Optical biopsy for prostate cancer diagnosis using fluorescence spectroscopy - Wu, Binlin; Gao, Xin; Smith, Jason (International Journal of High Speed Electronics and Systems, 2018)
Native fluorescence spectra are acquired from fresh normal and cancerous human prostate tissues. The fluorescence data are analyzed using an unsupervised machine learning algorithm such as non-negative matrix factorization. The nonnegative spectral components are retrieved and attributed to the native fluorophores such as collagen, reduced nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD) in tissue. The retrieved scores of the components are used to estimate the relative concentrations of the native fluorophores such as NADH and FAD and the redox ratio. A supervised machine learning algorithm such as support vector machine (SVM) is used to classify normal and cancerous tissue samples based on either the relative concentrations of NADH and FAD or the redox ratio alone. Various statistical measures such as sensitivity, specificity, and accuracy, along with the area under receiver operating characteristic (ROC) curve are used to show the classification performance. A cross validation method such as leave-one-out is used to further evaluate the predictive performance of the SVM classifier to avoid bias due to overfitting, and the accuracy was found to be 93.3%.
0 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/140-001-317-060-791, pop00222 tex.type: [object Object]
Keywords: Biopsy; Diseases; FAD; Factorization; Fluorescence; Fluorescence spectroscopy; Fluorophores; Learning algorithms; Learning systems; Machine learning; Matrix algebra; NADH; Nonnegative matrix factorization; Nucleotides; Optical biopsies; Optical emission spectroscopy; Prostate cancer; Prostate cancers; Redox ratio; Supervised learning; Support vector machines; Tissue; Urology; dept.phy; fluorescence spectroscopy; intrinsic tissue emission; machine learning; nonnegative matrix factorization; optical biopsy; peer-reviewed; redox ratio; support vector machine

Optical biopsy using fluorescence spectroscopy for prostate cancer diagnosis - Wu, Binlin; Gao, Xin; Smith, Jason; Bailin, Jacob (Therapeutics and Diagnostics in Urology: Lasers, Robotics, Minimally Invasive, and Advanced Biomedical Devices, 2017)
Native fluorescence spectra are acquired from fresh normal and cancerous human prostate tissues. The fluorescence data are analyzed using a multivariate analysis algorithm such as non-negative matrix factorization. The nonnegative spectral components are retrieved and attributed to the native fluorophores such as collagen, reduced nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD) in tissue. The retrieved weights of the components, e.g. NADH and FAD are used to estimate the relative concentrations of the native fluorophores and the redox ratio. A machine learning algorithm such as support vector machine (SVM) is used for classification to distinguish normal and cancerous tissue samples based on either the relative concentrations of NADH and FAD or the redox ratio alone. The classification performance is shown based on statistical measures such as sensitivity, specificity, and accuracy, along with the area under receiver operating characteristic (ROC) curve. A cross validation method such as leave-one-out is used to evaluate the predictive performance of the SVM classifier to avoid bias due to overfitting.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/116-092-737-895-249, pop00138 tex.type: [object Object]
Keywords: Artificial intelligence; Biopsy; Cross validation; Diseases; Factorization; Fluorescence; Fluorescence spectroscopy; Fluorophores; Learning algorithms; Learning systems; Matrix algebra; Multivariant analysis; Nonnegative matrix factorization; Nucleotides; Optical biopsies; Prostate cancers; Redox ratio; Robotics; Support vector machines; Tissue; Urology; cross validation; dept.phy; fluorescence spectroscopy; machine learning; nonnegative matrix factorization; optical biopsy; prostate cancer; redox ratio; support vector machine

Rapid measurement of meat spoilage using fluorescence spectroscopy - Wu, Binlin; Dahlberg, Kevin; Gao, Xin; Smith, Jason; Bailin, Jacob (Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XV, 2017)
Food spoilage is mainly caused by microorganisms, such as bacteria. In this study, we measure the autofluorescence in meat samples longitudinally over a week in an attempt to develop a method to rapidly detect meat spoilage using fluorescence spectroscopy. Meat food is a biological tissue, which contains intrinsic fluorophores, such as tryptophan, collagen, nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) etc. As meat spoils, it undergoes various morphological and chemical changes. The concentrations of the native fluorophores present in a sample may change. In particular, the changes in NADH and FAD are associated with microbial metabolism, which is the most important process of the bacteria in food spoilage. Such changes may be revealed by fluorescence spectroscopy and used to indicate the status of meat spoilage. Therefore, such native fluorophores may be unique, reliable and nonsubjective indicators for detection of spoiled meat. The results of the study show that the relative concentrations of all above fluorophores change as the meat samples kept in room temperature (~19° C) spoil. The changes become more rapidly after about two days. For the meat samples kept in a freezer (~-12° C), the changes are much less or even unnoticeable over a-week-long storage.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/026-929-135-501-547, pop00136 tex.type: [object Object]
Keywords: Amino acids; Bacteria; Biological tissues; Biomolecules; Collagen; Flavin adenine dinucleotide; Fluorescence; Fluorescence spectroscopy; Fluorophores; Food microbiology; Histology; Meat food; Meats; Metabolism; Microbial metabolism; Molecular biology; NADH; Nicotinamide adenine dinucleotides; Nucleotides; Physiology; Rapid measurement; Relative concentration; Spectrum analysis; Spoilage; Tissue; Tryptophan; dept.phy

A Rapid Method Based on Fluorescence Spectroscopy for Meat Spoilage Detection - Wu, Binlin; Dahlberg, Kevin; Gao, Xin; Smith, Jason; Bailin, Jacob (International Journal of High Speed Electronics and Systems, 2018)
Food spoilage is mainly caused by microorganisms, such as bacteria. In this study, we measure the autofluorescence in meat samples longitudinally over a week in an attempt to develop a method to rapidly detect meat spoilage using fluorescence spectroscopy. Meat food is a biological tissue, which contains intrinsic fluorophores, such as tryptophan, collagen, nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) etc. As meat spoils, it undergoes various morphological and chemical changes. The concentrations of the native fluorophores present in a sample may change. In particular, the changes in NADH and FAD are associated with microbial metabolism, which is the most important process of the bacteria in food spoilage. Such changes may be revealed by fluorescence spectroscopy and used to indicate the status of meat spoilage. Therefore, such native fluorophores may be unique, reliable and non-subjective indicators for detection of spoiled meat. The results of the study show that the relative concentrations of all above fluorophores change as the meat samples kept in room temperature (~19°C) spoil. The changes become more rapidly after about two days. For the meat samples kept in a freezer (~ -12°C), the changes are much less or even unnoticeable over a-week-long storage.
4 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/014-150-261-026-674, pop00221 tex.type: [object Object]
Keywords: Amino acids; Autofluorescence; Bacteria; Biological tissues; Collagen; Flavin adenine dinucleotide; Fluorescence; Fluorescence spectroscopy; Fluorophores; Food microbiology; Meats; Metabolism; Microbial metabolism; NADH; Nicotinamide adenine dinucleotides; Nucleotides; Relative concentration; Spoilage; bacteria; collagen; dept.phy; fluorescence spectroscopy; meat food; microbial metabolism; peer-reviewed; tryptophan

Characterization and discrimination of human breast cancer and normal breast tissues using resonance Raman spectroscopy - Wu, Binlin; Smith, Jason; Zhang, Lin; Gao, Xin; Alfano, Robert R. (Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2018)
Worldwide breast cancer incidence has increased by more than twenty percent in the past decade. It is also known that in that time, mortality due to the affliction has increased by fourteen percent. Using optical-based diagnostic techniques, such as Raman spectroscopy, has been explored in order to increase diagnostic accuracy in a more objective way along with significantly decreasing diagnostic wait-times. In this study, Raman spectroscopy with 532-nm excitation was used in order to incite resonance effects to enhance Stokes Raman scattering from unique biomolecular vibrational modes. Seventy-two Raman spectra (41 cancerous, 31 normal) were collected from nine breast tissue samples by performing a ten-spectra average using a 500-ms acquisition time at each acquisition location. The raw spectral data was subsequently prepared for analysis with background correction and normalization. The spectral data in the Raman Shift range of 750- 2000 cm-1 was used for analysis since the detector has highest sensitivity around in this range. The matrix decomposition technique nonnegative matrix factorization (NMF) was then performed on this processed data. The resulting leave-oneout cross-validation using two selective feature components resulted in sensitivity, specificity and accuracy of 92.6%, 100% and 96.0% respectively. The performance of NMF was also compared to that using principal component analysis (PCA), and NMF was shown be to be superior to PCA in this study. This study shows that coupling the resonance Raman spectroscopy technique with subsequent NMF decomposition method shows potential for high characterization accuracy in breast cancer detection.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/106-835-441-234-339, pop00115
Keywords: Background correction; Biopsy; Breast Cancer; Breast cancer; Breast cancer detection; Cancer diagnosis; Couplings; Decomposition methods; Diagnostic techniques; Diseases; Factorization; Learning systems; Machine learning; Matrix algebra; Molecular optical pathology; Nonnegative matrix factorization; Principal component analysis; Principal component analysis (PCA); Raman scattering; Raman spectroscopy; Resonance; Resonance Raman spectroscopy; Spectrum analysis; Support vector machine; Support vector machines; Tissue; dept.phy

Distinguishing metastatic triple-negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy - Bendau, Ethan; Smith, Jason; Zhang, Lin; Ackerstaff, Ellen; Kruchevsky, Natalia; Wu, Binlin; Koutcher, Jason A.; Alfano, Robert; Shi, Lingyan (Journal of Biophotonics, 2020)
Triple-negative breast cancer (TNBC) is an aggressive subset of breast cancer that is more common in African-American and Hispanic women. Early detection followed by intensive treatment is critical to improving poor survival rates. The current standard to diagnose TNBC from histopathology of biopsy samples is invasive and time-consuming. Imaging methods such as mammography and magnetic resonance (MR) imaging, while covering the entire breast, lack the spatial resolution and specificity to capture the molecular features that identify TNBC. Two nonlinear optical modalities of second harmonic generation (SHG) imaging of collagen, and resonance Raman spectroscopy (RRS) potentially offer novel rapid, label-free detection of molecular and morphological features that characterize cancerous breast tissue at subcellular resolution. In this study, we first applied MR methods to measure the whole-tumor characteristics of metastatic TNBC (4T1) and nonmetastatic estrogen receptor positive breast cancer (67NR) models, including tumor lactate concentration and vascularity. Subsequently, we employed for the first time in vivo SHG imaging of collagen and ex vivo RRS of biomolecules to detect different microenvironmental features of these two tumor models. We achieved high sensitivity and accuracy for discrimination between these two cancer types by quantitative morphometric analysis and nonnegative matrix factorization along with support vector machine. Our study proposes a new method to combine SHG and RRS together as a promising novel photonic and optical method for early detection of TNBC.
22 citations (Crossref) [2023-10-31] _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/jbio.202000005
Keywords: Breast; Breast Neoplasms; Collagen; Diseases; Factorization; Feature extraction; Female; Harmonic generation; Humans; Label-free detection; Lactate concentration; Magnetic resonance; Mammography; Matrix algebra; Molecular imaging; Morphological features; Morphometric analysis; Nonlinear optics; Nonnegative matrix factorization; Raman spectrometry; Raman spectroscopy; Resonance Raman spectroscopy; Second Harmonic Generation Microscopy; Spectrum Analysis; Raman; Subcellular resolution; Support vector machines; Triple Negative Breast Neoplasms; Triple-negative breast cancers; Tumors; breast; breast tumor; dept.phy; diagnostic imaging; female; human; mammography; peer-reviewed; second harmonic generation microscopy; triple negative breast cancer

Convolutional Neural Network for Binary Classification of Chromophobe Renal Cell Carcinoma and Oncocytoma - Cheng, Andrew; Icaza, Michael; Judd, Nicholas; Smith, Jason; Mukherjee, Sushmita; Jain, Manu; Wu, Binlin (Frontiers in Optics + Laser Science 2021 (2021), paper JTh5A.80, 2021)
Multiphoton microscopy images of chromophobe renal cell carcinoma and renal oncocytoma were classified using a convolutional neural network inspired by techniques in recent architectures and yielded over 70% accuracy.
0 citations (Crossref) [2023-10-31]
Keywords: dept.phy

A pilot study for distinguishing chromophobe renal cell carcinoma and oncocytoma using second harmonic generation imaging and convolutional neural network analysis of collagen fibrillar structure - Wu, Binlin; Judd, Nicolas; Smith, Jason; Icaza, Michael; Mukherjee, Sushmita; Jain, Manu; Gallagher, Ryan M; Szeligowski, Richard V (Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2018)
A clear distinction between oncocytoma and chromophobe renal cell carcinoma (chRCC) is critically important for clinical management of patients. But it may often be difficult to distinguish the two entities based on hematoxylin and eosin (H and E) stained sections alone. In this study, second harmonic generation (SHG) signals which are very specific to collagen were used to image collagen fibril structure. We conduct a pilot study to develop a new diagnostic method based on the analysis of collagen associated with kidney tumors using convolutional neural networks (CNNs). CNNs comprise a type of machine learning process well-suited for drawing information out of images. This study examines a CNN model's ability to differentiate between oncocytoma (benign), and chRCC (malignant) kidney tumor images acquired with second harmonic generation (SHG), which is very specific for collagen matrix. To the best of our knowledge, this is the first study that attempts to distinguish the two entities based on their collagen structure. The model developed from this study demonstrated an overall classification accuracy of 68.7% with a specificity of 66.3% and sensitivity of 74.6%. While these results reflect an ability to classify the kidney tumors better than chance, further studies will be carried out to (a) better realize the tumor classification potential of this method with a larger sample size and (b) combining SHG with two-photon excited intrinsic fluorescence signal to achieve better classification. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/110-186-936-817-103
Keywords: Biomedical signal processing; Biopsy; Chromophobe renal cell carcinoma; Classification accuracy; Collagen; Convolution; Convolutional Neural Networks (CNN); Convolutional neural network; Convolutional neural networks (CNN); Fluorescence; Harmonic analysis; Harmonic generation; Intrinsic fluorescence signals; Kidney tumor; Learning systems; Machine learning; Multiphoton microscopy; Multiphoton microscopy (MPM); Multiphoton processes; Neural networks; Nonlinear optics; Patient monitoring; Renal cell carcinoma; Renal oncocytoma; Second harmonic generation; Tumor classification; Tumors; dept.phy

Statistical analysis and machine learning algorithms for optical biopsy - Wu, Binlin; Liu, Cheng-hui; Boydston-White, Susie; Beckman, Hugh; Sriramoju, Vidyasagar; Sordillo, Laura; Zhang, Chunyuan; Zhang, Lin; Shi, Lingyan; Smith, Jason; Bailin, Jacob; Alfano, Robert R. (Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2018)
Analyzing spectral or imaging data collected with various optical biopsy methods is often times difficult due to the complexity of the biological basis. Robust methods that can utilize the spectral or imaging data and detect the characteristic spectral or spatial signatures for different types of tissue is challenging but highly desired. In this study, we used various machine learning algorithms to analyze a spectral dataset acquired from human skin normal and cancerous tissue samples using resonance Raman spectroscopy with 532nm excitation. The algorithms including principal component analysis, nonnegative matrix factorization, and autoencoder artificial neural network are used to reduce dimension of the dataset and detect features. A support vector machine with a linear kernel is used to classify the normal tissue and cancerous tissue samples. The efficacies of the methods are compared.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/030-448-994-607-54X, pop00047
Keywords: Artificial neural network; Biological basis; Biopsy; Cancer diagnosis; Cancerous tissues; Factorization; Learning algorithms; Learning systems; Least squares approximations; Machine learning; Matrix algebra; Neural networks; Nonnegative matrix factorization; Optical biopsies; Optical biopsy; Partial least square (PLS); Partial least squares; Principal component analysis; Resonance Raman spectroscopy; Spatial signature; Support vector machine; Support vector machines; Tissue; Tissue engineering; dept.phy