Autofluorescence bronchoscopy: Clinical experience with an optimized system in head and neck cancer patients

Gabrecht, Tanja; Radu, Alexandre; Zellweger, Matthieu; Lovisa, Blaise; Goujon, Didier; Grosjean, Pierre; Bergh, Hubert van den; Monnier, Philippe; Wagnières, Georges

doi:10.1016/j.mla.2007.09.003

Cited by 3 publications

(5 citation statements)

References 37 publications

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“…There were several reports on the autofluorescence in vivo using the endoscopic equipment [22][23][24]. Typically, the autofluorescence spectrum features exhibited little difference in the previous studies, and they differentiated malignant lesions from healthy tissues by the autofluorescence intensity change [22,24].…”

Section: Discussionmentioning

confidence: 99%

“…There have been some reports using spectroscopy with a combination of endoscopic equipment [20][21][22][23][24][25]. Bard et al used white-light reflectance spectroscopy during bronchoscopy examination to differentiate normal bronchus mucosa from malignant lesions [15,20].…”

Section: Introductionmentioning

confidence: 99%

“…Most of bronchoscopes measured the decrease of autofluorescence intensity within the green spectral region for early cancerous lesions as compared to healthy tissues under blue-violet light (around 405 nm) excitation [22,24]. According to Gabrecht et al [23,24], the additional detection of backscattered red light significantly improved the autofluorescence image quality. This phenomenon could be due to the increased vasculature around cancerous lesions, and partially due to changes in the fluorescent properties of the tissues.…”

Section: Introductionmentioning

confidence: 99%

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Autofluorescence Imaging and Spectroscopy of Human Lung Cancer

et al. 2016

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Lung cancer is one of the most common cancers, with high mortality rate worldwide. Autofluorescence imaging and spectroscopy is a non-invasive, label-free, real-time technique for cancer detection. In this study, lung tissue sections excised from patients were detected by laser scan confocal microscopy and spectroscopy. The autofluorescence images demonstrated the cellular morphology and tissue structure, as well as the pathology of stained images. Based on the spectra study, it was found that the majority of the patients showed discriminating fluorescence in tumor tissues from normal tissues. Therefore, autofluorescence imaging and spectroscopy may be a potential method for aiding the diagnosis of lung cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Autofluorescence Imaging and Spectroscopy of Human Lung Cancer

et al. 2016

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“…Goujon et al [18] and Qu et al [19] described the use of follow-up, off-line analyses of spectral images in AFB to reduce the FP results. Other authors reported numerical manipulations of the red and green emissions, combined with different methods for thresholding the images into true positive and FP groups [18] and [20]. They reported a reliability of 79 % using these methods.…”

Section: In Addition To Its Utilization Of Changes In Fluorescence Inmentioning

confidence: 99%

Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy

Finkst

Tasič

Terčelj

et al. 2017

SV-JME

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This paper presents a novel approach to the classification of bronchial tissue as either malignant or precancerous based on autofluorescence bronchoscopy (AFB) images. The study consisted of 44 images, of which 22 were confirmed as malignant and 22 as nonmalignant precancerous cases. Our approach starts with the detection of a region of interest (ROI). This is followed by an analysis of semi-normal intensity distributions in gray-scale images of red and green components of the previously identified ROI. Based on the results of this analysis, features are computed, which are then used to build an image-classification model. This model classifies the tissue images into malignant/ nonmalignant classes. We utilized several classification algorithms, i.e., naive Bayes, K-nearest-neighbor (K-NN), and support vector machine (SVM) with dot kernel. The criteria used when testing their performance were accuracy, sensitivity, specificity, and the area under the curve. Wilcoxon's signed-rank test was used to confirm the accuracy of the classification method. The proposed method was compared to a similar approach reported by Buountris et al., who analyzed the texture features in a gray-level co-occurrence matrix (GLCM). Using the bestperforming classification algorithm (SVM with dot kernel), the accuracy of the proposed approach (95.8 %) was better than that reported by Bountris et al. (92.1 %).

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“…This application has proven to be highly sensitive to early, noninvasive preneoplastic and microinvasive neoplastic lesions that may remain occult with conventional white-light bronchoscopy. [13][14][15][16][17] Commercially available AFB systems mostly utilize blue-violet light for the AF excitation and detect the AF intensity contrast in the green part of the spectrum. 12,16,[18][19][20][21][22] Optimal contrast can be achieved with excitation wavelengths around 405 nm ͑Ref.…”

Section: Introductionmentioning

confidence: 99%

In vivo time-resolved spectroscopy of the human bronchial early cancer autofluorescence

et al. 2009

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Time-resolved measurements of tissue autofluorescence (AF) excited at 405 nm were carried out with an optical-fiber-based spectrometer in the bronchi of 11 patients. The objectives consisted of assessing the lifetime as a new tumor/normal (T/N) tissue contrast parameter and trying to explain the origin of the contrasts observed when using AF-based cancer detection imaging systems. No significant change in the AF lifetimes was found. AF bronchoscopy performed in parallel with an imaging device revealed both intensity and spectral contrasts. Our results suggest that the spectral contrast might be due to an enhanced blood concentration just below the epithelial layers of the lesion. The intensity contrast probably results from the thickening of the epithelium in the lesions. The absence of T/N lifetime contrast indicates that the quenching is not at the origin of the fluorescence intensity and spectral contrasts. These lifetimes (6.9 ns, 2.0 ns, and 0.2 ns) were consistent for all the examined sites. The fact that these lifetimes are the same for different emission domains ranging between 430 and 680 nm indicates that there is probably only one dominant fluorophore involved. The measured lifetimes suggest that this fluorophore is elastin.

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Autofluorescence bronchoscopy: Clinical experience with an optimized system in head and neck cancer patients

Cited by 3 publications

References 37 publications

Autofluorescence Imaging and Spectroscopy of Human Lung Cancer

Autofluorescence Imaging and Spectroscopy of Human Lung Cancer

Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy

In vivo time-resolved spectroscopy of the human bronchial early cancer autofluorescence

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