Finding distinctions between oral cancer and periodontitis using saliva metabolites and machine learning

Abstract

Objective The aim of this research is the study of metabolic pathways related to oral cancer and periodontitis along with development of machine‐learning model for elucidation of these diseases based on saliva metabolites of patients. Methods Data mining, metabolomic pathways analysis, study of metabolite–gene networks related to these diseases. Machine‐learning and deep‐learning methods for development of the model for recognition of oral cancer versus periodontitis, using patients' saliva. Results The most accurate classifications between oral cancer and periodontitis were performed using neural networks, logistic regression and stochastic gradient descent confirmed by the separate 10‐fold cross‐validations. The best results were achieved by the deep‐learning neural network with the TensorFlow program. Accuracy of the resulting model was 79.54%. The other methods, which did not rely on deep learning, were able to achieve comparable, although slightly worse results with respect to accuracy. Conclusion Our results demonstrate a possibility to distinguish oral cancer from periodontal disease by analysis the saliva metabolites of a patient, using machine‐learning methods. These findings may be useful in the development of a non‐invasive method to aid care providers in determining between oral cancer and periodontitis quickly and effectively.

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

“…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.…”

“…Additionally, specificity and accuracy were also used to demonstrate the performance of the deep learning model for prognostication in OSCC [ 23 ]. Other studies used either accuracy, C-index (concordance index), F1-score, or Dice similarity coefficient (Dsc) mean value as the performance metrics for reporting the potential benefits of the deep learning model [ 2 , 18 , 18 , 26 , 28 , 29 , 31 – 33 , 39 – 41 , 44 , 45 , 49 ].…”

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

“…Additionally, these models have assisted in the prediction of lymph node metastasis [27][28][29]76]. Besides, they have been reported to perform well in differentiating between precancerous and cancerous lesions [64,[77][78][79][80][81]. These models have been integrated to offer an automated diagnosis of oral cancer [24,64,82].…”

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

“…Furthermore, recent studies have identified important molecular mechanisms/signaling pathways in cancer development and progression [71,, and several pathway analysis methods have been reported to elucidate the true nature of cancer and identify drug targets by using features extracted from large-scale data. The methodology is correct, and several results have been published that have contributed greatly to the development of the field of oncology [139][140][141][142][143][144][145][146][147][148][149][150]. However, it should be adequately recognized that there are limitations to the results obtained by a dry lab approach, and it is important to validate the results obtained by the dry lab approach using appropriate wet lab experiments (cell-level studies or animal-level studies using mice).…”