Back to basics : Hematoxylin and eosin staining is the principal tool for histopathological risk assessment of oral cancer

Non peer reviewe

Insight into Classification and Risk Stratification of Head and Neck Squamous Cell Carcinoma in Era of Emerging Biomarkers with Focus on Histopathologic Parameters

Tumor-node-metastasis (TNM) staging system is the cornerstone for treatment planning of head and neck squamous cell carcinoma (HNSCC). Many prognostic biomarkers have been introduced as modifiers to further improve the TNM classification of HNSCC. Here, we provide an overview on the use of the recent prognostic biomarkers, with a focus on histopathologic parameters, in improving the risk stratification of HNSCC and their application in the next generation of HNSCC staging systems

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

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.© 2022 Alabi, Almangush, Elmusrati and Mäkitie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.fi=vertaisarvioitu|en=peerReviewed

Risk stratification in oral squamous cell carcinoma using staging of the eighth American Joint Committee on Cancer : Systematic review and meta-analysis

The eighth edition of the American Joint Committee on Cancer (AJCC8) staging manual has major changes in oral squamous cell carcinoma (OSCC). We searched PubMed, OvidMedline, Scopus, and Web of Science for studies that examined the performance of AJCC8 in OSCC. A total of 40 808 patients were included in the studies of our meta-analysis. A hazard ratio (HR) of 1.87 (95%CI 1.78-1.96) was seen for stage II, 2.65 (95%CI 2.51-2.80) for stage III, 3.46 (95%CI 3.31-3.61) for stage IVa, and 7.09 (95%CI 4.85-10.36) for stage IVb. A similar gradual increase in risk was noted for the N classification. For the T classification, however, there was a less clear variation in risk between T3 and T4. AJCC8 provides a good risk stratification for OSCC. Future research should examine the proposals introduced in the published studies to further improve the performance of AJCC8.Peer reviewe

Does Evaluation of Tumour Budding in Diagnostic Biopsies have a Clinical Relevance? : A Systematic Review

Tumour budding has emerged as a promising prognostic marker in many cancers. We systematically reviewed all studies that evaluated tumour budding in diagnostic biopsies. We conducted a systematic review of PubMed, MEDLINE, Scopus, Web of Science and Cochrane library for all articles that have assessed tumour budding in diagnostic (i.e. pretreatment or pre-operative) biopsies of any tumour type. Two independent researchers screened the retrieved studies, removed duplicates, excluded irrelevant studies and extracted data from the eligible studies. A total of 13 reports comprising 11 cohorts were found to have studied tumour budding in diagnostic biopsies. All these reports showed that evaluation of tumour budding in diagnostic biopsies was easily applicable. A strong association was observed between tumour budding score in diagnostic biopsies and corresponding surgical samples. Evaluation of tumour budding in diagnostic biopsies had a significant prognostic value for lymph node metastasis and patient survival. In all studies, tumour budding was a valuable marker of tumour aggressiveness and can be evaluated in technically satisfactory diagnostic biopsies. Thus, the assessment of tumour budding seems to identify the behaviour of cancer, and therefore to facilitate treatment planning.Peer reviewe

Characteristics of Laryngeal Osteosarcoma : A Critical Review

Laryngeal sarcomas constitute an extremely rare entity among head and neck malignancies. Furthermore, most of them are chondrosarcomas, and the osteogenic form remains a true rarity. In general, there is a lack of information on the characteristics of laryngeal osteosarcoma. Thus, we sought to critically review the existing world literature on laryngeal osteosarcoma in order to develop a more accurate clinicopathological profile of this malignancy. Laryngeal osteosarcoma has a predilection for elderly male patients, as 87% were male in the present series and the mean age was 62 years (range 32-80), and without a direct association with tobacco exposure. Osteosarcoma of the larynx is typically a highly malignant neoplasm that metastasizes early, has a propensity for hematogenous spread and also has a marked tendency to recur. Twelve (41%) out of the 29 cases in the present review with follow-up data had metastatic disease. The aspects that distinguish osteosarcoma from its differential diagnostic alternatives are discussed in this review.Peer reviewe

Tumour-infiltrating lymphocytes in oropharyngeal cancer : a validation study according to the criteria of the International Immuno-Oncology Biomarker Working Group

Background The evaluation of immune response can aid in prediction of cancer behaviour. Here, we assessed the prognostic significance of tumour-infiltrating lymphocytes (TILs) in oropharyngeal squamous cell carcinoma (OPSCC). Methods A total of 182 patients treated for OPSCC were included in this study. Assessment of TILs was conducted on tumour sections stained with standard haematoxylin and eosin (HE) staining. We used the scoring criteria proposed by the International Immuno-Oncology Biomarker Working Group. Results The multivariable analysis showed that TILs associated with disease-specific survival with a hazard ratio (HR) of 2.13 (95% CI 1.14-3.96; P = 0.017). Similarly, TILs associated significantly with overall survival with HR of 1.87 (95% CI 1.11-3.13; P = 0.018). In a sub-analysis of HPV-positive and HPV-negative cases separately, TILs showed a significant prognostic value in both groups (P < 0.05). Conclusion The evaluation of TILs as proposed by the International Immuno-Oncology Biomarker Working Group is a simple and promising method in prediction of survival of OPSCC. It is easily applicable and after further validation can be implemented in the routine pathological report as a basic immune parameter.Peer reviewe

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

Peer reviewe

Biomarkers for Immunotherapy of Oral Squamous Cell Carcinoma: Current Status and Challenges

Oral squamous cell carcinoma (OSCC) forms a major health problem in many countries. For several decades the management of OSCC consisted of surgery with or without radiotherapy or chemoradiotherapy. Aiming to increase survival rate, recent research has underlined the significance of harnessing the immune response in treatment of many cancers. The promising finding of checkpoint inhibitors as a weapon for targeting metastatic melanoma was a key event in the development of immunotherapy. Furthermore, clinical trials have recently proven inhibitor of PD-1 for treatment of recurrent/metastatic head and neck cancer. However, some challenges (including patient selection) are presented in the era of immunotherapy. In this mini-review we discuss the emergence of immunotherapy for OSCC and the recently introduced biomarkers of this therapeutic strategy. Immune biomarkers and their prognostic perspectives for selecting patients who may benefit from immunotherapy are addressed. In addition, possible use of such biomarkers to assess the response to this new treatment modality of OSCC will also be discussed

Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer

Background: The prediction of overall survival in tongue cancer is important for planning of personalized care and patient counselling. Objectives: This study compares the performance of a nomogram with a machine learning model to predict overall survival in tongue cancer. The nomogram and machine learning model were built using a large data set from the Surveillance, Epidemiology, and End Results (SEER) program database. The comparison is necessary to provide the clinicians with a comprehensive, practical, and most accurate assistive system to predict overall survival of this patient population. Methods: The data set used included the records of 7596 tongue cancer patients. The considered machine learning algorithms were logistic regression, support vector machine, Bayes point machine, boosted decision tree, decision forest, and decision jungle. These algorithms were mainly evaluated in terms of the areas under the receiver operating characteristic (ROC) curve (AUC) and accuracy values. The performance of the algorithm that produced the best result was compared with a nomogram to predict overall survival in tongue cancer patients. Results: The boosted decision-tree algorithm outperformed other algorithms. When compared with a nomogram using external validation data, the boosted decision tree produced an accuracy of 88.7% while the nomogram showed an accuracy of 60.4%. In addition, it was found that age of patient, T stage, radiotherapy, and the surgical resection were the most prominent features with significant influence on the machine learning model's performance to predict overall survival. Conclusion: The machine learning model provides more personalized and reliable prognostic information of tongue cancer than the nomogram. However, the level of transparency offered by the nomogram in estimating patients' outcomes seems more confident and strengthened the principle of shared decision making between the patient and clinician. Therefore, a combination of a nomogram - machine learning (NomoML) predictive model may help to improve care, provides information to patients, and facilitates the clinicians in making tongue cancer management-related decisions.Peer reviewe