Laser induced fluorescence of cervical tissues: an in-vitro study for the diagnosis of cervical cancer from the cervicitis

Abstract

The transformation of normal cervix to cervicitis as well as to cervical cancer is accompanied with biochemical alterations at cellular level. Laser induced fluorescence can reflect those changes either as variations in the fluorescence intensity or as shift in the fluorescence maxima of bio fluorophores present in tissues. The curve resolved fluorescence investigation of tissues under 325 nm excitation provides Collagen, bound NADH and free NADH as the discrimination factors between normal, cervicitis and cervical cancer. Even though the fluorescence emission intensity derived from collagen fiber is comparable in both normal and cervicitis, a considerable reduction was observed for the cervical cancer tissues compared to the former. Fluorescence corresponding to bound NADH is found to be reduced during the progression from normal to cervicitis and to cervical cancer, whereas the free NADH shows an opposite trend. The principal component analysis (PCA) was performed to obtain classification of spectral data from different categories on a reduced dimensional space. Furthermore, to test the usefulness of the recorded fluorescence spectra in discriminating the malignant and non-malignant (cervicitis and normal) samples, a supervised machine learning model based on Support Vector Machine (SVM) was built using the PCA-reduced data. The proposed SVM model was able to detect the malignant samples with a Sensitivity of 94.19% and Specificity of 96.51%. Moreover, the Raman spectral data from the corresponding tissue sites corroborate well with the observations derived from the fluorescence measurement. The results obtained in the present pilot study strongly suggests the potential of laser induced fluorescence technique combined with multivariate data analysis tool for the diagnosis of cervicitis and cervical malignancy.

Delineation and detection of breast cancer using novel label-free fluorescence

“…Spectra typically have a higher predictive precision than a judgment made only on the basis of a biopsy. Repeated biopsies may not be necessary as often thanks to LIF emission spectra [56]. Fluorescence intensity can indeed serve as a differentiating factor between malignant and normal tissue in our proposed studies, indicating the presence of specific molecules, metabolic activities, and structural changes within the tissue [57][58][59].…”

Wavelet scattering transform and entropy features in fluorescence spectral signal analysis for cervical cancer diagnosis

“…It has the potential to detect minute morphological and biochemical changes that occur inside the cervical tissue structure during the initial stages of the disease, based on the interaction of different fluorophores (FAD, NADH, porphyrin, and collagen) present inside the tissue structure [13,14]. To enhance the efficiency and accuracy of disease detection, integrating machine learning (ML) with spectral data analysis is essential [15][16][17]. However, ML techniques typically demand an efficient feature extraction approach to effectively capture the intricate variability present in the spectral data.…”

A smartphone‐based standalone fluorescence spectroscopy tool for cervical precancer diagnosis in clinical conditions

“…Fluorescence spectroscopy captures biochemical and morphological changes occurring in different layers of tissue with disease progression. These changes play a significant role in identifying discriminatory signatures present in the fluorescence spectra of different grades of abnormalities [18][19][20][21][22][23][24]. Cervical tissue undergoes various changes with increasing abnormality such as breakage of collagen fiber cross-links in the bottom stromal layer, increase in NADH fluorescence and decrease in FAD fluorescence in the top epithelium layer due to increase in the metabolic activities, hence change in the energy cycle [25][26][27].…”