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As early detection is crucial for improvement of cancer prognosis, we searched for biomarkers in plasma from individuals who later developed squamous cell carcinoma of the oral tongue (SCCOT) as well as in patients with an already established SCCOT. Levels of 261 proteins related to inflammation and/or tumor processes were measured using the proximity extension assay (PEA) in 179 plasma samples (42 collected before diagnosis of SCCOT with 81 matched controls; 28 collected at diagnosis of SCCOT with 28 matched controls). Statistical modeling tools principal component analysis (PCA) and orthogonal partial least square - discriminant analysis (OPLS-DA) were applied to provide insights into separations between groups. PCA models failed to achieve group separation of SCCOT patients from controls based on protein levels in samples taken prior to diagnosis or at the time of diagnosis. For pre-diagnostic samples and their controls, no significant OPLS-DA model was identified. Potentials for separating pre-diagnostic samples collected up to five years before diagnosis (n = 15) from matched controls (n = 28) were seen in four proteins. For diagnostic samples and controls, the OPLS-DA model indicated that 21 proteins were important for group separation. TNF receptor associated factor 2 (TRAF2), decreased in pre-diagnostic plasma (< 5 years) but increased at diagnosis, was the only protein showing altered levels before and at diagnosis of SCCOT (p-value < 0.05). Taken together, changes in plasma protein profiles at diagnosis were evident, but not reliably detectable in pre-diagnostic samples taken before clinical signs of tumor development. Variation in protein levels during cancer development poses a challenge for the identification of biomarkers that could predict SCCOT development.

Background: Interpretable machine learning (ML) for early detection of cancer has the potential to improve risk assessment and early intervention.

Methods: Data from 261 proteins related to inflammation and/or tumor processes in 123 blood samples collected from healthy persons, but of whom a sub-group later developed squamous cell carcinoma of the oral tongue (SCCOT), were analyzed. Samples from people who developed SCCOT within less than 5 years were classified as tumor-to-be and all other samples as tumor-free. The optimal ML algorithm for feature selection was identified and feature importance computed by the SHapley Additive exPlanations (SHAP) method. Five popular ML algorithms (AdaBoost, Artificial neural networks [ANNs], Decision Tree [DT], eXtreme Gradient Boosting [XGBoost], and Support Vector Machine [SVM]) were applied to establish prediction models, and decisions of the optimal models were interpreted by SHAP.

Results: Using the 22 selected features, the SVM prediction model showed the best performance (sensitivity = 0.867, specificity = 0.859, balanced accuracy = 0.863, area under the receiver operating characteristic curve [ROC-AUC] = 0.924). SHAP analysis revealed that the 22 features rendered varying person-specific impacts on model decision and the top three contributors to prediction were Interleukin 10 (IL10), TNF Receptor Associated Factor 2 (TRAF2), and Kallikrein Related Peptidase 12 (KLK12).

Conclusion: Using multidimensional plasma protein analysis and interpretable ML, we outline a systematic approach for early detection of SCCOT before the appearance of clinical signs.

Background: In Sweden around 1400 people are affected by head and neck cancer each year, and around 400 of these tumours are located in the mobile tongue (SCCOT). A major problem with these tumours is the high degree of relapse. In order to broaden our understanding of the group of squamous cell carcinoma of the head and neck (SCCHN) tumours we evaluated and compared the outcomes of panendoscopy with biopsy, ultrasonography with fine needle aspiration cytology (US-FNAC), and preoperative positron emission tomography/computed tomography (PET/CT) data from the same patients. As patients with SCCHN frequently have distant metastasis and locoregional recurrences, machine learning (ML) techniques were used to create classification models that accurately predict the likelihood of an early recurrence.

Materials and methods: From patients suspected of having head and neck cancer between 2014–2016 results from PET/CT, panendoscopy with biopsy and US-FNAC were compared. Clinical, genomic, transcriptomic, and proteomic markers identifying recurrence risk were investigated. In blood samples taken from healthy individuals, data from proteins relevant to inflammation and/or tumor processes were evaluated. The SHapley Additive Explanations (SHAP) approach was used to determine the best ML algorithm for feature selection. AdaBoost, Artificial neural networks (ANNs), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) were used to create prediction models. Clinical data from patients were analyzed using statistical and ML techniques.

Results: The concordance between results from PET/CT and panendoscopy with biopsy was 91.3%, and somewhat lower, 89.1%, for PET/CT and US-FNAC. The top contributors to classification with the ML approach were five mRNAs (PLAUR, DKK1, AXIN2, ANG, VEGFA), and 10 proteins (RAD50, 4E-BP1, MYH11, MAP2K1, BECN1, NF2, RAB25, ERRFI1, KDR, SERPINE1), using the extreme gradient boosting (XGBoost) method. The SHAP approach was used for feature selection. Using data from analysis of proteins in blood and interpretable ML showed that the Support Vector Machine (SVM) had the best performance with a balanced accuracy of 0.863, and a ROC-AUC of 0.924. The top three contributors to the SVM prediction model's performance were IL10, TNF Receptor Associated Factor 2 (TRAF2), and Kallikrein Related Peptidase 12 (KLK12). Recurrence was correlated with diabetes (p = 0.003), radiographic neck metastasis (p = 0.010), and T stage (p = 0.0012). A ML model got an accuracy rate of 71.2%. In the SCCOT group, diabetics predominated over non-diabetics, and also had lower recurrence rates and better survival (p = 0.012).

Conclusion: Results show that the combination PET/CT is useful in diagnosis of SCCHN. It further emphasizes the use of ML to identify transcriptomic and proteomic factors that are significant in predicting risk of recurrence in patients with SCCHN. It provides a methodical strategy for early diagnosis of SCCOT before onset of clinical symptoms using multidimensional plasma protein profiling and interpretable ML. A model for predicting recurrence of SCCOT is provided by ML utilizing clinical data. As SCCOT patients with co-existing diabetes showed a better prognosis than non-diabetics, results suggest that individuals with SCCOT, regardless of diabetes status, may benefit from therapeutic management of glucose levels.

Introduction: To compare data from preoperative positron emission tomography/computed tomography (PET/CT) with results of panscopy with biopsy and ultrasound with fine needle aspiration cytology (US-FNAC) on the same patients.

Methods: In this retrospective (2014-2016) study, we compared PET/CT results with the results from panscopy with biopsy and US-FNAC in patients suspected of head and neck malignancy treated at the University Hospital in Umea, Sweden.

Results: A 91.3% concordance was seen between results from PET/CT and panscopy with biopsy, whereas between PET/CT and US-FNAC the concordance was 89.1%.

Conclusions: The present data show the usefulness of PET/CT in the diagnosis of head and neck malignancies.

Studies have shown lower treatment-related morbidity when using transoral robotic surgery (TORS) compared to conventional surgery. Patients investigated for oro- and hypopharyngeal cancer (T1, T2) were compared concerning quality of life (QoL) after tonsillectomy and TORS using validated QoL questionnaires: QLQ-C30 and QLQ-H&N35. The patients treated with TORS showed a higher pain score and thus also a higher need for painkillers, whereas they had lower values on self-assessment of anxiety/depression using the Hospital Anxiety and Depression Scale score. The pre- and postoperative information given did not meet the expectations of the patients treated with conventional surgery. The present data show advantages of the TORS technique from the patients' perspective. Even if patients treated with TORS are in need of more painkilling treatment, they cope better with the long-term effects of treatment, as judged by self-assessment of anxiety and depression.

Background: Patients with squamous cell carcinoma of the head and neck (SCCHN) have a high-risk of recurrence. We aimed to develop machine learning methods to identify transcriptomic and proteomic features that provide accurate classification models for predicting risk of early recurrence in SCCHN patients.

Methods: Clinical, genomic, transcriptomic and proteomic features distinguishing recurrence risk were examined in SCCHN patients from The Cancer Genome Atlas (TCGA). Recurrence within one year after treatment was classified as high-risk and no recurrence as low-risk.

Results: No significant differences in individual clinicopathological characteristics, mutation profiles or mRNA expression patterns were seen between the groups using conventional statistical analysis. Using the machine learning algorithm, extreme gradient boosting (XGBoost), ten proteins (RAD50, 4E-BP1, MYH11, MAP2K1, BECN1, NF2, RAB25, ERRFI1, KDR, SERPINE1) and five mRNAs (PLAUR, DKK1, AXIN2, ANG and VEGFA) made the greatest contribution to classification. These features were used to build improved models in XGBoost, achieving the best discrimination performance when combining transcriptomic and proteomic data, providing an accuracy of 0.939 and an Area Under the ROC Curve (AUC) of 0.951.

Conclusions: This study highlights machine learning to identify transcriptomic and proteomic factors that play important roles in predicting risk of recurrence in patients with SCCHN and to develop such models by iterative cycles to enhance their accuracy, thereby aiding the introduction of personalized treatment regimens.

Locoregional recurrences and distant metastases are major problems for patients with squamous cell carcinoma of the head and neck (SCCHN). Because SCCHN is a heterogeneous group of tumours with varying characteristics, the present study concentrated on the subgroup of squamous cell carcinoma of the oral tongue (SCCOT) to investigate the use of machine learning approaches to predict the risk of recurrence from routine clinical data available at diagnosis. The approach also identified the most important parameters that identify and classify recurrence risk. A total of 66 patients with SCCOT were included. Clinical data available at diagnosis were analysed using statistical analysis and machine learning approaches. Tumour recurrence was associated with T stage (P=0.001), radiological neck metastasis (P=0.010) and diabetes (P=0.003). A machine learning model based on the random forest algorithm and with attendant explainability was used. Whilst patients with diabetes were overrepresented in the SCCOT cohort, diabetics had lower recur‑ rence rates (P=0.015 after adjusting for age and other clinical features) and an improved 2‑year survival (P=0.025) compared with non‑diabetics. Clinical, radiological and histological data available at diagnosis were used to establish a prognostic model for patients with SCCOT. Using machine learning to predict recurrence produced a classification model with 71.2% accuracy. Notably, one of the findings of the feature importance rankings of the model was that diabetics exhibited less recur‑ rence and improved survival compared with non‑diabetics, even after accounting for the independent prognostic variables of tumour size and patient age at diagnosis. These data imply that the therapeutic manipulation of glucose levels used to treatdiabetes may be useful for patients with SCCOT regardless of their diabetic status. Further studies are warranted to investigatethe impact of diabetes in other SCCHN subtypes.