Influence of time between surgery and adjuvant radiotherapy on prognosis for patients with head and neck squamous cell carcinoma: A systematic review.

The timing of postoperative radiotherapy following surgical intervention in patients with head and neck cancer remains a controversial issue. This review aims to summarize findings from available studies to investigate the influence of time delays between surgery and postoperative radiotherapy on clinical outcomes. Articles between 1 January 1995 and 1 February 2022 were sourced from PubMed, Web of Science, and ScienceDirect. Twenty-three articles met the study criteria and were included; ten studies showed that delaying postoperative radiotherapy might negatively impact patients and lead to a poorer prognosis. Delaying the start time of radiotherapy, 4 weeks after surgery did not result in poorer prognoses for patients with head and neck cancer, although delays beyond 6 weeks might worsen patients' overall survival, recurrence-free survival, and locoregional control. Prioritization of treatment plans to optimize the timing of postoperative radiotherapy regimes is recommended.

Predicting Overall Survival Using Machine Learning Algorithms in Oral Cavity Squamous Cell Carcinoma.

BACKGROUND/AIM: Machine learning (ML) models are often modelled to predict cancer prognosis but rarely consider spatial factors in a region. Hence this study explored machine learning algorithms utilising Local Government Areas (LGAs) in Queensland, Australia to spatially predict 3- and 5-year prognosis of oral cancer patients and provide clinical interpretability of the predicted outcome made by the ML model. PATIENTS AND METHODS: Data from a total of 3,841 oral cancer patients were retrieved from the Queensland Cancer Registry (QCR). Synthesizing minority oversampling technique together with edited nearest neighbours (SMOTE-ENN) was used to pre-process unbalanced datasets. Five ML models: logistic regression, random forest classifier, XGBoost, Gaussian Naïve Bayes and Voting Classifier were trained. Predictive features were age, sex, LGAs, tumour site and differentiation. Outcomes were 3- and 5-year overall survival of patients. Model performances on test set were evaluated using area under the curve and F1 scores. SHapley Additive exPlanations (SHAP) method was applied to the best performing model for model interpretation of the predicted outcome. RESULTS: The Voting Classifier was the best performing model with F1 score of 0.58 and 0.64 for 3- and 5-year overall survival, respectively. Age was the most important feature in the Voting Classifier in 3- and 5-year prognosis prediction. LGAs at diagnosis was the top 3 predictive feature for both 3- and 5-year models. CONCLUSION: The Voting Classifier demonstrated the best overall performance in classifying both 3- and 5-year overall survival of oral cancer patients in Queensland. SHAP method provided clinical understanding of the predictive features of the Voting Classifier.

Performance of a simplified scoring system for risk stratification in oral cancer and oral potentially malignant disorders screening.

BACKGROUND: Impact and efficiency of oral cancer and oral potentially malignant disorders screening are most realized in "at-risk" individuals. However, tools that can provide essential knowledge on individuals' risks are not applied in risk-based screening. This study aims to optimize a simplified risk scoring system for risk stratification in organized oral cancer and oral potentially malignant disorders screening. METHODS: Participants were invited to attend a community-based oral cancer and oral potentially malignant disorders screening program in Hong Kong. Visual oral examination was performed for all attendees and information on sociodemographic characteristics as well as habitual, lifestyle, familial, and comorbidity risk factors were obtained. Individuals' status of those found to have suspicious lesions following biopsy and histopathology were classified as positive/negative and this outcome was used in a multiple logistic regression analysis with variables collected during screening. Odds ratio weightings were then used to develop a simplified risk scoring system which was validated in an external cohort. RESULTS: Of 979 participants, 4.5% had positive status following confirmatory diagnosis. A 12-variable simplified risk scoring system with weightings was generated with an AUC, sensitivity, and specificity of 0.82, 0.71, and 0.78 for delineating high-risk cases. Further optimization on the validation cohort of 491 participants yielded a sensitivity and specificity of 0.75 and 0.87 respectively. CONCLUSIONS: The simplified risk scoring system was able to stratify oral cancer and oral potentially malignant disorders risk with satisfactory sensitivity and specificity and can be applied in risk-based disease screening.

Comparison of time-to-event machine learning models in predicting oral cavity cancer prognosis.

BACKGROUND: Applying machine learning to predicting oral cavity cancer prognosis is important in selecting candidates for aggressive treatment following diagnosis. However, models proposed so far have only considered cancer survival as discrete rather than dynamic outcomes. OBJECTIVES: To compare the model performance of different machine learning-based algorithms that incorporate time-to-event data. These algorithms included DeepSurv, DeepHit, neural net-extended time-dependent cox model (Cox-Time), and random survival forest (RSF). MATERIALS AND METHODS: Retrospective cohort of 313 oral cavity cancer patients were obtained from electronic health records. Models were trained on patient data following preprocessing. Predictors were based on demographic, clinicopathologic, and treatment information of the cases. Outcomes were the disease-specific and overall survival. Multivariable analyses were conducted to select significant prognostic features associated with tumor prognosis. Two models were generated per algorithm based on all-prognostic features and significant-prognostic features following statistical analysis. Concordance index (c-index) and integrated Brier scores were used as performance evaluators and model stability was assessed using intraclass correlation coefficients (ICC) calculated from these measures obtained from the cross-validation folds. RESULTS: While all models were satisfactory, better discriminatory performance and calibration was observed for disease-specific than overall survival (mean c-index: 0.85 vs 0.74; mean integrated Brier score: 0.12 vs 0.17). DeepSurv performed best in terms of discrimination for both outcomes (c-indices: 0.76 -0.89) while RSF produced better calibrated survival estimates (integrated Brier score: 0.06 -0.09). Model stability of the algorithms varied with the outcomes as Cox-Time had the best intraclass correlation coefficient (mean ICC: 1.00) for disease-specific survival while DeepSurv was most stable for overall survival prediction (mean ICC: 0.99). CONCLUSIONS: Machine learning algorithms based on time-to-event outcomes are successful in predicting oral cavity cancer prognosis with DeepSurv and RSF producing the best discriminative performance and calibration.

Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review.

OBJECTIVES: Machine learning platforms are now being introduced into modern oncological practice for classification and prediction of patient outcomes. To determine the current status of the application of these learning models as adjunctive decision-making tools in oral cavity cancer management, this systematic review aims to summarize the accuracy of machine-learning based models for disease outcomes. METHODS: Electronic databases including PubMed, Scopus, EMBASE, Cochrane Library, LILACS, SciELO, PsychINFO, and Web of Science were searched up until December 21, 2020. Pertinent articles detailing the development and accuracy of machine learning prediction models for oral cavity cancer outcomes were selected in a two-stage process. Quality assessment was conducted using the Quality in Prognosis Studies (QUIPS) tool and results of base studies were qualitatively synthesized by all authors. Outcomes of interest were malignant transformation of precancer lesions, cervical lymph node metastasis, as well as treatment response, and prognosis of oral cavity cancer. RESULTS: Twenty-seven articles out of 950 citations identified from electronic and manual searching were included in this study. Five studies had low bias concerns on the QUIPS tool. Prediction of malignant transformation, cervical lymph node metastasis, treatment response, and prognosis were reported in three, six, eight, and eleven articles respectively. Accuracy of these learning models on the internal or external validation sets ranged from 0.85 to 0.97 for malignant transformation prediction, 0.78-0.91 for cervical lymph node metastasis prediction, 0.64-1.00 for treatment response prediction, and 0.71-0.99 for prognosis prediction. In general, most trained algorithms predicting these outcomes performed better than alternate methods of prediction. We also found that models including molecular markers in training data had better accuracy estimates for malignant transformation, treatment response, and prognosis prediction. CONCLUSION: Machine learning algorithms have a satisfactory to excellent accuracy for predicting three of four oral cavity cancer outcomes i.e., malignant transformation, nodal metastasis, and prognosis. However, considering the training approach of many available classifiers, these models may not be streamlined enough for clinical application currently.

"Fact or fiction?": Oral cavity cancer in nonsmoking, nonalcohol drinking patients as a distinct entity-Scoping review.

Oral cavity cancer is often described as a lifestyle-related malignancy due to its strong associations with habitual factors, including tobacco use, heavy alcohol consumption, and betel nut chewing. However, patients with no genetically predisposing conditions who do not indulge in these risk habits are still being encountered, albeit less commonly. The aim of this review is to summarize contemporaneous reports on these nonsmoking, nonalcohol drinking (NSND) patients. We performed database searching to identify relevant studies from January 1, 2000 to March 31, 2021. Twenty-six articles from 20 studies were included in this study. We found that these individuals were mostly females in their eighth decade with tumors involving the tongue and gingivobuccal mucosa. This review also observed that these patients were likely diagnosed with early stage tumors with overexpression of programmed death-ligand 1 (PD-L1) and increased intensity of tumor infiltrating lymphocytes. Treatment response and disease-specific prognosis were largely comparable between NSND and smoking/drinking patients.

Prognostic value of non-smoking, non-alcohol drinking status in oral cavity cancer.

OBJECTIVES: To compare the treatment response and prognosis of oral cavity cancer between non-smoking and non-alcohol-drinking (NSND) patients and smoking and alcohol-drinking (SD) patients. METHODS: A total of 313 consecutively treated patients from 2000 to 2019 were included. Demographic, clinicopathologic, treatment, and prognosis information were obtained. Relapse-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) were compared between NSND and SD groups using Kaplan-Meier plots, log-rank test, and multivariate Cox regression analysis. RESULTS: Sample prevalence of NSND patients was 54.6%. These patients were predominantly females in their eighth decade with lower prevalence of floor of the mouth cancers compared to SD patients (1.8% vs 14.8%). No difference in the RFS and DSS between both groups was found following multivariable analysis; however, NSND patients had better OS (HR (95% CI) - 0.47 (0.29-0.75); p = 0.002). Extracapsular extension was associated with significantly poorer OS, DSS, and RFS in this oral cavity cancer cohort. CONCLUSION: Treatment response and disease-specific prognosis are comparable between NSND and SD patients with oral cavity cancer. However, NSND patients have better OS. CLINICAL RELEVANCE: This study shows that oral cavity cancer in NSND is not less or more aggressive compared to SD patients. Although better survival is expected for NSND than SD patients, this is likely due to the reduced incidence of other chronic diseases in the NSND group.

Impact of unilateral ureteral obstruction on cognition and neurodegeneration.

INTRODUCTION: Cognitive impairment is a common complication in chronic kidney disease (CKD) patients. Currently, limited types of animal models are available for studying cognitive impairment in CKD. We used unilateral ureteral obstruction (UUO) in mice as an animal model to study the cognitive changes and related pathology under prolonged renal impairment METHODS: UUO was performed in 8-week-old male C57BL/6 N mice with double-ligation of their left ureter. A sham group was subjected to the same experimental procedure without ureteral obstruction. Cognitive and behavioral tests were performed to examine potential changes in cognition and behavior at 2, 4 and 12 weeks after surgery. Sera were collected, and kidneys and brains were harvested for the detection of systemic inflammation markers and neurodegenerative changes. RESULTS: These mice displayed weak performance in the novel object recognition test, Y-maze test, and puzzle box test compared to the sham group. Reductions in synaptic proteins such as synapsin-1, synaptophysin, synaptotagmin, PSD95, NMDAR2B and AMPAR were confirmed by western blot analysis. Histological examination revealed elevated levels of Nrf2 and 8-hydroxyguanosine, and hyperphosphorylation of tau in the hippocampus. UUO mice also had increased levels of C-reactive protein (CRP) and TNF-α. CONCLUSIONS: We characterized the cognitive and neuropathological changes in UUO mice. The results show that this mouse model can be used to further study cognitive changes related to chronic renal impairment.