Analysis and classification of oral tongue squamous cell carcinoma based on Raman spectroscopy and convolutional neural networks

Xia, Jiabin; Zhu, Lianqing; Yu, Mingxin; Zhang, Tao; Zhu, Zhihui; Lou, Xiaoping; Sun, Greg; Dong, Mingli

doi:10.1080/09500340.2020.1742395

Cited by 19 publications

(14 citation statements)

References 32 publications

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“…The majority of these studies used a convolutional neural network (CNN) [ 2 , 15 – 22 , 24 – 26 , 28 , 31 – 36 , 38 – 41 , 43 – 45 , 48 , 49 ]. Several data types such as gene expression data [ 15 , 45 ], spectra data [ 20 , 21 , 29 , 34 , 37 , 44 , 48 ], and other image data types—anatomical [ 16 ], intraoral [ 17 ], histology [ 18 , 27 ], auto-fluorescence [ 19 , 22 ], cytology-image [ 23 ], neoplastic [ 40 ], clinical [ 28 , 36 , 38 ], oral lesions [ 42 ], computed tomography images [ 24 – 26 , 33 , 35 , 41 , 49 ], clinicopathologic [ 2 ], saliva metabolites [ 31 ], histopathological [ 30 , 32 , 43 ], and pathological [ 39 ] images have been used in the included studies.…”

Section: Resultsmentioning

confidence: 99%

“…The deep machine learning method has been reported to show promising results in the prediction and detection of OSCC [ 2 , 15 , 16 , 18 , 19 , 22 – 24 , 27 – 30 , 34 , 36 , 39 , 44 ] and lymph node metastasis [ 25 , 33 , 41 ]. Furthermore, the deep machine learning model have been reported to show significant prognostication of grading of the disease [ 32 , 43 ] and survival prediction of oral cancer patients [ 2 , 35 ].…”

Section: Resultsmentioning

confidence: 99%

“…The utilization of deep machine learning for prognostication include the distinction between potentially malignant disease and OSCC [ 15 , 17 , 26 , 39 ], differentiation between the oral tongue squamous cell carcinoma from non-tumorous tissue [ 20 , 21 , 34 ], prediction and detection of oral squamous cell carcinoma [ 16 , 18 , 22 – 24 , 51 ], diagnosis of lymph node metastasis [ 25 , 33 ], differentiation between OSCC and diseases such as periodontitis [ 31 ], ability to offer multi-class grading of OSCC [ 32 ], and prediction of survival in OSCC patients [ 2 ].…”

Section: Discussionmentioning

confidence: 99%

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Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

Alabi

Bello

Youssef

et al. 2021

Front. Oral. Health

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The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases—PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

Alabi

Bello

Youssef

et al. 2021

Front. Oral. Health

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“…For example, for precise diagnosis purposes, deep learning models have been used in the detection of oral cancer [24,25,[64][65][66][67][68][69][70][71][72][73][74][75]. Additionally, these models have assisted in the prediction of lymph node metastasis [27][28][29]76].…”

Section: Deep Learning For Oral Cancer: From Precise Diagnosis To Precision Medicinementioning

confidence: 99%

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Alabi¹,

Almangush²,

Elmusrati³

et al. 2022

Front. Oral. Health

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Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.

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“…Convolutional neural network (CNN) models have already been used for the interpretation of raw Raman spectra with respect to mineral species recognition, mixture composition analysis, or human tissue characterization. [ 37–43 ] The reported techniques are very much restricted to the specific applications. Our method is applicable to any Raman spectrum and to other spectroscopic techniques whose spectra contain Gaussian, Lorentzian, or Voigt profile peaks.…”

Section: Introductionmentioning

confidence: 99%

Refinement of spectra using a deep neural network: Fully automated removal of noise and background

Gebrekidan

Knipfer

Braeuer

2021

J Raman Spectroscopy

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We report the potential of U‐Net deep neural network for the efficient removal of noise and background from raw Raman spectra. The U‐Net method was first trained on simulated spectra and then tested with experimental spectra. The quality of the test results was quantified via different signal‐to‐noise ratios and the structural similarity index metric. The U‐Net recovered Raman spectra feature a high structural similarity index, even for raw spectra that were dominated by background. The U‐Net model does not rely on any human intervention.

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Analysis and classification of oral tongue squamous cell carcinoma based on Raman spectroscopy and convolutional neural networks

Cited by 19 publications

References 32 publications

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Refinement of spectra using a deep neural network: Fully automated removal of noise and background

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