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

Resonance raman of BCC and normal skin - Liu, Cheng-hui; Sriramoju, Vidyasagar; Boydston-White, Susie; Wu, Binlin; Zhang, Chunyuan; Pei, Zhe; Sordillo, Laura; M.d, Hugh Beckman; Alfano, Robert R. (Optical Biopsy XV: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2017)
The Resonance Raman (RR) spectra of basal cell carcinoma (BCC) and normal human skin tissues were analyzed using 532nm laser excitation. RR spectral differences in vibrational fingerprints revealed skin normal and cancerous states tissues. The standard diagnosis criterion for BCC tissues are created by native RR biomarkers and its changes at peak intensity. The diagnostic algorithms for the classification of BCC and normal were generated based on SVM classifier and PCA statistical method. These statistical methods were used to analyze the RR spectral data collected from skin tissues, yielding a diagnostic sensitivity of 98.7% and specificity of 79% compared with pathological reports.
Keywords: Basal cell carcinoma; Basel cell carcinoma (BCC); Biopsy; Cell carcinoma; Diagnostic algorithms; Histology; Human skin; Human skin tissues; Laser excitation; Principal component analysis; Principal component analysis (PCA); Resonance; Resonance Raman spectra; Resonance Raman spectroscopy; Skin cancer; Skin cancers; Spectral differences; Statistical methods; Support vector machine (SVM); Support vector machines; Tissue; Tissue engineering; dept.phy

Assessment of basal cell carcinoma from normal human skin tissues using Resonance Raman spectroscopy - Liu, Chenghui; Wu, Binlin; Sordillo, Laura A; Boydstonwhite, Susie; Sriramoju, Vidyasagar; Zhang, Chunyuan; Beckman, Hugh; Zhang, Lin; Pei, Zhe; Shi, Lingyan; Alfano, R R (OSA - The Optical Society, 2018)
VRR spectroscopy was used for BCC and normal skin tissues with 532nm excitation. The spectra showed significant changes in collagen, carotenoids and lipids. These enhanced fingerprints demonstrate a potential use as label-free pathology method. © 2018 The Author(s).
Keywords: Basal cell carcinoma; Histology; Human skin; Label free; Normal skin; Resonance Raman spectroscopy; Tissue; 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.
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