Tumor Microenvironment-Based Risk Stratification of Oropharyngeal Squamous Cell Carcinoma.

BACKGROUND: Evaluation of the prognostic impact of tumor microenvironment (TME) has received attention in recent years. We introduce a TME-based risk stratification for oropharyngeal squamous cell carcinoma (OPSCC). MATERIAL AND METHODS: A total of 182 patients treated for OPSCC at the Helsinki University Hospital were included. TME-based risk stratification was designed combining tumor-stroma ratio and stromal tumor-infiltrating lymphocytes assessed in hematoxylin and eosin-stained sections. RESULTS: In multivariable analysis, TME-based risk stratification associated with poor disease-free survival with a hazard ratio (HR) of 2.68 (95% CI 1.11-6.48, p = 0.029). In addition, the proposed risk stratification was associated with poor disease-specific survival (HR 2.687, 95% CI 1.28-5.66, p = 0.009) and poor overall survival (HR 2.21, 95% CI 1.23-3.99, p = 0.008). CONCLUSION: Our TME-based risk stratification provides a powerful prognostic tool that can be used in daily treatment planning of OPSCC together with tumor-related prognostic markers.

Psychological Factors Related to Treatment Outcomes in Head and Neck Cancer.

BACKGROUND: Patients with head and neck cancer (HNC) often demonstrate stress, distress, anxiety, depression, and are at risk for suicide. These affect their quality of life (QoL) but less attention has been given to psychological variables that may impact response to treatment. OBJECTIVES: This study aims to systematically review publications during 2013-2023 to collate evidence on the effects of psychological variables on HNC treatment outcomes. METHODS: We searched Ovid Medline, PubMed, Scopus, and Web of Science for articles that examined psychological factors related to treatment outcomes in patients with HNC. RESULTS: There were 29 studies (5 before treatment, 2 during, 17 after, and 5 covering the whole management trajectory) including 362,766 patients. The psychological factors were either behavioral (adjustment and coping strategy, unrealistic ideas, self-blame), cognitive (elevated risk of psychiatric co-comorbidity), or emotional (distress, depression, anxiety, nervousness, and fear of disfigurement and complications). It was found that there was a relationship between depression and decreased survival in patients with HNC. Pretreatment pain was an independent predictor of decreased survival in a large sample of patients. The distress level was approximately  54%, emotional problems ranged between 10 and 44%, while financial difficulties were identified in 54% of the patients. Sixty-nine percent of patients were reported to have used at least one cost-coping strategy within 6 months after treatment initiation. During post-treatment period, depression increased from 15% at the baseline to 29%, while the fear of recurrence was found among at least 35% of patients. DISCUSSION AND CONCLUSION: Several psychological factors predict QoL and survival among HNC survivors. Distress encompasses depression and anxiety, and physical burden from HNC diagnosis and treatment. Routine screening and early interventions that target distress could improve HNC survivors' QoL. A systematic and standardized measurement approach for QoL is warranted to homogenize these findings and to understand the underlying relationships.

Web-based prognostic tools for oral tongue cancer: An analysis of online predictors.

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.

Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects.

BACKGROUND: Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction. OBJECTIVES: This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC). It further explores the workflow of AI-based radiomics for personalized and precision oncology in HNC. Finally, it examines the current challenges of AI-based radiomics in daily clinical oncology and offers possible solutions to these challenges. METHODS: Comprehensive electronic databases (PubMed, Medline via Ovid, Scopus, Web of Science, CINAHL, and Cochrane Library) were searched following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. 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). RESULTS: Out of the 659 search hits retrieved, 45 fulfilled the inclusion criteria. Our review revealed that the application of AI-based radiomics model as an ancillary tool for improved decision-making in HNC management includes radiomics-based cancer diagnosis and radiomics-based cancer prognosis. The radiomics-based cancer diagnosis includes tumor staging, tumor grading, and classification of malignant and benign tumors. Similarly, radiomics-based cancer prognosis includes prediction for treatment response, recurrence, metastasis, and survival. In addition, the challenges in the implementation of these models for clinical evaluations include data imbalance, feature engineering (extraction and selection), model generalizability, multi-modal fusion, and model interpretability. CONCLUSION: Considering the highly subjective and interobserver variability that is peculiar to the interpretation of medical images by expert clinicians, AI-based radiomics seeks to offer potentially useful quantitative information, which is not visible to the human eye or unintentionally often remain ignored during clinical imaging practice. By enabling the extraction of this type of information, AI-based radiomics has the potential to revolutionize HNC oncology, providing a platform for more personalized, higher quality, and cost-effective care for HNC patients.

The prognostic role of single cell invasion and nuclear diameter in early oral tongue squamous cell carcinoma.

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.

Interpretable machine learning model for prediction of overall survival in laryngeal cancer.

Background: 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.

Emerging histopathological parameters in the prognosis of oral squamous cell carcinomas.

Oral squamous cell carcinoma (OSCC) is the most common oral malignancy, representing 90% of all malignant neoplasms in the head and neck region. Patients with this aggressive tumor have an overall 5-year survival rate of approximately 50%, which drops to less than 30% when tumors are diagnosed at advanced clinical stages. Over decades, several studies provided high-level evidence of the impact of histopathological features on treatment guidelines and prognosis of OSCC. The 8th American Joint Committee on Cancer (AJCC) TNM staging system recognized the importance of depth of invasion to the T category and extranodal extension to the N category for OSCC. This review provides the current knowledge on emerging histopathological parameters identified as potential biomarkers for OSCC, such as depth of invasion, tumor thickness, the pattern of invasion, inflammatory profile, and tumor-stroma ratio, evaluating their clinical relevance on patient outcomes. Analysis, limitations, and potential biological mechanisms are highlighted and discussed. Assessing and reporting these markers are cost-effective and can be incorporated into daily practice.

Prognostic Significance of Tumor-associated Stroma in Nasopharyngeal Carcinoma: A Multicenter Study.

Assessment of tumor-associated stroma has shown a reliable prognostic value in recent research. We evaluated the prognostic value of tumor-stroma ratio (TSR) in a large multicenter cohort of nasopharyngeal carcinoma (NPC). We used the conventional hematoxylin and eosin-stained slides of 115 cases of NPC to assess TSR as described in recent guidelines. The amount of tumor-associated stroma was assessed as a percentage and then tumors were classified as stroma-high (>50%) or stroma-low (≤50%). Kaplan-Meier curves, χ 2 test, and Cox regression univariable and multivariable analyses were carried out. A total of 48 (41.7%) tumors were stroma-high and 67 (58.3%) tumors were stroma-low. In the Cox regression multivariable analysis, the tumors categorized as stroma-high were associated with a worse overall survival with a hazard ratio of 2.30 (95% CI: 1.27-4.15, P =0.006) and with poor disease-specific survival (hazard ratio=1.87, 95% CI: 1.07-3.28, P =0.029). The assessment of TSR in NPC is simple and cost-effective, and it has a significant prognostic value. TSR can aid in risk stratification and clinical decision-making in NPC.

Predictive value of tumor budding in head and neck squamous cell carcinoma: an update.

Head and neck squamous cell carcinoma forms an anatomically and functionally complex group of malignancies. The significant local aggressiveness and frequent regional relapses motivate ongoing research to identify more reliable and sensitive prognostic and predictive biomarkers. One emerging area of cancer biology is the evaluation of tumor budding at the advancing invasive front of various types of epithelial cancers. Recent studies suggest that tumor budding is a relatively common phenomenon in cancer progression and that it may have important prognostic implications for patients due to its potential to provide valuable insights into the biology and clinical behavior of head and neck cancer. In this review, we aim to provide information about tumor budding in head and neck squamous cell carcinoma. Thus, we hope to shed light on the complex biology of these malignancies, as well as aiding diagnostic, classification, and better characterization and thereby, looking for new avenues for improving patient outcomes.

Programmed death-ligand 1 and tumor-infiltrating lymphocytes (TILs) - low TIL density may predict poorer long-term prognosis in T1 laryngeal cancer.

We evaluated the prognostic role of programmed death-ligand 1 (PD-L1) and tumor-infiltrating lymphocytes (TILs) in T1 glottic laryngeal squamous cell carcinoma (LSCC). T1 glottic LSCC patients (n = 174) treated at five Finnish university hospitals between 2003 and 2013 were included. Tissue microarray (TMA) blocks were used for PD-L1 immunohistochemistry. TILs were scored from intratumoral and stromal regions in whole tissue sections. Of 174 patients, 92 (53%) had negative, 66 (38%) intermediate, and 16 (9%) high PD-L1 levels. Of 80 patients whose TILs were analyzed, 50 (63%) had low and 30 (38%) high stromal TIL density. Patients with a local recurrence or a new primary tumor of the larynx had lower TIL density than had other patients (p = 0.047). High PD-L1 expression with low stromal TIL density was associated with inferior 5-year disease-specific survival (85% vs. 100%, p = 0.02). In conclusion, in patients treated for T1 glottic LSCC, low stromal TIL density was associated with local recurrences and new primary tumors of the larynx. High PD-L1 expression with low stromal TIL density may be associated with worse survival in T1 glottic LSCC.

The risk of second primary cancer after nasopharyngeal cancer: a systematic review.

PURPOSE: Second primary cancers (SPCs) after nasopharyngeal cancer (NPC) are rare, but have an impact on the follow-up of this patient population. The aim of this study is to systematically review the literature to determine the prevalence and most typical sites of SPCs after NPC. METHODS: We searched the databases of PubMed, Web of Science, and Scopus for articles on SPCs after NPC. The Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines were followed. RESULTS: This review includes data on 89 168 patients with NPC from 21 articles. The mean occurrence for SPCs was 6.6% and varied from 4.9% in endemic areas to 8.7% in non-endemic areas. The most frequent locations of SPCs were oral cavity, pharynx, nose and paranasal sinuses, esophagus and lung. CONCLUSION: There is an increased risk for a SPC after NPC management, especially in non-endemic areas. However, their mean rate is lower than after other head and neck carcinomas.

Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP.

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.

Application of artificial intelligence for overall survival risk stratification in oropharyngeal carcinoma: A validation of ProgTOOL.

BACKGROUND: In recent years, there has been a surge in machine learning-based models for diagnosis and prognostication of outcomes in oncology. However, there are concerns relating to the model's reproducibility and generalizability to a separate patient cohort (i.e., external validation). OBJECTIVES: This study primarily provides a validation study for a recently introduced and publicly available machine learning (ML) web-based prognostic tool (ProgTOOL) for overall survival risk stratification of oropharyngeal squamous cell carcinoma (OPSCC). Additionally, we reviewed the published studies that have utilized ML for outcome prognostication in OPSCC to examine how many of these models were externally validated, type of external validation, characteristics of the external dataset, and diagnostic performance characteristics on the internal validation (IV) and external validation (EV) datasets were extracted and compared. METHODS: We used a total of 163 OPSCC patients obtained from the Helsinki University Hospital to externally validate the ProgTOOL for generalizability. In addition, PubMed, OvidMedline, Scopus, and Web of Science databases were systematically searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: The ProgTOOL produced a predictive performance of 86.5% balanced accuracy, Mathew's correlation coefficient of 0.78, Net Benefit (0.7) and Brier score (0.06) for overall survival stratification of OPSCC patients as either low-chance or high-chance. In addition, out of a total of 31 studies found to have used ML for the prognostication of outcomes in OPSCC, only seven (22.6%) reported a form of EV. Three studies (42.9%) each used either temporal EV or geographical EV while only one study (14.2%) used expert as a form of EV. Most of the studies reported a reduction in performance when externally validated. CONCLUSION: The performance of the model in this validation study indicates that it may be generalized, therefore, bringing recommendations of the model for clinical evaluation closer to reality. However, the number of externally validated ML-based models for OPSCC is still relatively small. This significantly limits the transfer of these models for clinical evaluation and subsequently reduces the likelihood of the use of these models in daily clinical practice. As a gold standard, we recommend the use of geographical EV and validation studies to reveal biases and overfitting of these models. These recommendations are poised to facilitate the implementation of these models in clinical practice.

Tumor-stroma ratio is a promising prognostic classifier in oropharyngeal cancer.

Tumor-stroma ratio (TSR) has been analyzed in many tumor types. To date, the clinical significance of TSR has not been investigated in oropharyngeal squamous cell carcinoma (OPSCC). We used a recently introduced recommendation for the assessment of TSR in a large cohort of 182 patients with OPSCC treated at the Helsinki University Hospital. The percentage of tumor-associated stroma was estimated in hematoxylin and eosin (HE)-stained sections and categorized into 2 groups: "stroma-high" (>50%) and "stroma-low" (≤50%). In multivariable analysis, TSR had a significant association with patient survival as stroma-high tumors showed worse disease-free survival (hazard ratio [HR] = 3.22, 95% confidence interval [CI] = 1.43-7.26, P = .005), disease-specific survival (HR = 2.48, 95% CI = 1.29-4.74, P = .006), and overall survival (HR = 2.23, 95% CI = 1.29-3.85, P = .004). The prognostic value of TSR was superior to the Tumor-Node-Metastasis classification. In addition, the significant prognostic value of TSR was demonstrated when analyzing human papillomavirus (HPV)-positive and HPV-negative cases separately (P < .05). In conclusion, TSR is a powerful prognostic indicator in OPSCC. It can be assessed quickly without additional costs using standard HE slides. Owing to its simplicity and reproducibility, TSR can be implemented in routine pathology diagnostics and reporting. Patients with stroma-rich tumors have an increased risk of recurrence and cancer-related mortality and may benefit from appropriate intensive treatment strategies with close follow-up.

Advanced-stage tongue squamous cell carcinoma: a machine learning model for risk stratification and treatment planning.

BACKGROUND: A significant number of tongue squamous cell carcinoma (TSCC) patients are diagnosed at late stage. OBJECTIVES: We primarily aimed to develop a machine learning (ML) model based on ensemble ML paradigm to stratify advanced-stage TSCC patients into the likelihood of overall survival (OS) for evidence-based treatment. We compared the survival outcome of patients who received either surgical treatment only (Sx) or surgery combined with postoperative radiotherapy (Sx + RT) or postoperative chemoradiotherapy (Sx + CRT). MATERIAL AND METHODS: A total of 428 patients from Surveillance, Epidemiology, and End Results (SEER) database were reviewed. Kaplan-Meier and Cox proportional hazards models examine OS. In addition, a ML model was developed for OS likelihood stratification. RESULTS: Age, marital status, N stage, Sx, and Sx + CRT were considered significant. Patients with Sx + RT showed better OS than Sx + CRT or Sx alone. A similar result was obtained for T3N0 subgroup. For T3N1 subgroup, Sx + CRT appeared more favorable for 5-year OS. In T3N2 and T3N3 subgroups, the numbers of patients were small to make insightful conclusions. The OS predictive ML model showed an accuracy of 86.3% for OS likelihood prediction. CONCLUSIONS AND SIGNIFICANCE: Patients stratified as having high likelihood of OS may be managed with Sx + RT. Further external validation studies are needed to confirm these results.

Clinical significance of overall assessment of tumor-infiltrating lymphocytes in oropharyngeal cancer: A meta-analysis.

BACKGROUND: The overall assessment of tumor-infiltrating lymphocytes (TILs) evaluated using hematoxylin and eosin (HE) staining has been recently studied in oropharyngeal squamous cell carcinoma (OPSCC). METHODS: We conducted a systematic review of Scopus, Ovid Medline, PubMed, Web of Science, and Cochrane Library to retrieve studies assessing TILs in HE-stained sections of OPSCC. We used fixed-effect models and random-effect models to estimate the pooled hazard ratios (HRs) and confidence intervals (CIs) for disease-free survival (DFS), overall survival (OS) and disease-specific survival (DSS). RESULTS: Eleven studies were identified that had analyzed the prognostic significance of TILs in OPSCC using HE-stained specimens. Our meta-analyses showed that a high infiltration of TILs was significantly associated with improved DFS (HR 0.39, 95%CI 0.24-0.65, P = 0.0003), OS (HR 0.38, 95%CI 0.29-0.50, P < 0.0001), and DSS (HR 0.32, 95%CI 0.19-0.53, P < 0.0001). CONCLUSION: Findings of our meta-analysis support a growing body of evidence indicating that assessment of TILs in OPSCC using HE-stained sections has reliable prognostic value. The clinical significance of such assessment of TILs has been reported repeatedly in many studies on OPSCC. The assessment is cost-effective, feasible, easy to transfer from lab to clinic, and therefore can be incorporated in daily practice.