Prevalence of neurocognitive and perceived speech deficits in patients with head and neck cancer before treatment:Associations with demographic, behavioral, and disease-related factors

BACKGROUND: Neurocognition and speech, relevant domains in head and neck cancer (HNC), may be affected pretreatment. However, the prevalence of pretreatment deficits and their possible concurrent predictors are poorly understood.METHODS: Using an HNC prospective cohort (Netherlands Quality of Life and Biomedical Cohort Study, N ≥ 444) with a cross-sectional design, we investigated the estimated prevalence of pretreatment deficits and their relationship with selected demographic, behavioral, and disease-related factors.RESULTS: Using objective assessments, rates of moderate-to-severe neurocognitive deficit ranged between 4% and 8%. From patient-reported outcomes, 6.5% of patients reported high levels of cognitive failures and 46.1% reported speech deficits. Patient-reported speech functioning was worse in larynx compared to other subsites. Other nonspeech outcomes were unrelated to any variable. Patient-reported neurocognitive and speech functioning were modestly correlated, especially in the larynx group.CONCLUSIONS: These findings indicate that a subgroup of patients with HNC shows pretreatment deficits, possibly accentuated in the case of larynx tumors.</p

Assessing the prognostic value of tumor-infiltrating CD57+ cells in advanced stage head and neck cancer using QuPath digital image analysis

This study aimed to assess the prognostic value of intratumoral CD57+ cells in head and neck squamous cell carcinoma (HNSCC) and to examine the reproducibility of these analyses using QuPath. Pretreatment biopsies of 159 patients with HPV-negative, stage III/IV HNSCC treated with chemoradiotherapy were immunohistochemically stained for CD57. The number of CD57+ cells per mm2 tumor epithelium was quantified by two independent observers and by QuPath, software for digital pathology image analysis. Concordance between the observers and QuPath was assessed by intraclass correlation coefficients (ICC). The correlation between CD57 and clinicopathological characteristics was assessed; associations with clinical outcome were estimated using Cox proportional hazard analysis and visualized using Kaplan-Meier curves. The patient cohort had a 3-year OS of 65.8% with a median follow-up of 54 months. The number of CD57+ cells/mm2 tumor tissue did not correlate to OS, DFS, or LRC. N stage predicted prognosis (OS: HR 0.43, p = 0.008; DFS: HR 0.41, p = 0.003; LRC: HR 0.24, p = 0.007), as did WHO performance state (OS: HR 0.48, p = 0.028; LRC: 0.33, p = 0.039). Quantification by QuPath showed moderate to good concordance with two human observers (ICCs 0.836, CI 0.805–0.863, and 0.741, CI 0.692–0.783, respectively). In conclusion, the presence of CD57+ TILs did not correlate to prognosis in advanced stage, HPV-negative HNSCC patients treated with chemoradiotherapy. Substantial concordance between human observers and QuPath was found, confirming a promising future role for digital, algorithm driven image analysis

Sleep quality trajectories from head and neck cancer diagnosis to six months after treatment

Objectives: Patients with head and neck cancer (HNC) often report disturbances in their sleep quality, impairing their quality of life. This study aims to examine the trajectories of sleep quality from diagnosis up to 6-month after treatment, as well as the pre-treatment risk factors for poor sleep trajectories. Materials and Methods: Sleep quality (Pittsburgh sleep quality index) was measured shortly after diagnosis (pre-treatment), and at 3 and 6 months after finishing treatment. Patients were categorized into 5 trajectory groups. We examined the association of sleep quality trajectories with sociodemographic and clinical characteristics, coping style, HNC symptoms, and psychological distress. Results: Among 412 included patients, about a half either had a persistent good sleep (37.6%) or an improving (16.5%) trajectory. About a third had a persistent poor sleep (21.8%) or worsening (10.9%) sleep trajectory. The remaining patients (13.1%), alternated between good and poor sleep. Using persistent good sleep as a reference outcome, persistent poor sleepers were more likely to be woman (odds ratio [OR] = 1.98, 95% confidence interval [CI] 1.01–3.90), use painkillers prior to treatment (OR = 2.52, 95% CI 1.33–4.77), and have more pre-treatment anxiety symptoms (OR = 1.26, 95% CI 1.15–1.38). Conclusion: Unfavorable sleep quality trajectories are prevalent among HNC patients from pre-treatment to 6-month after treatment. A periodic sleep evaluation starting shortly after HNC diagnosis is necessary to identify persistent sleep problems, especially among high-risk group

Poor sleep quality among newly diagnosed head and neck cancer patients:prevalence and associated factors

BACKGROUND: Head and neck cancer (HNC) patients often suffer from distress attributed to their cancer diagnosis which may disturb their sleep. However, there is lack of research about poor sleep quality among newly diagnosed HNC patients. Therefore, our aim was to investigate the prevalence and the associated factors of poor sleep quality among HNC patients before starting treatment. MATERIALS AND METHODS: A cross-sectional study was conducted using the baseline data from NET-QUBIC study, an ongoing multi-center cohort of HNC patients in the Netherlands. Poor sleep quality was defined as a Pittsburgh Sleep Quality Index (PSQI) total score of > 5. Risk factors examined were sociodemographic factors (age, sex, education level, living situation), clinical characteristics (HNC subsite, tumor stage, comorbidity, performance status), lifestyle factors, coping styles, and HNC symptoms. RESULTS: Among 560 HNC patients, 246 (44%) had poor sleep quality before start of treatment. Several factors were found to be significantly associated with poor sleep: younger age (odds ratio [OR] for each additional year 0.98, 95% CI 0.96-1.00), being female (OR 2.6, 95% CI 1.7-4.1), higher passive coping style (OR 1.18, 95% CI 1.09-1.28), more oral pain (OR 1.10, 95% CI 1.01-1.19), and less sexual interest and enjoyment (OR 1.13, 95% CI 1.06-1.20). CONCLUSION: Poor sleep quality is highly prevalent among HNC patients before start of treatment. Early evaluation and tailored intervention to improve sleep quality are necessary to prepare these patients for HNC treatment and its consequences

Psychoneurological Symptoms and Biomarkers of Stress and Inflammation in Newly Diagnosed Head and Neck Cancer Patients:A Network Analysis

Psychoneurological symptoms are commonly reported by newly diagnosed head and neck cancer (HNC) patients, yet there is limited research on the associations of these symptoms with biomarkers of stress and inflammation. In this article, pre-treatment data of a multi-center cohort of HNC patients were analyzed using a network analysis to examine connections between symptoms (poor sleep quality, anxiety, depression, fatigue, and oral pain), biomarkers of stress (diurnal cortisol slope), inflammation markers (c-reactive protein [CRP], interleukin [IL]-6, IL-10, and tumor necrosis factor alpha [TNF-α]), and covariates (age and body mass index [BMI]). Three centrality indices were calculated: degree (number of connections), closeness (proximity of a variable to other variables), and betweenness (based on the number of times a variable is located on the shortest path between any pair of other variables). In a sample of 264 patients, poor sleep quality and fatigue had the highest degree index; fatigue and CRP had the highest closeness index; and IL-6 had the highest betweenness index. The model yielded two clusters: a symptoms—cortisol slope—CRP cluster and a IL-6—IL-10—TNF-α—age—BMI cluster. Both clusters were connected most prominently via IL-6. Our findings provide evidence that poor sleep quality, fatigue, CRP, and IL-6 play an important role in the interconnections between psychoneurological symptoms and biomarkers of stress and inflammation in newly diagnosed HNC patients

Psychological Problems among Head and Neck Cancer Patients in Relation to Utilization of Healthcare and Informal Care and Costs in the First Two Years after Diagnosis

BACKGROUND: To investigate associations between psychological problems and the use of healthcare and informal care and total costs among head and neck cancer (HNC) patients. METHOD: Data were used of the NETherlands QUality of Life and Biomedical Cohort study. Anxiety and depression disorder (diagnostic interview), distress, symptoms of anxiety and depression (HADS), and fear of cancer recurrence (FCR) and cancer worry scale (CWS) were measured at baseline and at 12-month follow-up. Care use and costs (questionnaire) were measured at baseline, 3-, 6-, 12-, and 24-month follow-up. Associations between psychological problems and care use/costs were investigated using logistic and multiple regression analyses. RESULTS: Data of 558 patients were used. Distress, symptoms of anxiety or depression, FCR, and/or anxiety disorder at baseline were significantly associated with higher use of primary care, supportive care, and/or informal care (odds ratios (ORs) between 1.55 and 4.76). Symptoms of anxiety, FCR, and/or depression disorder at 12-month follow-up were significantly associated with use of primary care, supportive care, and/or informal care (ORs between 1.74 and 6.42). Distress, symptoms of anxiety, and FCR at baseline were associated with higher total costs. DISCUSSION: HNC patients with psychological problems make more use of healthcare and informal care and have higher costs. This is not the result of worse clinical outcomes

Prospective longitudinal study on fear of cancer recurrence in patients newly diagnosed with head and neck cancer:Course, trajectories, and associated factors

Background: This study assessed the course of fear of cancer recurrence (FCR) in patients newly diagnosed with head and neck cancer (HNC), identified FCR trajectories and factors associated with FCR trajectories. Methods: Six hundred and seventeen HNC patients from the NET-QUBIC cohort study completed the Cancer Worry Scale-6 at diagnosis, 3 and 6 months post-treatment. FCR trajectories were identified using Latent Class Growth Analysis. Associations were explored between FCR trajectories and baseline demographic and medical variables, coping and self-efficacy. Results: Overall, FCR decreased slightly between baseline and 3 months post-treatment and remained stable up to 6 months. Two FCR trajectories were identified: “high stable” (n = 125) and “low declining” (n = 492). Patients with high stable FCR were younger, reported more negative adjustment, passive coping, and reassuring thoughts, and less avoidance. Conclusions: The majority of HNC patients have low declining FCR after diagnosis, but one in five patients experience persistent high FCR up to 6 months post-treatment

Accurate detection of circulating tumor DNA using nanopore consensus sequencing

Levels of circulating tumor DNA (ctDNA) in liquid biopsies may serve as a sensitive biomarker for real-time, minimally-invasive tumor diagnostics and monitoring. However, detecting ctDNA is challenging, as much fewer than 5% of the cell-free DNA in the blood typically originates from the tumor. To detect lowly abundant ctDNA molecules based on somatic variants, extremely sensitive sequencing methods are required. Here, we describe a new technique, CyclomicsSeq, which is based on Oxford Nanopore sequencing of concatenated copies of a single DNA molecule. Consensus calling of the DNA copies increased the base-calling accuracy ~60×, enabling accurate detection of TP53 mutations at frequencies down to 0.02%. We demonstrate that a TP53-specific CyclomicsSeq assay can be successfully used to monitor tumor burden during treatment for head-and-neck cancer patients. CyclomicsSeq can be applied to any genomic locus and offers an accurate diagnostic liquid biopsy approach that can be implemented in clinical workflows

Author Correction: Accurate detection of circulating tumor DNA using nanopore consensus sequencing

The Data Availability statement in the original version of the paper reads: “The sequencing datasets generated during the current study are available upon request at EGA, under accession number EGAS00001003759”. However, as this data upload was not successful, the authors reuploaded the data under a different accession number and have amended the Data Availability statement to read “The sequencing datasets generated during the current study are available upon request at EGA, under accession number EGAS00001007090”. The original article has been corrected.</p