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

Abstract

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.

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

“…Of the 7 included systematic reviews [ 7 , 18 – 23 ], 4 were conducted in Asia (i.e., 2 each from China and India) [ 18 , 19 , 21 , 22 ] while 2 reviews was conducted in Europe (Finland) [ 7 , 23 ] and one review in the United States [ 20 ] ( Table 2 ). The quality appraisal for all the review papers considered in this study indicated that 42.9% showed high quality [ 7 , 21 , 22 ] while 57.1% showed medium quality [ 18 – 20 , 23 ]. Three of the reviews were conducted in the year 2020 [ 18 – 20 ] while four were conducted in the year 2021 [ 7 , 21 – 23 ].…”

“…Of the 7 included systematic reviews [7,[18][19][20][21][22][23], 4 were conducted in Asia (i.e., 2 each from China and India) [18,19,21,22] while 2 reviews was conducted in Europe (Finland) [7,23] and one review in the United States [20] (Table 2). The quality appraisal for all the review papers considered in this study indicated that 42.9% showed high quality [7,21,22] while 57.1% showed medium quality [18][19][20]23]. Three of the reviews were conducted in the year 2020 [18][19][20] while four were conducted in the year 2021 [7,[21][22][23].…”

“…Several data types such as pathologic and radiographic images have been used by deep learning in the quest to achieve precise diagnosis and prognosis [20,[22][23][24][25][26][27][28][29][30][31][32]. Other data types include gene expression data, spectra data, saliva metabolites, autofluorescence, cytology-image, computed tomography images, and clinicopathologic images that have been used in the deep learning analysis for improved diagnosis of oral cancer [7].…”

“…Late diagnosis has hampered the quest for precision medicine despite the recent improvement in the understanding of the molecular mechanisms of cancer. Therefore, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity [ 7 ]. Automated image analysis clearly has the potential to assist pathologists and clinicians in the early-stage detection of OSCC and in making informed decisions regarding cancer management.…”

“…We have decided to use this approach of a systematic review of systematic reviews because it offers the opportunity to summarize the evidence from more than one systematic review that have examined the application of deep learning for prognostication oral cancer outcomes. In our previous study, we examined the utilization of deep learning techniques for cancer prognostication [ 7 ]. In this present study, we aim to summarize the evidence relating to the qualitative (reliability of the studies that examined deep learning for cancer prognostication) and quantitative (performance metrics of the deep learning algorithms) parameters that are needed to provide insightful overview on how deep learning techniques have helped in the improved and effective management of oral cancer.…”