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Background: Oral tongue squamous cell carcinoma (OTSCC) often presents with aggressive clinical behaviour that may require multimodality treatment based on reliable prognostication. We aimed to evaluate the prognostic ability of five online web‐based tools to predict the clinical behaviour of OTSCC resection and biopsy samples. Methods: A total of 135 OTSCC resection cases and 33 OTSCC biopsies were included to predict recurrence and survival. Area under the receiver operating characteristic curves (AUC), χ2 tests, and calibration plots constructed to estimate the prognostic power of each tool. Results: The tool entitled 'Prediction of risk of Locoregional Recurrences in Early OTSCC' presented an accuracy of 82%. The tool, 'Head & Neck Cancer Outcome Calculator' for 10‐year cancer‐related mortality had an accuracy 77% and AUC 0.858. The other tool entitled 'Cancer Survival Rates' for 5‐year mortality showed an accuracy of 74% and AUC of 0.723. For biopsy samples, 'Cancer Survival Prediction Calculators' predicted the recurrence free survival with an accuracy of 70%. Conclusions: Web‐based tools can aid in clinical decision making of OTSCC. Three of five online web‐based tools could predict recurrence risk and cancer‐related mortality in resected OTSCC and one tool could help in clinical decision making for biopsy samples. [ABSTRACT FROM AUTHOR]

AbstractBackground: The mortality rates of laryngeal squamous cell carcinoma cancer (LSCC) have not significantly decreased in the last decades.Objectives: We primarily aimed to compare the predictive performance of DeepTables with the state-of-the-art machine learning (ML) algorithms (Voting ensemble, Stack ensemble, and XGBoost) to stratify patients with LSCC into chance of overall survival (OS). In addition, we complemented the developed model by providing interpretability using both global and local model-agnostic techniques.Methods: A total of 2792 patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with LSCC were reviewed. The global model-agnostic interpretability was examined using SHapley Additive exPlanations (SHAP) technique. Likewise, individual interpretation of the prediction was made using Local Interpretable Model Agnostic Explanations (LIME).Results: The state-of-the-art ML ensemble algorithms outperformed DeepTables. Specifically, the examined ensemble algorithms showed comparable weighted area under receiving curve of 76.9, 76.8, and 76.1 with an accuracy of 71.2%, 70.2%, and 71.8%, respectively. The global methods of interpretability (SHAP) demonstrated that the age of the patient at diagnosis, N-stage, T-stage, tumor grade, and marital status are among the prominent parameters.Conclusions: A ML model for OS prediction may serve as an ancillary tool for treatment planning of LSCC patients. [ABSTRACT FROM AUTHOR]

Purpose/Objectives: Artificial intelligence (AI) and its subfields such as machine learning (ML) and deep learning (DL) have been reported to show high performance, particularly in the diagnosis and prognosis of various cancers 1–3. Despite the promising results obtained from these studies, advancements in technology have increased the availability of medical images in different formats such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasound images 1,2. Interestingly, these image formats are more than pictures, they are in fact, data 4. As a result, several approaches such as radiomics have been recently used to extract information that is not visible to the human eye during routine examination from these images 5. Hence, radiomics is an emerging field in radiology that provides a non-invasive approach for extracting quantitative features from medical cancer images such as CT, PET, MRI, or a combination of these image formats 5. Traditionally, the extracted features are processed using statistical approaches. However, due to the large amount of extracted features, statistical approaches can no longer effectively handle these features 6,7. Therefore, in recent years, several studies exploring the potential of AI or its subfields such as ML and DL in handling the extracted features to produce a predictive model have shown promising results that are capable of revolutionizing HNC management 8. However, in actual everyday clinical practice, the use of AI-based radiomics models remains limited 5. This is due to several implementation challenges 5. Objectives: This study provides a systematic review of the challenges, concerns, and limitations of AI-based models in head and neck cancer (HNC) oncology. Furthermore, we suggest possible solutions to these challenges. We searched OvidMedline, PubMed, Scopus, and Web of Science databases for articles that examine the challenges of AI-based models in HNC oncology. The quality of included studies and their risk of biases were evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Risk of Bias Assessment Tool (PROBAST). A total of 11 studies examined the challenges of these models in HNC oncology. These challenges have been summarized into 5 themes. These include data, science of machine learning, model generalizability, model maintenance and performance improvements, and ethical and regulatory concerns. The data concerns include data management, data imbalance and heterogeneity, privacy, and confidentiality. The concerns relating to the science of machine learning include model and result interpretability, explainability, and model performance. The model's generalizability encompasses internal and external independent geographic validation of the models. Some of the ethical and regulatory concerns were as follows: model-clinician disagreement (contradictory diagnostic or prognostic opinion between the model and clinician), workflow and user-design challenges, patient's liberty to decide the type of treatment to follow may be violated, patient–clinician relationship may change, standardized reporting guidelines of AI model development, and outdated regulatory and legal frameworks. Stakeholders such as the governments, medical institutions, hospital management, clinicians, physicians, regulatory agencies, technological companies, politicians, ethicists, philosophers, legal experts, patient representatives, data scientists, and machine learning experts need to be deeply involved in the development of internationally standardized and structured reporting guidelines for the AI-based model to be beneficial in daily clinical practice. The envisioned guidelines should define solutions to data issues by defining a robust data economy framework. Model interpretability and explainability can be handled using explainability techniques by providing both local and global interpretations of the predictions made by the model. Independent external geographic validation of the model should be a standard procedure. A federated learning paradigm can enhance continuous model improvement and generalizability. The guidelines should also include a defined ethical and regulatory framework for the development and reporting of AI-based radiomics models. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Background: While the relevance of the World Health Organization histopathological grading system as a prognostic tool for oral squamous cell carcinoma has received many critics, other histopathological features such as tumor‐stroma ratio, tumor‐infiltrating lymphocytes, and tumor budding are displaying promising results. Here, we evaluated the prognostic impact of the incorporation of tumor‐stroma ratio, tumor‐infiltrating lymphocytes, and tumor budding into World Health Organization histopathological grading for patients with oral squamous cell carcinoma. Methods: A total of 95 patients with early‐stage oral squamous cell carcinoma were enrolled in the study, and World Health Organization tumor grading, tumor‐stroma ratio, tumor‐infiltrating lymphocytes, and tumor budding were evaluated in surgical slides stained with hematoxylin and eosin. Survival analyses for cancer‐specific survival and disease‐free survival were performed using Cox regression models, and receiver operating characteristic curves were applied for assessment of the performance of the combinations. Results: Tumor‐stroma ratio (stroma‐rich) was significantly and independently associated with both shortened cancer‐specific survival and poor disease‐free survival, individually and in combination with World Health Organization histopathological grading. The combination of tumor‐stroma ratio with World Health Organization grading did not improve the discriminatory ability compared to tumor‐stroma ratio alone. Although low tumor‐infiltrating lymphocytes were associated with shortened cancer‐specific survival, the association did not withstand multivariate analysis. However, in combination with World Health Organization grading, low tumor‐infiltrating lymphocytes were independently associated with poor cancer‐specific survival. The combination of tumor‐infiltrating lymphocytes and World Health Organization histopathological grading displayed a better discrimination of poor cancer‐specific survival than tumor‐infiltrating lymphocytes alone, but not at a significant level. Conclusion: Our findings support tumor‐stroma ratio as a potential prognostic marker for patients with oral squamous cell carcinoma, and the incorporation of tumor‐infiltrating lymphocytes into the World Health Organization grading system improves the prognostic ability of the tumor grading alone. [ABSTRACT FROM AUTHOR]

AbstractBackgroundObjectivesMethodologyResultsConclusionsThere is a lack of prognosticators of overall survival (OS) for Oral Squamous Cell Carcinoma (OSCC).We examined collaborative machine learning (cML) in estimating the OS of OSCC patients. The prognostic significance of the clinicopathological parameters was examined.Altogether, 9439 OSCC patients were extracted from the Surveillance, Epidemiology, and End Results database (US). Five ML models – voting ensemble, stacked ensemble, extreme gradient boosting, light boosting, and logistic regression were used to predict OS. Three of these ML algorithms were combined to form a cluster of cML models. The performance of the cML was compared with the best performing individual ML algorithm following model training.The performance accuracy of the voting ensemble, stacked ensemble, extreme gradient boosting, light boosting, and logistic regression models was 70.2%, 69.9%, 69.1%, 69.4%, and 69.5% respectively, following model training. When the voting ensemble model was compared with cML using temporal validation, the cML showed a comparable performance accuracy. The most significant prognostic factors were age of the patient at diagnosis, T stage, tumor grade, marital status, gender, primary site, surgery, N stage, radiotherapy, ethnicity, chemotherapy, and M stage.cML appears to give reliability to the final prediction and thereby may mark a paradigm shift from model individualism to a more cooperative paradigm. This approach may aid in determining an enhanced individualized treatment for OSCC patients. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Materials and Methods Results Conclusion There is lack of knowledge on the utility of prognostic histopathologic characteristics in adenoid cystic carcinoma (ACC) of the head and neck. We evaluated the prognostic value of tumor and stroma‐related histopathologic features in ACC.A total of 65 cases of ACC from minor and major salivary glands were included in this study. We evaluated tumor budding, tumor‐infiltrating lymphocytes (TILs), and tumor‐stroma ratio (TSR) in hematoxylin and eosin (HE) stained sections.Stroma‐rich ACCs recurred more frequently (p = 0.029) during follow‐up and associated with distant metastasis (p = 0.038). In multivariable analysis, stroma‐rich tumors associated with poorer disease‐specific survival with a hazard ratio of 3.76 (95% CI 1.10–12.83, p = 0.034). ACCs commonly showed a low infiltration of TILs as 89% of the tumors was characterized by an immune desert pattern. Low infiltration of TILs associated significantly with increased tumor budding (p = 0.039).Adverse features of TSR and tumor budding are widely expressed in ACC, and stroma‐rich tumors are associated with poor prognosis. Low number of TILs in ACC tissue indicates a weak immune response by the host and illustrates the nature of ACC as a relentless malignancy. [ABSTRACT FROM AUTHOR]