Prediction of the risk of cancer and the grade of dysplasia in leukoplakia lesions using deep learning

Ferrer-Sánchez, Antonio; Bagán, José; Vila‐Francés, Joan; Magdalena‐Benedito, Rafael; Bagán-Debón, Leticia

doi:10.1016/j.oraloncology.2022.105967

Cited by 15 publications

(3 citation statements)

References 31 publications

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“…Clinical and histopathological analysis by the exclusion of other disorders is the conventional diagnosis of oral leukoplakia (Warnakulasuriya et al, 2007 ; van der Waal, 2009 ). Machine learning was used in clinical images to predict the high risk of dysplasia and evolution to cancer (Ferrer-Sánchez et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

OralImmunoAnalyser: a software tool for immunohistochemical assessment of oral leukoplakia using image segmentation and classification models

Al-Tarawneh,

Pena-Cristóbal,

Cernadas

et al. 2024

Front. Artif. Intell.

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Oral cancer ranks sixteenth amongst types of cancer by number of deaths. Many oral cancers are developed from potentially malignant disorders such as oral leukoplakia, whose most frequent predictor is the presence of epithelial dysplasia. Immunohistochemical staining using cell proliferation biomarkers such as ki67 is a complementary technique to improve the diagnosis and prognosis of oral leukoplakia. The cell counting of these images was traditionally done manually, which is time-consuming and not very reproducible due to intra- and inter-observer variability. The software presently available is not suitable for this task. This article presents the OralImmunoAnalyser software (registered by the University of Santiago de Compostela–USC), which combines automatic image processing with a friendly graphical user interface that allows investigators to oversee and easily correct the automatically recognized cells before quantification. OralImmunoAnalyser is able to count the number of cells in three staining levels and each epithelial layer. Operating in the daily work of the Odontology Faculty, it registered a sensitivity of 64.4% and specificity of 93% for automatic cell detection, with an accuracy of 79.8% for cell classification. Although expert supervision is needed before quantification, OIA reduces the expert analysis time by 56.5% compared to manual counting, avoiding mistakes because the user can check the cells counted. Hence, the SUS questionnaire reported a mean score of 80.9, which means that the system was perceived from good to excellent. OralImmunoAnalyser is accurate, trustworthy, and easy to use in daily practice in biomedical labs. The software, for Windows and Linux, with the images used in this study, can be downloaded from https://citius.usc.es/transferencia/software/oralimmunoanalyser for research purposes upon acceptance.

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Section: Introductionmentioning

confidence: 99%

OralImmunoAnalyser: a software tool for immunohistochemical assessment of oral leukoplakia using image segmentation and classification models

Al-Tarawneh,

Pena-Cristóbal,

Cernadas

et al. 2024

Front. Artif. Intell.

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“…6 In the course of routine diagnosis, digital whole slide image (DWSI) of hematoxylin and eosin (H&E) stained tumor tissue slice is manually magnified by a trained histopathologist through a computer. 7,8 The diagnosis was made by analyzing the morphological variation in the cancerous area, quantifying the density of the malignant area, and observing the spatial arrangement of the tumor microenvironment. [9][10][11] Nevertheless, the scarcity of pathologists and high clinical experience requirements exacerbate the conflict between clinical demands and actual production.…”

Section: Introductionmentioning

confidence: 99%

“…After surgery, it is a critical step to discriminate the extent of tumor development (benign, malignant, and leukoplakia that a state between benign and malignant) accurately, depending on the tumor microenvironment 6 . In the course of routine diagnosis, digital whole slide image (DWSI) of hematoxylin and eosin (H&E) stained tumor tissue slice is manually magnified by a trained histopathologist through a computer 7,8 . The diagnosis was made by analyzing the morphological variation in the cancerous area, quantifying the density of the malignant area, and observing the spatial arrangement of the tumor microenvironment 9–11 .…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence driven digital whole slide image for intelligent recognition of different development stages of tongue tumor via a new deep learning framework

Peng

Hou

et al. 2023

Engineering Reports

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Accurate clinical diagnosis of the stage of tumor development is essential for formulating a treatment plan. However, the stage of tongue tumor development among malignant, benign and leukoplakia is easily misdiagnosed, resulting differing treatment approaches, taking patients in danger and preventing them from receiving the deserved care. This study aimed to establish an automatic recognition system for tongue tumors at different stages of development using artificial intelligence methods along with pathological tissue section images. By improving Swin Transformer framework, a new framework in deep learning, the tissue slice image is used to identify the lesion and non‐lesion areas by the patch‐based method, and then an output is reconstructed by self‐Assembly method that the lesion areas is marked with heat map. Subsequently, an automatic recognition system with a friendly page is designed for the stage of tongue tumor development (malignant, benign, and leukoplakia). The proposed model has a high recognition accuracy (98.45%). The prediction accuracy of each category of the system is higher than that of specialist doctors with 13 years of experience. The Swin Transformer framework was improved in this study to accurately and automatically identify the various stages of tongue tumor development.

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Machine Learning for Maximizing the Memristivity of Single and Coupled Quantum Memristors

Hernani‐Morales,

Alvarado,

Albarrán‐Arriagada

et al. 2024

Adv Quantum Tech

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Machine learning (ML) methods are proposed to characterize the memristive properties of single and coupled quantum memristors. It is shown that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. The results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.

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Prediction of the risk of cancer and the grade of dysplasia in leukoplakia lesions using deep learning

Cited by 15 publications

References 31 publications

OralImmunoAnalyser: a software tool for immunohistochemical assessment of oral leukoplakia using image segmentation and classification models

OralImmunoAnalyser: a software tool for immunohistochemical assessment of oral leukoplakia using image segmentation and classification models

Artificial intelligence driven digital whole slide image for intelligent recognition of different development stages of tongue tumor via a new deep learning framework

Machine Learning for Maximizing the Memristivity of Single and Coupled Quantum Memristors

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