Clinical label-free biochemical and metabolic fluorescence lifetime endoscopic imaging of precancerous and cancerous oral lesions

Duran-Sierra, Elvis; Cheng, Shannon; Cuenca-Martinez, Rodrigo; Malik, Bilal; Maitland, Kristen C.; Cheng, Yueming; Wright, John M.; Ahmed, Beena; Ji, Jim; Martinez, Mathias; Alkhalil, Moustafa; Al-Enazi, Hussain; Jo, Javier A.

doi:10.1016/j.oraloncology.2020.104635

Cited by 31 publications

(27 citation statements)

References 31 publications

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“…From the study in [ 44 ], oral tissue samples were used for validation in this section that belong to different regions in the oral cavity. Dysplastic and cancerous oral lesions were analyzed by in vivo clinical endogenous mFLIM images.…”

Section: Synthetic and Experimental Validationmentioning

confidence: 99%

“…In multi-spectral FLIM (mFLIM) several spectral channels are recorded simultaneously, and the resulting time-responses are concatenated to construct a characteristic time-frequency signature [ 5 , 6 ]. The mFLIM methodology has shown to be a powerful medical resource for early and non-invasive diagnosis for different pathologies such as cardiovascular and dermatological diseases [ 5 , 7 – 9 ], oral pre-cancer and cancer conditions [ 10 , 11 ], colonic dysplasia [ 12 ], skin cancer [ 13 , 14 ], and to assess therapeutic responses of anticancer drugs [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Blind deconvolution estimation by multi-exponential models and alternated least squares approximations: Free-form and sparse approach

Campos-Delgado¹,

Gutierrez-Navarro²,

Salinas-Martínez³

et al. 2021

PLoS ONE

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The deconvolution process is a key step for quantitative evaluation of fluorescence lifetime imaging microscopy (FLIM) samples. By this process, the fluorescence impulse responses (FluoIRs) of the sample are decoupled from the instrument response (InstR). In blind deconvolution estimation (BDE), the FluoIRs and InstR are jointly extracted from a dataset with minimal a priori information. In this work, two BDE algorithms are introduced based on linear combinations of multi-exponential functions to model each FluoIR in the sample. For both schemes, the InstR is assumed with a free-form and a sparse structure. The local perspective of the BDE methodology assumes that the characteristic parameters of the exponential functions (time constants and scaling coefficients) are estimated based on a single spatial point of the dataset. On the other hand, the same exponential functions are used in the whole dataset in the global perspective, and just the scaling coefficients are updated for each spatial point. A least squares formulation is considered for both BDE algorithms. To overcome the nonlinear interaction in the decision variables, an alternating least squares (ALS) methodology iteratively solves both estimation problems based on non-negative and constrained optimizations. The validation stage considered first synthetic datasets at different noise types and levels, and a comparison with the standard deconvolution techniques with a multi-exponential model for FLIM measurements, as well as, with two BDE methodologies in the state of the art: Laguerre basis, and exponentials library. For the experimental evaluation, fluorescent dyes and oral tissue samples were considered. Our results show that local and global perspectives are consistent with the standard deconvolution techniques, and they reached the fastest convergence responses among the BDE algorithms with the best compromise in FluoIRs and InstR estimation errors.

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Section: Synthetic and Experimental Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blind deconvolution estimation by multi-exponential models and alternated least squares approximations: Free-form and sparse approach

Campos-Delgado¹,

Gutierrez-Navarro²,

Salinas-Martínez³

et al. 2021

PLoS ONE

Self Cite

View full text Add to dashboard Cite

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“…Optical images could be used to study biophysical processes in living systems and to monitor morphological and physiological changes such as precancerous or cancerous conditions [21]. In a previous study, it has been indicated that epithelial-mesenchymal transition (EMT) contributes to chemoresistance acquisition [22] and indeed CP-resistant cell images result in the formation of dense 3D structures with characteristic rough surfaces compared to wild type cells.…”

Section: Cellsmentioning

confidence: 99%

Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-Resistance by Feature Extraction of Gray Level Co-Occurrence Matrix Using Optical Images

Huang

Lian

et al. 2020

Diagnostics

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Ovarian cancer is the most malignant of all gynecological cancers. A challenge that deteriorates with ovarian adenocarcinoma in neoplastic disease patients has been associated with the chemoresistance of cancer cells. Cisplatin (CP) belongs to the first-line chemotherapeutic agents and it would be beneficial to identify chemoresistance for ovarian adenocarcinoma cells, especially CP-resistance. Gray level co-occurrence matrix (GLCM) was characterized imaging from a numeric matrix and find its texture features. Serous type (OVCAR-4 and A2780), and clear cell type (IGROV1) ovarian carcinoma cell lines with CP-resistance were used to demonstrate GLCM texture feature extraction of images. Cells were cultured with cell density of 6 × 105 in a glass-bottom dish to form a uniform coverage of the glass slide to get the optical images by microscope and DVC camera. CP-resistant cells included OVCAR-4, A2780 and IGROV and had the higher contrast and entropy, lower energy, and homogeneity. Signal to noise ratio was used to evaluate the degree for chemoresistance of cell images based on GLCM texture feature extraction. The difference between wile type and CP-resistant cells was statistically significant in every case (p < 0.001). It is a promising model to achieve a rapid method with a more reliable diagnostic performance for identification of ovarian adenocarcinoma cells with CP-resistance by feature extraction of GLCM in vitro or ex vivo.

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“…By adding an RGB (red, green, and blue) additive color model in silico, a strong inverse correlation between the green color component of AFI images and the severity of the mucosal inflammation was found [10]. In a similar prospective study by Moriichi et al, 135 WLE and AFI images from the same lesion in 43 patients with UC were evaluated by eleven…”

Section: Afi In Intestinal Inflammationmentioning

confidence: 99%

“…In gastroenterology, ex vivo lifetime analysis identified that neoplastic colonic polyps exhibit shorter lifetime values than normal tissue upon 435 nm excitation [134]. Recently, an in vivo clinical study applied FLIM endoscopy for oral lesions, indicating a contrast in cancerous vs. normal oral tissue, e.g., shorter lifetimes for collagen and NADH [135]. However, these encouraging results need to be validated by larger systemic studies.…”

Section: Fluorescence Lifetime Imaging Microscopymentioning

confidence: 99%

Recent Advances and the Potential for Clinical Use of Autofluorescence Detection of Extra-Ophthalmic Tissues

et al. 2020

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The autofluorescence (AF) characteristics of endogenous fluorophores allow the label-free assessment and visualization of cells and tissues of the human body. While AF imaging (AFI) is well-established in ophthalmology, its clinical applications are steadily expanding to other disciplines. This review summarizes clinical advances of AF techniques published during the past decade. A systematic search of the MEDLINE database and Cochrane Library databases was performed to identify clinical AF studies in extra-ophthalmic tissues. In total, 1097 articles were identified, of which 113 from internal medicine, surgery, oral medicine, and dermatology were reviewed. While comparable technological standards exist in diabetology and cardiology, in all other disciplines, comparability between studies is limited due to the number of differing AF techniques and non-standardized imaging and data analysis. Clear evidence was found for skin AF as a surrogate for blood glucose homeostasis or cardiovascular risk grading. In thyroid surgery, foremost, less experienced surgeons may benefit from the AF-guided intraoperative separation of parathyroid from thyroid tissue. There is a growing interest in AF techniques in clinical disciplines, and promising advances have been made during the past decade. However, further research and development are mandatory to overcome the existing limitations and to maximize the clinical benefits.

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Clinical label-free biochemical and metabolic fluorescence lifetime endoscopic imaging of precancerous and cancerous oral lesions

Cited by 31 publications

References 31 publications

Blind deconvolution estimation by multi-exponential models and alternated least squares approximations: Free-form and sparse approach

Blind deconvolution estimation by multi-exponential models and alternated least squares approximations: Free-form and sparse approach

Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-Resistance by Feature Extraction of Gray Level Co-Occurrence Matrix Using Optical Images

Recent Advances and the Potential for Clinical Use of Autofluorescence Detection of Extra-Ophthalmic Tissues

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