Diagnostic model based on Raman spectra of normal, hyperplasia and prostate adenocarcinoma tissuesin vitro

Abstract

Abstract. This study evaluated the use of Raman spectroscopy to identify the spectral differences between normal (N), benign hyperplasia (BPH) and adenocarcinoma (CaP) in fragments of prostate biopsies in vitro with the aim of developing a spectral diagnostic model for tissue classification. A dispersive Raman spectrometer was used with 830 nm wavelength and 80 mW excitation. Following Raman data collection and tissue histopathology (48 fragments diagnosed as N, 43 as BPH and 14 as CaP), two diagnostic models were developed in order to extract diagnostic information: the first using PCA and Mahalanobis analysis techniques and the second one a simplified biochemical model based on spectral features of cholesterol, collagen, smooth muscle cell and adipocyte. Spectral differences between N, BPH and CaP tissues, were observed mainly in the Raman bands associated with proteins, lipids, nucleic and amino acids. The PCA diagnostic model showed a sensitivity and specificity of 100%, which indicates the ability of PCA and Mahalanobis distance techniques to classify tissue changes in vitro. Also, it was found that the relative amount of collagen decreased while the amount of cholesterol and adipocyte increased with severity of the disease. Smooth muscle cell increased in BPH tissue. These characteristics were used for diagnostic purposes.

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 2: in-vivo tumor-targeting using a classification model combining spectral and MRI-radiomics features

“… 22 , 56 Finally, the band from 1760 to , influenced by DNA/RNA, proteins and phospholipids, is also present on benign prostatic hyperplasia. 23 , 56 …”

“… 25 – 28 Furthermore, with the development of optical fiber RS probes, this technique is moving to clinical applications 29 ; different optical probe designs have been used for in-vivo tissue characterization (in human and animal models) for targeting skin cancer in open surgeries, 30 minimally invasive diagnosis of lung cancers, 31 bladder cancer detection using a superficial and nonsuperficial Raman probes, 32 observation of skin changes after breast cancer treatment, 33 and others. 34 – 37 In prostate applications, it has been used for ex-vivo characterization and in-vivo margin detection, 19 , 20 , 23 , 38 , 39 but, to the best of our knowledge, so far, not for real-time in-vivo prostate tumor burden confirmation, which can provide great benefit for clinical procedures.…”

“…[17][18][19][20] Raman spectroscopy (RS), on the other hand, characterizes microscopic information of prostate tissue, providing real-time molecular signatures and taking advantage of the tissue's highly sensitive and specific optical properties. 18,[21][22][23][24] Based on inelastic light scattering, RS has been used for years for ex-vivo sample characterization, producing spectra with molecular vibrational states information, showing great potential for detecting several diseases. [25][26][27][28] Furthermore, with the development of optical fiber RS probes, this technique is moving to clinical applications 29 ; different optical probe designs have been used for in-vivo tissue characterization (in human and animal models) for targeting skin cancer in open surgeries, 30 minimally invasive diagnosis of lung cancers, 31 bladder cancer detection using a superficial and nonsuperficial Raman probes, 32 observation of skin changes after breast cancer treatment, 33 and others.…”

“…Paper 220064GR received Mar. 23,2022; accepted for publication Aug. 12, 2022; published online Sep. 9, 2022.…”

Application and Progress of Raman Spectroscopy in Male Reproductive System

“…In a study by Stone et al (2007) , prostate cancer cells were found to have a higher nuclear/cytoplasmic ratio and higher DNA, choline and glyceric acid concentrations than prostate hyperplastic tissue. Based on the differences in the biochemical composition like proteins, lipids, nucleic acids and amino acids, and corresponding Raman bands in normal, BPH and prostatic adenocarcinoma, Lopes et al (2011) developed a diagnostic algorithm combined PCA with Mahalanobis distance discriminant analysis to identify normal, BPH and adenocarcinoma tissues from in vitro prostate biopsy fragments and then developed a spectral diagnostic model for tissue classification. Prostate lesion samples were classified into three histological groups in this model with 100% sensitivity and specificity.…”