Analysis of Stokes shift spectra for distinguishing human prostate cancerous and normal tissues using machine learning methods - Mo, Haiding; Wang, Eric; Tian, Yuke; Pu, Yang; Wu, Binlin; Alfano, Robert R. (Photonic diagnosis, monitoring, prevention, and treatment of infections and inflammatory diseases 2021, 2021)
Early detection of prostate cancer is critical for the success of cancer therapy. It is believed that the biochemical changes that cause the optical spectra changes would appear earlier than the histological aberration. The aim of this ex vivo study was to evaluate the ability of Stokes Shift Spectra (S3) to identify human prostate cancerous tissues from the normal. Fifteen (15) pairs of with pathologically confirmed human prostate cancerous and normal tissues underwent Stokes Shift Spectra measurements with selective wavelength interval of 40 nm. The spectra were then analyzed using machine learning (ML) algorithms to classify the two types of tissues. The ML algorithms including principal component analysis (PCA) and nonnegative matrix factorization (NMF) were used for dimension reduction and feature detection. The characteristic component spectra were used to identify the key fluorophores related to carcinogenesis. The results show that these key fluorophores within tissue, e.g., tryptophan, collagen, and NADH, have different relative concentrations between cancerous and normal tissues. A multi-class classification was performed using support vector machines (SVMs). A leave-one-out cross validation was used to evaluate the performance of the classification with the gold standard histopathological results as the ground truth. The results with high sensitivity and specificity indicate that the S3 method is effective for detecting changes of fluorophore composition in human prostate tissues due to the development of cancer. © 2021 SPIE.
0 citations (Crossref) [2023-10-31]
Keywords: Amino acids; Diagnosis; Dimension reduction; Diseases; Factorization; Fluorophores; Histology; Learning systems; Leave-one-out cross validations; Machine learning methods; Matrix algebra; Multi-class classification; Nonnegative matrix factorization; Relative concentration; Spectra measurements; Statistical methods; Support vector machine (SVMs); Support vector machines; Tissue; Tissue engineering; Urology; dept.phy

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

Chapter 15 - Tryptophan fluorescence and machine learning to study the aggressiveness of prostate cancer cell lines: A pilot study - Xue, Jianpeng; Mo, Haiding; Tian, Yuke; Tang, Rui; Wu, Binlin (Biophotonics, Tryptophan and Disease, 2022)
We report on the use of label-free, native fluorescence (NFL) spectroscopy and machine learning (ML) algorithms to study the correlation of relative tryptophan levels with prostate cancer aggressiveness. Three extensively studied prostate cancer cell lines were used; PC3, an aggressive, androgen-resistant line, with a high tendency to metastasize in vivo, DU-145, a less aggressive cancer cell line, also androgen-resistant, and LNCaP, an androgen sensitive line, which has a low tendency to metastasize. Using an excitation of 300nm, differences in the NFL spectral profiles from these cell lines were found to correlate with changes in the relative concentrations of tryptophan and reduced nicotinamide adenine dinucleotide (NADH). The use of ML may present a powerful tool for the assessment of the likelihood of a cancer to metastasize. This technique could aid in the decision whether to use highly aggressive adjuvant chemotherapy or radiation therapy after surgical resection of a prostate cancer.
Keywords: Fluorescence spectroscopy; Machine learning; Metastatic cancer; Native fluorescence; Prostate cancer; Support vector machine; Tryptophan; dept.phy

Optical Molecular Signatures of IDH Genotype for Classification of Human Glioma Using Visible Resonance Raman Spectroscopy - Zhang, Liang; Zhou, Yan; Wu, Binlin; Zhang, Shengjia; Zhu, Ke; Liu, C.; Alfano, Robert; Yu, Xinguang (Journal of Biophotonics, 2026)
Keywords: Biomarker; Gene; Genotype; Glioma; IDH mutation; IDH1; Isocitrate dehydrogenase; Mutation; Raman spectroscopy; Resonance Raman spectroscopy; dept.phy; glioma; optical molecular biopsy; visible resonance Raman spectroscopy

Lignin-derived carbon dots from leaf waste as sustainable plant sunscreens and UV-protective foliar coatings - Thangavelu, Raja; Gray, Sarah; Wu, Binlin; Pellegrino, Richard; da Silva, Washington; Zuverza-Mena, Nubia; Dimkpa, Christian; White, Jason (Environmental Science Nano, 2026)
Keywords: Agricultural waste; Agriculture; Carbon fibers; Lignin; Maple; Sustainable agriculture; Sustainable production; dept.phy

A handheld visible resonance raman analyzer used in intraoperative detection of human glioma - Zhang, Liang; Zhou, Yan; Wu, Binlin; Zhang, Shengjia; Zhu, Ke; Liu, Cheng-Hui; Yu, Xinguang; Alfano, Robert R. (Cancers, 2023)
There is still a lack of reliable intraoperative tools for glioma diagnosis and to guide the maximal safe resection of glioma. We report continuing work on the optical biopsy method to detect glioma grades and assess glioma boundaries intraoperatively using the VRR-LRRTM Raman analyzer, which is based on the visible resonance Raman spectroscopy (VRR) technique. A total of 2220 VRR spectra were collected during surgeries from 63 unprocessed fresh glioma tissues using the VRR-LRRTM Raman analyzer. After the VRR spectral analysis, we found differences in the native molecules in the fingerprint region and in the high-wavenumber region, and differences between normal (control) and different grades of glioma tissues. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with the gold standard of histopathology reports of glioma. The VRR-LRRTM Raman analyzer may be a new label-free, real-time optical molecular pathology tool aiding in the intraoperative detection of glioma and identification of tumor boundaries, thus helping to guide maximal safe glioma removal and adjacent healthy tissue preservation.
0 citations (Crossref) [2023-10-31] Number: 6
Keywords: dept.phy; glioma; human brain; intraoperative; optical biopsy; optical molecular pathology; peer-reviewed; portable optical-fiber-probe Raman analyzer; visible resonance Raman (VRR)

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

Glioma tumors classified using visible resonance raman spectroscopy and machine learning - Wu, Binlin; Zhou, Yan; Zhang, Liang; Zhang, Shengjia; Yu, Xinguang; Wang, Eric; Zhu, Ke; Liu, Cheng-hui; Alfano, Robert R. (Frontiers in Optics, 2020)
Machine learning algorithms were used to classify and analyze spectral data collected by visible resonance Raman spectroscopy to distinguish normal human brain tissue and glioma tumor tissues at different grades and show promising results. © OSA 2020 © 2020 The Author(s)
0 citations (Crossref) [2023-10-31]
Keywords: dept.phy; ⛔ No DOI found

Human glioma tumors detection by a portable visible resonance Raman analyzer with a hand-held optical fiber probe - Zhou, Yan; Zhang, Shengjia; Wu, Binlin; Yu, Xinguang; Cheng, Gangge; Zhu, Ke; Zhao, Mingyue; Zheng, Jichun; Zhang, Liang; Zhang, Mingqian; Liang, Qijun; Go, Yun Ii; et al. (Biomedical Vibrational Spectroscopy 2020: Advances in Research and Industry, 2020)
Based on Visible Resonance Raman (VRR) method, we have developed a novel label-free portable VRR LRR2000 Raman analyzer with a portable fiber-optic probe and used it for the classification of human gliomas ex vivo and for the analysis of changes in tumor chemical compositions in molecular level. The purpose of this study was to examine the performance of the LRR2000 Raman analyzer as an optical biopsy tool for detecting human brain tumors compared to the commercial laboratory HR800 and WITec300 micro confocal Raman spectroscopy instruments. As of 2018, a total 1,938 VRR spectra were collected using LRR2000, HR800 and WITec300 Raman system, ex vivo. Identification of the four grades of glioma tumors and control tissues was performed based on the characteristic native molecular fingerprints. LRR2000 demonstrated consistent diagnostic results with HR800 and WITec300 Raman systems. LRR2000 showed the advantages of high speed, convenience and low cost compared to the two confocal micro Raman systems. Using artificial intelligence (AI)-based analysis of part of the data, the cross-validated accuracy for identifying glioma tumors is ~90% compared with gold standard histopathology examination.
3 citations (Crossref) [2023-10-31]
Keywords: Biopsy; Brain; Chemical analysis; Handhelds; Histology; Human brain; Industrial research; Molecular biology; Optical biopsies; Optical fibers; Optical molecular Pathology; Palmprint recognition; Principal component analysis; Principle component analysis; Probes; Resonance; Resonance Raman; Spectroscopy; Spectrum analysis; Support vector machines; Tissue engineering; Tumors; Vibrational spectroscopy; dept.phy; glioma tumor; handheld LRR2000 analyzer; human brain tissues; optical biopsy; principle component analysis(PCA); support vector machine (SVM); visible resonance Raman (VRR)