Background
Treatment of head and neck cancer (HNC) often leads to visible and severe functional impairments. In addition, patients often suffer from a variety of psychosocial problems, significantly associated with a decreased quality of life. We aimed to compare depression, anxiety, fatigue and quality of life (QoL) between HNC patients and a large sample of the general population in Germany and to examine the impact of sociodemographic, behavioral and clinical factors on these symptoms.
Methods
We assessed data of HNC patients during the aftercare consultation at the Leipzig University Medical Center with a patient reported outcome (PRO) tool named “OncoFunction”. Depression, anxiety, fatigue and QoL were assessed using validated outcome measures including the PHQ-9, the GAD-2, and the EORTC QLQ-C30 questionnaire.
Results
A total of 817 HNC patients were included in our study and compared to a sample of 5018 individuals of the general German population. HNC patients showed significantly higher levels of impairment in all dimensions assessed. Examination of association between depression, anxiety, fatigue and QoL and clinical as well as sociodemographic variables showed significant relationships between occupational status, ECOG-state, body mass index and time since diagnosis.
Conclusions
HNC patients suffer significantly from psychological distress. The used questionnaires are suitable for the use in daily routine practice and can be helpful to increase the detection of depression, anxiety and fatigue and therefore can improve HNC aftercare.
Purpose
Head and neck cancer (HNC) and its treatment can leave devastating side effects with a relevant impact on physical and emotional quality of life (QoL) of HNC patients. The objectives were to examine the amount of dysphagia, voice problems, and pain in HNC patients, the impact of sociodemographic, behavioral, and clinical factors on these symptoms, the psychometric properties of the EAT-10, and the relationship between these symptoms and QoL variables.
Methods
HNC patients attending for regular follow-up from 07/2013 to 09/2019 completed questionnaires (Eating Assessment Tool-10 (EAT-10); questions from the EORTC QLQ-C30 and EORTC H&N35) on dysphagia, voice problems, pain, fatigue, and QoL collected with the software OncoFunction. Associations between prognostic factors and symptoms were tested with analyses of variance (ANOVAs). Associations between the symptom scales and QoL variables were expressed with Pearson correlations.
Results
Of 689 patients, 54.9% suffered from dysphagia, the EAT-10 proved to be a reliable measure. The mean voice score was 37.6 (± 33.9) [range 0–100], the mean pain score 1.98 (± 2.24) [range 0–10]. Trimodality treatment was associated with the highest dysphagia scores. Dysphagia, voice problems, and pain significantly correlated with each other, the highest association was found for dysphagia and pain (r = 0.51). QoL was strongly correlated with dysphagia and pain (r = − 0.39 and r = − 0.40, respectively), while the association with voice problems was weaker (r = − 0.28).
Conclusion
Dysphagia is an important symptom in HNC patients greatly affecting patients’ QoL and significantly correlating with voice problems and pain.
The direct closure procedure is quick, simple and can be performed without secondary donor site morbidity. For wound healing, cosmetic and function of the forearm and hand, no inferior results can be measured for the direct procedure compared to the indirect coverage technique.
Purpose
In the context of aviation and automotive navigation technology, assistance functions are associated with predictive planning and wayfinding tasks. In endoscopic minimally invasive surgery, however, assistance so far relies primarily on image-based localization and classification. We show that navigation workflows can be described and used for the prediction of navigation steps.
Methods
A natural description vocabulary for observable anatomical landmarks in endoscopic images was defined to create 3850 navigation workflow sentences from 22 annotated functional endoscopic sinus surgery (FESS) recordings. Resulting FESS navigation workflows showed an imbalanced data distribution with over-represented landmarks in the ethmoidal sinus. A transformer model was trained to predict navigation sentences in sequence-to-sequence tasks. The training was performed with the Adam optimizer and label smoothing in a leave-one-out cross-validation study. The sentences were generated using an adapted beam search algorithm with exponential decay beam rescoring. The transformer model was compared to a standard encoder-decoder-model, as well as HMM and LSTM baseline models.
Results
The transformer model reached the highest prediction accuracy for navigation steps at 0.53, followed by 0.35 of the LSTM and 0.32 for the standard encoder-decoder-network. With an accuracy of sentence generation of 0.83, the prediction of navigation steps at sentence-level benefits from the additional semantic information. While standard class representation predictions suffer from an imbalanced data distribution, the attention mechanism also considered underrepresented classes reasonably well.
Conclusion
We implemented a natural language-based prediction method for sentence-level navigation steps in endoscopic surgery. The sentence-level prediction method showed a potential that word relations to navigation tasks can be learned and used for predicting future steps. Further studies are needed to investigate the functionality of path prediction. The prediction approach is a first step in the field of visuo-linguistic navigation assistance for endoscopic minimally invasive surgery.
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