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Abstract Background Many histopathologic prognostic markers, identified by routine hematoxylin and eosin (HE) staining, have been proposed for predicting the survival of patients with oral squamous cell carcinoma (OSCC). Subsequently, several meta-analyses have been conducted on these prognostic markers. We sought to analyze the accumulated evidence from these meta-analyses. Methods An electronic database search of PubMed, Scopus, Ovid Medline, Web of Science, and Cochrane Library was conducted to retrieve all meta-analysis articles published on histopathologic prognostic markers of OSCC. The risk of bias of the included studies was analyzed using the Risk of Bias in Systematic Reviews (ROBIS) tool. The synthesis of the results was conducted following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results There were 16 meta-analysis articles published on the histological prognostic markers of OSSC. The accumulated evidence from these meta-analyses highlighted the powerful prognostic value of depth of invasion, tumor thickness, perineural invasion, lymphovascular invasion, worst pattern of invasion, tumor budding, and tumor-stroma ratio. The highest odds ratio (OR) of a relationship between a histopathologic prognostic marker and outcome was for the depth of invasion (OR 10.16, 95% CI 5.05–20.46) and tumor thickness (OR 7.32, 95% CI 5.3–10.1) in predicting lymph node metastasis. Conclusion The published meta-analyses present robust evidence on the significance of emerging histopathologic markers, namely, worst pattern of invasion, tumor budding, and tumor-stroma ratio. It is time to consider such markers in daily pathology reporting and risk stratification of OSCC.

Abstract Background The clinical significance of single cell invasion and large nuclear diameter is not well documented in early-stage oral tongue squamous cell carcinoma (OTSCC). Methods We used hematoxylin and eosin-stained sections to evaluate the presence of single cell invasion and large nuclei in a multicenter cohort of 311 cases treated for early-stage OTSCC. Results Single cell invasion was associated in multivariable analysis with poor disease-specific survival (DSS) with a hazard ratio (HR) of 2.089 (95% CI 1.224–3.566, P = 0.007), as well as with disease-free survival (DFS) with a HR of 1.666 (95% CI 1.080–2.571, P = 0.021). Furthermore, large nuclei were associated with worse DSS (HR 2.070, 95% CI 1.216–3.523, P = 0.007) and with DFS in multivariable analysis (HR 1.645, 95% CI 1.067–2.538, P = 0.024). Conclusion Single cell invasion and large nuclei can be utilized for classifying early OTSCC into risk groups.

Abstract Nasopharyngeal cancer (NPC) has a unique histopathology compared with other head and neck cancers. Individual NPC patients may attain different outcomes. This study aims to build a prognostic system by combining a highly accurate machine learning model (ML) model with explainable artificial intelligence to stratify NPC patients into low and high chance of survival groups. Explainability is provided using Local Interpretable Model Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) techniques. A total of 1094 NPC patients were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database for model training and internal validation. We combined five different ML algorithms to form a uniquely stacked algorithm. The predictive performance of the stacked algorithm was compared with a state-of-the-art algorithm—extreme gradient boosting (XGBoost) to stratify the NPC patients into chance of survival groups. We validated our model with temporal validation (n = 547) and geographic external validation (Helsinki University Hospital NPC cohort, n = 60). The developed stacked predictive ML model showed an accuracy of 85.9% while the XGBoost had 84.5% after the training and testing phases. This demonstrated that both XGBoost and the stacked model showed comparable performance. External geographic validation of XGBoost model showed a c-index of 0.74, accuracy of 76.7%, and area under curve of 0.76. The SHAP technique revealed that age of the patient at diagnosis, T-stage, ethnicity, M-stage, marital status, and grade were among the prominent input variables in decreasing order of significance for the overall survival of NPC patients. LIME showed the degree of reliability of the prediction made by the model. In addition, both techniques showed how each feature contributed to the prediction made by the model. LIME and SHAP techniques provided personalized protective and risk factors for each NPC patient and unraveled some novel non-linear relationships between input features and survival chance. The examined ML approach showed the ability to predict the chance of overall survival of NPC patients. This is important for effective treatment planning care and informed clinical decisions. To enhance outcome results, including survival in NPC, ML may aid in planning individualized therapy for this patient population.

Abstract Background The clinical significance of tertiary lymphoid structures (TLSs) is not well-documented in early oral tongue squamous cell carcinoma (OTSCC). Methods A total of 310 cases of early (cT1-2N0) OTSCC were included in this multicenter study. Assessment of TLSs was conducted on hematoxylin and eosin-stained sections. TLSs were assessed both in the central part of the tumor and at the invasive front area. Results The presence of TLSs associated with improved survival of early OTSCC as presented by Kaplan–Meier survival analyses for disease-specific survival (P = 0.01) and overall survival (P = 0.006). In multivariable analyses, which included conventional prognostic factors, the absence of TLSs associated with worse disease-specific survival with a hazard ratio (HR) of 1.96 (95% CI 1.09–3.54; P = 0.025) and poor overall survival (HR 1.66, 95% CI 1.11–2.48; P = 0.014). Conclusion Histological evaluation of TLSs predicts survival in early OTSCC. TLSs showed superior prognostic power independent of routine WHO grading and TNM staging system.

Abstract Background The clinical significance of tumor-stroma ratio (TSR) has been examined in many tumors. Here we systematically reviewed all studies that evaluated TSR in head and neck cancer. Methods Four databases (Scopus, Medline, PubMed and Web of Science) were searched using the term tumo(u)r-stroma ratio. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) were followed. Results TSR was studied in nine studies of different subsites (including cohorts of nasopharyngeal, oral, laryngeal and pharyngeal carcinomas). In all studies, TSR was evaluated using hematoxylin and eosin staining. Classifying tumors based on TSR seems to allow for identification of high-risk cases. In oral cancer, specifically, our meta-analysis showed that TSR is significantly associated with both cancer-related mortality (HR 2.10, 95%CI 1.56–2.84) and disease-free survival (HR 1.84, 95%CI 1.38–2.46). Conclusions The assessment of TSR has a promising prognostic value and can be implemented with minimum efforts in routine head and neck pathology.

Objectives: Tumor mutation burden (TMB) is an emerging predictive cancer biomarker. Few studies have addressed the prognostic role of TMB in non-small cell lung carcinoma, with conflicting results. Moreover, the association of TMB with different histological subtypes of lung adenocarcinoma has hitherto not been systematically evaluated. Here we studied the prognostic value of TMB and its distribution in different histological subtypes of lung adenocarcinomas in a retrospective cohort using the most recent updated classification guidelines. Materials and methods: 176 surgically resected stage I–IV lung adenocarcinomas were histologically reclassified according to WHO 2015 guidelines. A modified classification subdividing the acinar subtype into classic acinar, complex glandular and cribriform subtypes was further applied and potentially prognostic histopathological characteristics such as tumor-infiltrating lymphocytes were evaluated. 148 patients with stage I–III tumors and complete follow-up data were included in the survival analyses. TMB was determined by a commercial next generation sequencing panel from 131 tumors, out of which 105 had survival data available. Results: Predominant micropapillary, solid and complex glandular as well as nonpredominant cribriform histological subtypes were associated with significantly shorter survival. High TMB concentrated in micropapillary, solid and acinar predominant subtypes. Interestingly, TMB ≥ 14 mutations/MB conferred a stage- and histology-independent survival benefit compared to TMB

Abstract Background Cell-in-cell structures (caused by cell cannibalistic activity) have been related to prognosis of many cancers. This is the first multi-institutional study to assess the prognostic impact of cell-in-cell structures in a large cohort of early oral tongue squamous cell carcinomas (OTSCC). Methods A total of 308 cases from five Finnish University Hospitals and from the A.C. Camargo Cancer Center, São Paulo, Brazil, were included in this study. Cell-in-cell structures were evaluated on surgical postoperative sections that stained with hematoxylin and eosin staining. Results We found that cell-in-cell structures associated with cancer-related mortality in univariable analysis with a hazard ratio (HR) of 2.99 (95%CI 1.52–5.88; P = 0.001). This association was confirmed in multivariable analysis (HR 2.22, 95%CI 1.12–4.44; P = 0.024). In addition, statistically significant associations were observed between the cell-in-cell structures and other adverse histopathologic characteristics including deep invasion (P

Abstract 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.

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.

ObjectiveOver many decades, studies on histopathological features have not only presented high-level evidence of contribution for treatment directions and prognosis of oral squamous cell carcinoma (OSCC) but also provided inconsistencies, making clinical application difficult. The 8th TNM staging system of OSCC has acknowledged the importance of some histopathological features, by incorporating depth of invasion (DOI) to T category and extranodal extension (ENE) to N category. The aim of this systematic review with meta-analysis is to determine the most clinically relevant histopathological features for risk assessment and treatment planning of OSCC and to elucidate gaps in the literature.MethodsA systematic review was conducted using PRISMA guidelines, and the eligibility criteria were based on population, exposure, comparison, outcome, and study type (PECOS). PubMed, Cochrane, Scopus, and Web of Science were searched for articles exploring the impact of histopathological features on OSCC outcomes with Cox multivariate analysis. Pooled data were subjected to an inverse variance method with random effects or fixed effect model, and the risk of bias was evaluated using quality in prognosis studies (QUIPS). Quality of evidence was assessed with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria.ResultsThe study included 172 articles published from 1999 to 2021. Meta-analyses confirmed the prognostic potential of DOI, ENE, perineural invasion, lymphovascular invasion, and involvement of the surgical margins and brought promising results for the association of bone invasion, tumor thickness, and pattern of invasion with increased risk for poor survival. Although with a small number of studies, the results also revealed a clinical significance of tumor budding and tumor-stroma ratio on predicted survival of patients with OSCC. Most of the studies were considered with low or moderate risk of bias, and the certainty in evidence varied from very low to high.ConclusionOur results confirm the potential prognostic usefulness of many histopathological features and highlight the promising results of others; however, further studies are advised to apply consistent designs, filling in the literature gaps to the pertinence of histopathological markers for OSCC prognosis.Systematic Review RegistrationInternational Prospective Register of Systematic Reviews (PROSPERO), identifier CRD42020219630.

Objectives: The purpose of this study was to provide a scoping review on how to address and mitigate burnout in the profession of clinical oncology. Also, it examines how artificial intelligence (AI) can mitigate burnout in oncology.Methods: We searched Ovid Medline, PubMed, Scopus, and Web of Science, for articles that examine how to address burnout in oncology.Results: A total of 17 studies were found to examine how burnout in oncology can be mitigated. These interventions were either targeted at individuals (oncologists) or organizations where the oncologists work. The organizational interventions include educational (psychosocial and mindfulness-based course), art therapies and entertainment, team-based training, group meetings, motivational package and reward, effective leadership and policy change, and staff support. The individual interventions include equipping the oncologists with adequate training that include—communication skills, well-being and stress management, burnout education, financial independence, relaxation, self-efficacy, resilience, hobby adoption, and work-life balance for the oncologists. Similarly, AI is thought to be poised to offer the potential to mitigate burnout in oncology by enhancing the productivity and performance of the oncologists, reduce the workload and provide job satisfaction, and foster teamwork between the caregivers of patients with cancer.Discussion: Burnout is common among oncologists and can be elicited from different types of situations encountered in the process of caring for patients with cancer. Therefore, for these interventions to achieve the touted benefits, combinatorial strategies that combine other interventions may be viable for mitigating burnout in oncology. With the potential of AI to mitigate burnout, it is important for healthcare providers to facilitate its use in daily clinical practices.Conclusion: These combinatorial interventions can ensure job satisfaction, a supportive working environment, job retention for oncologists, and improved patient care. These interventions could be integrated systematically into routine cancer care for a positive impact on quality care, patient satisfaction, the overall success of the oncological ward, and the health organizations at large.

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.

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.

Human papillomavirus is detected in over 50% of oropharyngeal squamous cell carcinomas. Human papillomavirus–positive oropharyngeal squamous cell carcinomas differ from human papillomavirus–negative tumors, and both expression patterns are classified as distinct entities. The Bmi-1 oncogene is a well-known member of the mammalian polycomb-group family. HESC5:3 and HES77 are newly developed monoclonal antibodies produced against undifferentiated embryonic stem cells. Our aim was to explore their roles in both human papillomavirus–positive and –negative oropharyngeal squamous cell carcinomas. Our cohort comprised 202 consecutive oropharyngeal squamous cell carcinoma patients diagnosed and treated with curative intent. We used tissue microarray tumor blocks to study the immunohistochemical expression of Bmi-1, HESC5:3, and HES77. We compared the expressions of these stem cell markers with p16 immunoexpression and human papillomavirus status, as well as with other characteristics of the tumor, and with patients’ clinical data and follow-up data. Human papillomavirus– and p16-positive tumors expressed less Bmi-1 and more HESC5:3 than the negative tumors. HES77 expression was high in human papillomavirus–positive oropharyngeal squamous cell carcinoma, but it did not correlate with p16 positivity. In our multivariable model, Bmi-1 and HESC5:3 were still associated with human papillomavirus, but the association between human papillomavirus and HES77 remained absent. In conclusion, Bmi-1, HESC5:3, and HES77 may have a different role in human papillomavirus–positive and human papillomavirus–negative tumors. There was no correlation between Bmi-1, HESC5:3, and HES77 expression and survival.