectural features of neoplasia on a microscopic scale, with the spectral signature of the corresponding tissue measured by HSI on a macroscopic level. This study may provide insight into the pathophysiology underlying the hyperspectral dataset.

Original languageEnglish (US)
Article number17863
JournalScientific reports
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • General

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