Hyperspectral imaging of neoplastic progression in a mouse model of oral carcinogenesis

Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Müller, Susan; Zhang, Hongzheng; Chen, Amy Y.; Chen, Zhuo Georgia; Fei, Baowei

doi:10.1117/12.2216553

Cited by 24 publications

(19 citation statements)

References 21 publications

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“…The hyperspectral images were acquired using a CRI Maestro imaging system (Perkin Elmer Inc., Waltham, Massachusetts), which is composed of a xenon white-light illumination source, a liquid crystal tunable filter, and a 16-bit charge-coupled device camera capturing images at a resolution of 1040 × 1392 pixels and a spatial resolution of 25 μm per pixel. 27,28,30,31 The hypercube contains 91 spectral bands, ranging from 450 to 900 nm with a 5-nm spectral sampling interval. The average imaging time for acquiring a single HSI was about 1 min.…”

Section: Hyperspectral Imaging and Processingmentioning

confidence: 99%

Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks

et al. 2019

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For patients undergoing surgical cancer resection of squamous cell carcinoma (SCCa), cancer-free surgical margins are essential for good prognosis. We developed a method to use hyperspectral imaging (HSI), a noncontact optical imaging modality, and convolutional neural networks (CNNs) to perform an optical biopsy of ex-vivo, surgical gross-tissue specimens, collected from 21 patients undergoing surgical cancer resection. Using a cross-validation paradigm with data from different patients, the CNN can distinguish SCCa from normal aerodigestive tract tissues with an area under the receiver operator curve (AUC) of 0.82. Additionally, normal tissue from the upper aerodigestive tract can be subclassified into squamous epithelium, muscle, and gland with an average AUC of 0.94. After separately training on thyroid tissue, the CNN can differentiate between thyroid carcinoma and normal thyroid with an AUC of 0.95, 92% accuracy, 92% sensitivity, and 92% specificity. Moreover, the CNN can discriminate medullary thyroid carcinoma from benign multinodular goiter (MNG) with an AUC of 0.93. Classical-type papillary thyroid carcinoma is differentiated from MNG with an AUC of 0.91. Our preliminary results demonstrate that an HSI-based optical biopsy method using CNNs can provide multicategory diagnostic information for normal and cancerous head-and-neck tissue, and more patient data are needed to fully investigate the potential and reliability of the proposed technique.

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Section: Hyperspectral Imaging and Processingmentioning

confidence: 99%

Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks

et al. 2019

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“…We previously demonstrated the utility of HSI for head and neck cancer detection in a subcutaneous cancer animal model (17, 19, 34) and a chemically induced, oral cancer model (35). Uptake of the 2-NBDG fluorescent deoxyglucose derivative was associated with increased metabolic activity.…”

Section: Discussionmentioning

confidence: 99%

Detection of Head and Neck Cancer in Surgical Specimens Using Quantitative Hyperspectral Imaging

Little

Wang

et al. 2017

Clinical Cancer Research

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Purpose This study intends to investigate the feasibility of using hyperspectral imaging (HSI) to detect and delineate cancers in fresh, surgical specimens of patients with head and neck cancers. Experimental Design A clinical study was conducted in order to collect and image fresh, surgical specimens from patients (N = 36) with head and neck cancers undergoing surgical resection. A set of machine-learning tools were developed to quantify hyperspectral images of the resected tissue in order to detect and delineate cancerous regions which were validated by histopathologic diagnosis. More than two million reflectance spectral signatures were obtained by HSI and analyzed using machine-learning methods. The detection results of HSI were compared with autofluorescence imaging and fluorescence imaging of two vital-dyes of the same specimens. Results Quantitative HSI differentiated cancerous tissue from normal tissue in ex vivo surgical specimens with a sensitivity and specificity of 91% and 91%, respectively, and which was more accurate than autofluorescence imaging (P < 0.05) or fluorescence imaging of 2-NBDG (P < 0.05) and proflavine (P < 0.05). The proposed quantification tools also generated cancer probability maps with the tumor border demarcated and which could provide real-time guidance for surgeons regarding optimal tumor resection. Conclusions This study highlights the feasibility of using quantitative HSI as a diagnostic tool to delineate the cancer boundaries in surgical specimens, and which could be translated into the clinic application with the hope of improving clinical outcomes in the future.

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“…Proflavine has also been applied for distinguishing between benign and neoplastic mucosa in the head and neck [50]. We previously demonstrated the utility of hyperspectral imaging for head and neck cancer detection in a subcutaneous cancer animal model [47, 51] and a chemically-induced oral cancer model [52]. One prominent advantage of hyperspectral imaging is that it does not require the use of an exogenous contrast agent.…”

Section: Discussionmentioning

confidence: 99%

Tumor margin assessment of surgical tissue specimen of cancer patients using label-free hyperspectral imaging

Fei

Wang

et al. 2017

Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XV

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We are developing label-free hyperspectral imaging (HSI) for tumor margin assessment. HSI data, hypercube (x,y,λ), consists of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on the HSI image has an optical spectrum. We developed preprocessing and classification methods for HSI data. We used spectral features from HSI data for the classification of cancer and benign tissue. We collected surgical tissue specimens from 16 human patients who underwent head and neck (H&N) cancer surgery. We acquired both HSI, autofluorescence images, and fluorescence images with 2-NBDG and proflavine from the specimens. Digitized histologic slides were examined by an H&N pathologist. The hyperspectral imaging and classification method was able to distinguish between cancer and normal tissue from oral cavity with an average accuracy of 90±8%, sensitivity of 89±9%, and specificity of 91±6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94±6%, sensitivity of 94±6%, and specificity of 95±6%. Hyperspectral imaging outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study suggests that label-free hyperspectral imaging has great potential for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the hyperspectral imaging technology is warranted for its application in image-guided surgery.

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Hyperspectral imaging of neoplastic progression in a mouse model of oral carcinogenesis

Cited by 24 publications

References 21 publications

Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks

Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks

Detection of Head and Neck Cancer in Surgical Specimens Using Quantitative Hyperspectral Imaging

Tumor margin assessment of surgical tissue specimen of cancer patients using label-free hyperspectral imaging

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