Exploring Multiphoton Microscopy as a Novel Tool to Differentiate Chromophobe Renal Cell Carcinoma From Oncocytoma in Fixed Tissue Sections

Jain, Manu; Robinson, Brian D.; Wu, Binlin; Khani, Francesca; Mukherjee, Sushmita

doi:10.5858/arpa.2017-0056-oa

Cited by 24 publications

(21 citation statements)

References 29 publications

(40 reference statements)

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“…The scores were standardized for each spectrum using the following formula: (score − score mean)/score standard deviation. An SVM [108][109][110] with a linear kernel was then used for classification using the standardized features. In general, SVM attempts to find a hyper plane (a boundary line in two dimension) to separate two classes with the largest distance from the nearest class members (data points), which are called support vectors.…”

Section: Pca-svmmentioning

confidence: 99%

“…A more careful optimal feature selection method can be used and is presented elsewhere. 110 The classification performance of the SVM classifier was evaluated using statistical measures, including sensitivity, specificity, and accuracy, 111 along with the ROC curve. 112 To plot the ROC curve for the SVM classifier, the positive class (cancer) posterior probability (a data point classified into positive class) for each data point was calculated using the sigmoid function to map the SVM scores, which are the distances from the data points to the SVM separation line.…”

Section: Pca-svmmentioning

confidence: 99%

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Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy

Zhou

Liu

et al. 2019

J. Biomed. Opt.

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Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins.

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Section: Pca-svmmentioning

confidence: 99%

Section: Pca-svmmentioning

confidence: 99%

Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy

Zhou

Liu

et al. 2019

J. Biomed. Opt.

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“…Ex vivo microscopy has the potential to streamline laboratory workflow, 1 preserve valuable tissue for molecular assays, 2,3 and provide novel morphologic insights. 4 Each ex vivo microscopy technique has its own strengths and weakness, reflecting trade-offs among resolution, speed, cost, and ease of use. Light-sheet microscopy is a fluorescence microscopy technique with unparalleled ability to rapidly collect 3D microscopic information from intact specimens.…”

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confidence: 99%

Open-Top Light-Sheet Microscopy Image Atlas of Prostate Core Needle Biopsies

Reder

Glaser

McCarty

et al. 2019

Archives of Pathology &Amp; Laboratory Medicine

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Context.— Ex vivo microscopy encompasses a range of techniques to examine fresh or fixed tissue with microscopic resolution, eliminating the need to embed the tissue in paraffin or produce a glass slide. One such technique is light-sheet microscopy, which enables rapid 3D imaging. Our pathology-engineering collaboration has resulted in an open-top light-sheet (OTLS) microscope that is specifically tailored to the needs of pathology practice. Objective.— To present an image atlas of OTLS images of prostate core needle biopsies. Design.— Core needle biopsies (N = 9) were obtained from fresh radical prostatectomy specimens. Each biopsy was fixed in formalin, dehydrated in ethanol, stained with TO-PRO3 and eosin, optically cleared, and imaged using OTLS microscopy. The biopsies were then processed, paraffin embedded, and sectioned. Hematoxylin-eosin and immunohistochemical staining for cytokeratin 5 and cytokeratin 8 was performed. Results.— Benign and neoplastic histologic structures showed high fidelity between OTLS and traditional light microscopy. OTLS microscopy had no discernible effect on hematoxylin-eosin or immunohistochemical staining in this pilot study. The 3D histology information obtained using OTLS microscopy enabled new structural insights, including the observation of cribriform and well-formed gland morphologies within the same contiguous glandular structures, as well as the continuity of poorly formed glands with well-formed glands. Conclusions.— Three-dimensional OTLS microscopy images have a similar appearance to traditional hematoxylin-eosin histology images, with the added benefit of useful 3D structural information. Further studies are needed to continue to document the OTLS appearance of a wide range of tissues and to better understand 3D histologic structures.

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“…Since the contributions due to higher-order PCs significantly decrease according to the eigenvalues, limited number of PCs need to be evaluated and compared. More thorough search of optimal feature selection may be carried out [47,48] . The classification performance of the SVM classifier was evaluated using statistical measures including sensitivity, specificity, and accuracy, along with the receiver operating characteristic (ROC) curve [54,55] .…”

Section: Rr Spectral Data Analysis Methods By Pca-svmmentioning

confidence: 99%

“…It investigates the depth dependence of BCC which reveals a process of status change, and demonstrates the discrimination and classification between normal and BCC skin tissues using molecular vibrational fingerprints and a statistical model. The statistical model is based on principal component analysis (PCA) [44] and supports vector machine (SVM) [43,[45][46][47][48] . To the best of our knowledge, this study is the first to detail and discuss the depth dependence of BCC in correlation with statuses, and to distinguish BCC from normal skin tissue using the VRR technique.…”

Section: Introductionmentioning

confidence: 99%

A pilot study for distinguishing basal cell carcinoma from normal human skin tissues using visible resonance Raman spectroscopy

Liu¹,

Wu²,

Sordillo³

et al. 2019

JCMT

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Aim:The aim of the study is to test visible resonance Raman (VRR) spectroscopy for rapid skin cancer diagnosis, and evaluate its effectiveness as a new optical biopsy method to distinguish basal cell carcinoma (BCC) from normal skin tissues. Methods:The VRR spectroscopic technique was undertaken using 532 nm excitation. Normal and BCC human skin tissue samples were measured in seconds. The molecular fingerprints of various native biomolecules as biomarkers were analyzed. A principal component analysis -support vector machine (PCA-SVM) statistical analysis method based on the molecular fingerprints was developed for differentiating BCC from normal skin tissues.Results: VRR provides a rapid method and enhanced Raman signals from biomolecules with resonant and nearresonant absorption bands as compared with using a near-infrared excitation light source. The VRR technique revealed chemical composition changes of native biomarkers such as tryptophan, carotenoids, lipids and proteins. The VRR spectra from BCC samples showed a strong enhancement in proteins including collagen type I combined with amide I and amino acids, and a decrease in carotenoids and lipids. The PCA-SVM statistical analysis based on the molecular fingerprints of the biomarkers yielded a 93.0% diagnostic sensitivity, 100% specificity, and 94.5% accuracy compared with histopathology reports.Conclusion: VRR can enhance molecular vibrational modes of various native biomarkers to allow for very fast display of Raman modes in seconds. It may be used as a label-free molecular pathology method for diagnosis of skin cancer and other diseases and be used for combined treatment with Mohs surgery for BCC.

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Exploring Multiphoton Microscopy as a Novel Tool to Differentiate Chromophobe Renal Cell Carcinoma From Oncocytoma in Fixed Tissue Sections

Cited by 24 publications

References 29 publications

Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy

Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy

Open-Top Light-Sheet Microscopy Image Atlas of Prostate Core Needle Biopsies

A pilot study for distinguishing basal cell carcinoma from normal human skin tissues using visible resonance Raman spectroscopy

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