Use of autofluorescence and fluorescent probes as a potential diagnostic tool for oral cancer: A systematic review

Lima, Igor Felipe Pereira; Brand, Luiza Meurer; Figueiredo, José Antônio Poli de; Steier, Liviu; Lamers, Marcelo Lazzaron

doi:10.1016/j.pdpdt.2020.102073

Cited by 27 publications

(18 citation statements)

References 75 publications

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“…However, in answer to this third question, it must be taken into account that while fluorescence may be an adjunct or complement to oral examination in the diagnosis of oral precancer and cancer [73], it cannot be postulated as a substitute for biopsy [74]. This affirmation was ratified in the last Cochrane review, in which it was stated that none of the complementary tests, such as vital staining, oral cytology, light-based detection, and oral spectroscopy, replace biopsy for the diagnosis of oral cancer [75].…”

Section: Discussionmentioning

confidence: 99%

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

García-Pola

Pons-Fuster

Suárez-Fernández

et al. 2021

Cancers

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The early diagnosis of cancer can facilitate subsequent clinical patient management. Artificial intelligence (AI) has been found to be promising for improving the diagnostic process. The aim of the present study is to increase the evidence on the application of AI to the early diagnosis of oral cancer through a scoping review. A search was performed in the PubMed, Web of Science, Embase and Google Scholar databases during the period from January 2000 to December 2020, referring to the early non-invasive diagnosis of oral cancer based on AI applied to screening. Only accessible full-text articles were considered. Thirty-six studies were included on the early detection of oral cancer based on images (photographs (optical imaging and enhancement technology) and cytology) with the application of AI models. These studies were characterized by their heterogeneous nature. Each publication involved a different algorithm with potential training data bias and few comparative data for AI interpretation. Artificial intelligence may play an important role in precisely predicting the development of oral cancer, though several methodological issues need to be addressed in parallel to the advances in AI techniques, in order to allow large-scale transfer of the latter to population-based detection protocols.

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Section: Discussionmentioning

confidence: 99%

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

García-Pola

Pons-Fuster

Suárez-Fernández

et al. 2021

Cancers

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“…Because FI depends on various mucosal conditions, it has been reported that FV systems have high sensitivity but low specificity [ 8 ]. This is because FV devices are not capable of discriminating between malignant and benign lesions and inflammation [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, this method is unreliable because it does not access deep cells [ 7 ]. It has been pointed out that biopsy, considered the best method for diagnosing oral cancer, is invasive and sometimes results in tumour cells being seeded into the surrounding tissues [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Oral Mucosal Lesions Using the IllumiScan® Fluorescence Visualisation Device: Distinguishing Squamous Cell Carcinoma

Taguchi

Toratani

Matsui

et al. 2022

IJERPH

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We evaluated whether fluorescence intensity (FI) and its coefficient of variation (CV) can be used to diagnose squamous cell carcinoma (SCC) through IllumiScan®, an oral mucosa fluorescence visualisation (FV) device. Overall, 190 patients with oral mucosal lesions (OMLs; SCC, 59; non-SCC OMLs, 131) and 49 patients with normal oral mucosa (NOM) were enrolled between January 2019 and March 2021. The FI of the images was analysed using image analysis software. After establishing regions of interest for SCC, non-SCC, and NOM, the average FI, standard deviation (SD), and CV were compared. There was a significant difference in the average FI for all pairs of comparisons. The SD was not significantly different between the SCC and NOM groups (p = 0.07). The CV differed significantly for NOM (p < 0.001) and non-SCC groups (p < 0.001) relative to the SCC group but was not different between NOM and non-SCC groups (p = 0.15). Univariate analysis of SCC and non-SCC groups showed significant differences for all factors, except age. However, multivariate analysis showed a significant intergroup difference only in the CV (p = 0.038). Therefore, analysing the CV in FV images of OML may be useful for the diagnosis of oral cancer.

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“…Several handheld assistant tools, such as VELscope, Identafi, and EVINCE, have been developed to enhance the visualization of oral lesions based on observing the loss of green or blue autofluorescence in abnormal tissue [ 18 , 19 , 20 ]. In most studies in which these tools have been successfully used in clinical trials, examiners or surgeons identified the oral lesion using the tool or the image captured with the tool [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ]. In this study, we developed a portable handheld LED-induced autofluorescence multispectral imager that might be more suitable for quick screening of oral cancer compared to optical spectroscopy or HIS.…”

Section: Discussionmentioning

confidence: 99%

“…By observing the fluorescence loss, the development and progression of oral neoplasia can be artificially identified and scored using these devices. These devices have been used in clinician examinations and trials, showing their ability and efficacy for the screening of oral cancer [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. Recently, Jeng et al [ 32 ] developed a principle component analysis based method that combined a VELscope and Raman spectroscopy to improve the detection of oral cancer.…”

Section: Introductionmentioning

confidence: 99%

Band-Selection of a Portal LED-Induced Autofluorescence Multispectral Imager to Improve Oral Cancer Detection

Yan

Cheng

Jan

et al. 2021

Sensors

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This aim of this study was to find effective spectral bands for the early detection of oral cancer. The spectral images in different bands were acquired using a self-made portable light-emitting diode (LED)-induced autofluorescence multispectral imager equipped with 365 and 405 nm excitation LEDs, emission filters with center wavelengths of 470, 505, 525, 532, 550, 595, 632, 635, and 695 nm, and a color image sensor. The spectral images of 218 healthy points in 62 healthy participants and 218 tumor points in 62 patients were collected in the ex vivo trials at China Medical University Hospital. These ex vivo trials were similar to in vivo because the spectral images of anatomical specimens were immediately acquired after the on-site tumor resection. The spectral images associated with red, blue, and green filters correlated with and without nine emission filters were quantized by four computing method, including summated intensity, the highest number of the intensity level, entropy, and fractional dimension. The combination of four computing methods, two excitation light sources with two intensities, and 30 spectral bands in three experiments formed 264 classifiers. The quantized data in each classifier was divided into two groups: one was the training group optimizing the threshold of the quantized data, and the other was validating group tested under this optimized threshold. The sensitivity, specificity, and accuracy of each classifier were derived from these tests. To identify the influential spectral bands based on the area under the region and the testing results, a single-layer network learning process was used. This was compared to conventional rules-based approaches to show its superior and faster performance. Consequently, four emission filters with the center wavelengths of 470, 505, 532, and 550 nm were selected by an AI-based method and verified using a rule-based approach. The sensitivities of six classifiers using these emission filters were more significant than 90%. The average sensitivity of these was about 96.15%, the average specificity was approximately 69.55%, and the average accuracy was about 82.85%.

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Use of autofluorescence and fluorescent probes as a potential diagnostic tool for oral cancer: A systematic review

Cited by 27 publications

References 75 publications

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

Evaluation of Oral Mucosal Lesions Using the IllumiScan® Fluorescence Visualisation Device: Distinguishing Squamous Cell Carcinoma

Band-Selection of a Portal LED-Induced Autofluorescence Multispectral Imager to Improve Oral Cancer Detection

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