Oral cancer is among the top three types of cancers in India which accounts for about 30 percent of all types of cancer. We propose here a portable and cost-effective 3D printed smartphone based bimodal (spectroscopy and imaging) device for detection of oral cancer at an early stage. The device has the ability to perform fluorescence spectroscopy and imaging on a single platform using smartphone as an optical spectrometer and a CMOS camera respectively. A miniature 405 nm laser diode has been used as a source. Fluorescence spectra and images of some known fluorophores such as fluorescein, rhodamine, flavin adenine dinucleotide (FAD) and proto-porphyrin (PpIX) have been recorded using the proposed smartphone-based device for validation. The wavelength resolution of device for spectral measurements is 0.25 nm per pixel in the visible range and for imaging the total area captured at the detector is 1cm2 . Preliminary studies have been performed on patients with oral precancer and cancer to evaluate the efficacy of the proposed system for in-vivo diagnosis of the disease at an early stage.
We report a multi-class classification model built using random forest (RF) and synthetic minority oversampling technique (SMOTE) applied to extracted intrinsic fluorescence (IF) data to detect normal, pre-cancer, and cancer samples. Important features in the fluorescence signal often get suppressed by the noise which makes denoising an essential pre-processing step. The proposed algorithm implements a wavelet-based denoising technique as a pre-processing step before data analysis which utilizes the “coif3” mother wavelet function to denoise IF data. Synthetic minority oversampling technique (SMOTE) is utilized to generate a balanced dataset. We achieved the best classification for the denoised balanced dataset with accuracy, sensitivity, and specificity above 90% for normal/pre-cancer and precancer/cancer groups. Further, the receiver operating curve (ROC) shows a clear distinction among three grades with the area under curve (AUC) of 0.96 for normal and precancer samples and 1.00 for cancer samples. The python script prepared for this study is available on GitHub and Signal Science Lab.
An imaging device based on fluorescence for in vivo detection of oral cancer has been developed. Images collected from cancerous patients and normal volunteers have shown a clear difference in fluorescence intensity
In this study a hand held probe has been automated using a motorized stage and LabVIEW based control and acquisition system for in-vitro cervical precancer diagnosis. The stage movement is controlled by two motors for vertical and horizontal movement while a third motor is utilized for changing the polarization state. The data acquisition and storing was thus performed by moving the probe to a specific site, acquiring polarized fluorescence and elastic scattering signals and creating a user friendly graphical user interface(GUI) panel to suit our needs. Another GUI in MATLAB has also been developed for the real time analysis of data captured through the automatic acquisition system. Three windows were created inside the GUI to display calculated intrinsic fluorescence, for statistical analysis and classification. Principal component analysis was applied to extract the features and classification was done by Mahalanobis distance based algorithm. The classification results have been presented of different grades of cervical cancer
An in-house fabricated portable device has been tested to detect cervical precancer through the intrinsic fluorescence from human cervix of the whole uterus in a clinical setting. A previously validated technique based on simultaneously acquired polarized fluorescence and polarized elastic scattering spectra from a turbid medium is used to extract the intrinsic fluorescence. Using a diode laser at 405 nm, intrinsic fluorescence of flavin adenine dinucleotide, which is the dominant fluorophore and other contributing fluorophores in the epithelium of cervical tissue, has been extracted. Different grades of cervical precancer (cervical intraepithelial neoplasia; CIN) have been discriminated using principal component analysis-based Mahalanobis distance and linear discriminant analysis. Normal, CIN I and CIN II samples have been discriminated from one another with high sensitivity and specificity at 95% confidence level. This ex vivo study with cervix of whole uterus samples immediately after hysterectomy in a clinical environment indicates that the in-house fabricated portable device has the potential to be used as a screening tool for in vivo precancer detection using intrinsic fluorescence.
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