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

2022 ◽  
Vol 2 ◽  
Author(s):  
Rasheed Omobolaji Alabi ◽  
Alhadi Almangush ◽  
Mohammed Elmusrati ◽  
Antti A. Mäkitie
Keyword(s):  
Machine Learning ◽  
Squamous Cell Carcinoma ◽  
Deep Learning ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Precision Medicine ◽  
Treatment Planning ◽  
Squamous Cell ◽  
Early Stage ◽  
Learning Technology

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.

scholarly journals The REASON score: an epigenetic and clinicopathologic score to predict risk of poor survival in patients with early stage oral squamous cell carcinoma

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Chi T. Viet ◽  
Gary Yu ◽  
Kesava Asam ◽  
Carissa M. Thomas ◽  
Angela J. Yoon ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Early Stage ◽  
Genome Wide Association Studies ◽  
Gene Methylation ◽  
Poor Survival ◽  
Risk Of Death

Abstract Background Oral squamous cell carcinoma (OSCC) is a capricious cancer with poor survival rates, even for early-stage patients. There is a pressing need to develop more precise risk assessment methods to appropriately tailor clinical treatment. Genome-wide association studies have not produced a viable biomarker. However, these studies are limited by using heterogeneous cohorts, not focusing on methylation although OSCC is a heavily epigenetically-regulated cancer, and not combining molecular data with clinicopathologic data for risk prediction. In this study we focused on early-stage (I/II) OSCC and created a risk score called the REASON score, which combines clinicopathologic characteristics with a 12-gene methylation signature, to predict the risk of 5-year mortality. Methods We combined data from an internal cohort (n = 515) and The Cancer Genome Atlas (TCGA) cohort (n = 58). We collected clinicopathologic data from both cohorts to derive the non-molecular portion of the REASON score. We then analyzed the TCGA cohort DNA methylation data to derive the molecular portion of the risk score. Results 5-year disease specific survival was 63% for the internal cohort and 86% for the TCGA cohort. The clinicopathologic features with the highest predictive ability among the two the cohorts were age, race, sex, tobacco use, alcohol use, histologic grade, stage, perineural invasion (PNI), lymphovascular invasion (LVI), and margin status. This panel of 10 non-molecular features predicted 5-year mortality risk with a concordance (c)-index = 0.67. Our molecular panel consisted of a 12-gene methylation signature (i.e., HORMAD2, MYLK, GPR133, SOX8, TRPA1, ABCA2, HGFAC, MCPH1, WDR86, CACNA1H, RNF216, CCNJL), which had the most significant differential methylation between patients who survived vs. died by 5 years. All 12 genes have already been linked to survival in other cancers. Of the genes, only SOX8 was previously associated with OSCC; our study was the first to link the remaining 11 genes to OSCC survival. The combined molecular and non-molecular panel formed the REASON score, which predicted risk of death with a c-index = 0.915. Conclusions The REASON score is a promising biomarker to predict risk of mortality in early-stage OSCC patients. Validation of the REASON score in a larger independent cohort is warranted.

scholarly journals Prediction of oral squamous cell carcinoma based on machine learning of breath samples: a prospective controlled study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sophia Mentel ◽  
Kathleen Gallo ◽  
Oliver Wagendorf ◽  
Robert Preissner ◽  
Susanne Nahles ◽  
...  
Keyword(s):  
Machine Learning ◽  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Breath Analysis ◽  
Controlled Study ◽  
Control Group ◽  
Full Potential ◽  
Average Accuracy

Abstract Background The aim of this study was to evaluate the possibility of breath testing as a method of cancer detection in patients with oral squamous cell carcinoma (OSCC). Methods Breath analysis was performed in 35 OSCC patients prior to surgery. In 22 patients, a subsequent breath test was carried out after surgery. Fifty healthy subjects were evaluated in the control group. Breath sampling was standardized regarding location and patient preparation. All analyses were performed using gas chromatography coupled with ion mobility spectrometry and machine learning. Results Differences in imaging as well as in pre- and postoperative findings of OSCC patients and healthy participants were observed. Specific volatile organic compound signatures were found in OSCC patients. Samples from patients and healthy individuals could be correctly assigned using machine learning with an average accuracy of 86–90%. Conclusions Breath analysis to determine OSCC in patients is promising, and the identification of patterns and the implementation of machine learning require further assessment and optimization. Larger prospective studies are required to use the full potential of machine learning to identify disease signatures in breath volatiles.

scholarly journals Robust expression of LINE-1 retrotransposon encoded proteins in oral squamous cell carcinoma

2020 ◽  
Author(s):  
Koel Mukherjee ◽  
Debpali Sur ◽  
Abhijeet Singh ◽  
Sandhya Rai ◽  
Neeladrisingha Das ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Early Stage ◽  
Antibody Detection ◽  
Normal Brain ◽  
Unknown Mechanism ◽  
Stage Of Development ◽  
Amino Acid Stretch

AbstractRetrotransposons are sequences which transpose within genomes using RNA as an intermediate. Long INterpersed Element-1 (LINE1 or L1) is the only active retrotransposon occupying around 17% of the human genome with an estimated 500,000 copies. An active L1 encodes two proteins (L1ORF1p and L1ORF2p); both of which are critical in the process of retrotransposition. In-order to propagate to the nextgeneration, L1s remain active in germ tissues and at an early stage of development. Surprisingly, by some unknown mechanism, L1 also shows activity in certain parts of the normal brain and many cancers. L1 activity is generally determined by assaying L1ORF1p because of its high expression and availability of the antibody. However, due to its lowerexpression and the unavailability of a robust antibody, detection of L1ORF2p has been limited. L1ORF2p is the crucial protein in the process of retrotransposition as it provides endonuclease and reverse transcriptase (RT) activity. Here, we report a novel human L1ORF2p antibody generated using an 80-amino-acid stretch from the RT domain, which is highly conserved among different species. The antibody detects significant L1ORF2p expression in murine germ tissues and human oral squamous cell carcinoma (OSCC) samples. This particular cancer is prevalent in India due to excessive use of tobacco. Here, using our in-house antibodies against L1 proteins, we show that more than fifty percent of samples are positive for L1 proteins. Overall, we reported a novel L1ORF2p antibody that detects L1 activity in germ tissues and OSCC

Tumor depth of invasion and prognosis of early‐stage oral squamous cell carcinoma: A meta‐analysis

Oral Diseases ◽  
2019 ◽  
Vol 26 (7) ◽  
pp. 1357-1365 ◽  
Author(s):  
Patrícia Carlos Caldeira ◽  
Andrea María López Soto ◽  
Maria Cássia Ferreira Aguiar ◽  
Carolina Castro Martins
Keyword(s):  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Early Stage ◽  
Meta Analysis ◽  
Depth Of Invasion ◽  
Tumor Depth

Elective neck dissection in early-stage oral squamous cell carcinoma—does it influence recurrence and survival?

Head & Neck ◽  
2006 ◽  
Vol 29 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Ana Capote ◽  
Veronica Escorial ◽  
Mario F. Muñoz-Guerra ◽  
Francisco J. Rodríguez-Campo ◽  
Carlos Gamallo ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Neck Dissection ◽  
Squamous Cell ◽  
Early Stage ◽  
Elective Neck Dissection

Association between central and peripheral circulating tumor cell (CTC) clusters in oral squamous cell carcinoma (OSCC): A prospective, observational study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15548-e15548
Author(s):  
Ritvi K Bagadia ◽  
Vishal Uchila Shishir Rao ◽  
Ajay Balakrishnan ◽  
Abhijith George ◽  
Prashant Kumar
Keyword(s):  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Observational Study ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Early Stage ◽  
Clinical Stage ◽  
Survival Rates ◽  
Tnm Staging ◽  
Dapi Staining

e15548 Background: Around 90% of cancer-related mortalities are caused by tumor metastasis. CTC clusters, which constitute an intermediate stage of metastasis, have not been studied extensively in head & neck cancers. The mortality rate of oral cancers remains alarmingly high, despite multimodality treatment. The aim of the study is to identify the presence of CTC clusters in patients with Oral Squamous Cell Carcinoma (OSCC) and to correlate their presence with clinical and pathological factors. Methods: Fifty patients diagnosed with histologically proven OSCC, treatment naïve, and underwent surgery at HCG Cancer Centre, Bangalore, were consented and enrolled in the study. An IRB-approved protocol allowed for the collection of 10 ml of blood from central (jugular) and peripheral veins intra-operatively, prior to tumor removal. The culturing of CTC clusters was done using ellipsoidal microwell plates maintained at hypoxic conditions, at the Institute of Bioinformatics, Bangalore. After fourteen days of culturing, the cells were fixed and stained for DAPI, Pan-CK and CD45. The CTC clusters were classified into Loose, Tight and very Tight based on the median gray values obtained from DAPI staining on ImageJ software. Clinical data was collected from patient records and subjected to analysis using Descriptive statistics. Results: From the 50 patients included in the study, 22 (44%) patients exhibited tight clusters in central blood, while only 13 (26%) patients exhibited tight clusters in peripheral blood. A higher clinical stage was observed in a greater percentage of patients with tight clusters in central blood (early: 45.5% versus late: 54.5%), but the same findings could not be inferred with pathological staging (early stage: 59.1% versus late stage: 40.1%). No significant correlation with adverse pathological features was noted. Conclusions: This observational study provides an insight into the varying biological behaviours of similarly grouped cancers, which is based on the standard TNM staging. The study forms the basis for the hypothesis of tight clusters in the central and peripheral circulation, correlating with loco-regional and distant metastasis respectively, thus leading to poorer disease-free and overall survival rates.

scholarly journals Adjunctive Therapy in Oral Cancer

2021 ◽  
pp. 1903-1913
Author(s):  
Amit Dhawan
Keyword(s):  
Squamous Cell Carcinoma ◽  
Oral Cancer ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Cancer Patients ◽  
Squamous Cell ◽  
Adjunctive Therapy ◽  
Early Stage ◽  
Advanced Stage ◽  
Oral Cancer Patients

AbstractOral squamous cell carcinoma is the third most common cancer in Indian subcontinent affecting people with lower socioeconomic status. Due to inadequate screening facilities and lack of awareness among individuals most of the oral cancer cases are detected at an advanced stage. As early stage oral squamous cell carcinoma patients can be treated with single modality treatment (surgery or radical radiotherapy), multimodality regimen (surgery followed by concurrent chemoradiation) is adopted for high risk advanced stage cancers with multiple adverse features like extra nodal extension, lymphovascular invasion and perineural spread. The chapter outlines the principles of adjunctive therapy in oral cancer patients with special reference to different techniques, indications of radiotherapy and role of chemotherapeutic regimes in improving the overall survival of advanced stage oral cancer patients.

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