Population-based studies on early mortality in head and neck cancer (HNC) are sparse. This retrospective population-based study investigated early mortality of HNC and the influence of patients’ tumor and treatment characteristics. All 8288 patients with primary HNC of the German federal state Thuringia from 1996 to 2016 were included. Univariate and multivariate analysis were performed to identify independent factors for 30-day, 90-day, and 180-day mortality. The 30-, 90-, and 180-day mortality risks were 1.8%, 5.1%, and 9.6%, respectively. In multivariable analysis, male sex (odds ratio (OR) 1.41; 95% confidence interval (CI) 1.08–1.84), increasing age (OR 1.81; CI 1.49–2.19), higher T (T4: OR 3.09; CI 1.96–4.88) and M1 classification (OR 1.97; CI 1.43–2.73), advanced stage (IV: OR 3.97; CI 1.97–8.00), tumors of the cavity of mouth (OR 3.47; CI 1.23–9.75), oropharynx (OR 3.01; CI 1.06–8.51), and hypopharynx (OR 3.27; CI 1.14–9.40) had a significantly greater 180-day mortality. Surgery (OR 0.51; CI 0.36–0.73), radiotherapy (OR 0.37; CI 0.25–0.53), and multimodal therapy (OR 0.10; CI 0.07–0.13) were associated with decreased 180-day mortality. Typical factors associated with worse overall survival had the most important impact on early mortality in a population-based setting.

Background: Accurate and rapid intraoperative tumor margin assessment remains a major challenge in surgical oncology. Current gold-standard methods, such as frozen section histology, are time-consuming, operator-dependent, and prone to misclassification, which limits their clinical utility. Objective: To develop and evaluate a novel hyperspectral imaging (HSI) workflow that integrates deep learning with three-dimensional (3D) tumor modeling for real-time, label-free tumor margin delineation in head and neck squamous cell carcinoma (HNSCC). Methods: Freshly resected HNSCC samples were snap-frozen and imaged ex vivo from multiple perspectives using a standardized HSI protocol, resulting in a 3D model derived from HSI. Each sample was serially sectioned, stained, and annotated by pathologists to create high-resolution 3D histological reconstructions. The volumetric histological models were co-registered with the HSI data (n = 712 Datacubes), enabling voxel-wise projection of tumor segmentation maps from the HSI-derived 3D model onto the corresponding histological ground truth. Three deep learning models were trained and validated on these datasets to differentiate tumor from non-tumor regions with high spatial precision. Results: This work demonstrates strong potential for the proposed HSI system, with an overall classification accuracy of 0.98 and a tumor sensitivity of 0.93, underscoring the system’s ability to reliably detect tumor regions and showing high concordance with histopathological findings. Conclusion: The integration of HSI with deep learning and 3D tumor modeling offers a promising approach for precise, real-time intraoperative tumor margin assessment in HNSCC. This novel workflow has the potential to improve surgical precision and patient outcomes by providing rapid, label-free tissue differentiation.

This population-based study investigated the prognostic role of intraparotid (PAR) and cervical lymph node (LN) metastasis on overall survival (OS) of primary parotid cancer. All 345 patients (median age: 66 years; 43% female, 49% N+, 31% stage IV) of the Thuringian cancer registries with parotid cancer from 1996 to 2016 were included. OS was assessed in relation to the total number of removed PAR and cervical LN, number of positive intraparotid (PAR+), positive cervical LN, LN ratio, log odds of positive LN (LODDS), as well as including the PAR as LODDS-PAR. PAR was assessed in 42% of the patients (22% of these PAR+). T and N classification were not independent predictors of OS. When combining T with LODDS instead of N, higher T (T3/T4) became a prognosticator (hazard ratio (HR) = 2.588; CI = 1.329–5.040; p = 0.005) but not LODDS (p > 0.05). When combining T classification with LODDS-PAR, both higher T classification (HR = 2.256; CI = 1.288–3.950; p = 0.004) and the alternative classification with LODDS-PAR (≥median −1.11; HR 2.078; CI = 1.155–3.739; p = 0.015) became independent predictors of worse OS. LODDS-PAR was the only independent prognosticator out of the LN assessment for primary parotid cancer.

Investigations on the association between patient’s age and head and neck cancer (HNC) treatment decision are sparse. Nationwide diagnoses-related group-based data of 1,226,357 cases hospitalized with primary HNC in Germany from 2005 to 2018 were included. Negative binomial regression was performed to study the development of the treatment rates over time. For all treatment options, i.e., biopsies, surgery, radiotherapy, and chemotherapy/biologicals, increases in the treatment rates were seen in patients >80 years (surgery: oral cavity: relative risk [RR]: 1.2, CI: 1.13–1.20; oropharynx: RR: 1.2, CI: 1.15–1.34; hypopharynx: RR: 1.1, CI: 1.02–1.17; larynx: RR: 1.1, CI: 1.04–1.12; radiotherapy: oral cavity: RR: 1.1, CI: 1.07–1.23; oropharynx: RR: 1.3, CI: 1.16–1.49; hypopharynx: RR: 1.3, CI: 1.21–1.46; larynx: RR 1.2, CI: 1.03–1.29; chemotherapy: oral cavity: RR: 1.2, CI: 1.06–1.31; salivary glands: RR: 1.3, CI: 1.09–1.50; oropharynx: RR: 1.4, CI: 1.12–1.83; hypopharynx: RR: 1.3, CI: 1.06–1.48; larynx: RR: 1.3, CI: 1.08–1.52, all p < 0.05). Older age cohorts (≥80 years) need more awareness as they are mainly responsible for the increase in the rates of surgery, radiotherapy, and chemotherapy/biologics in HNC patients.

Background: In vivo visualization of malignant tumors remains the main challenge during head and neck cancer surgery. This can result in inadequate tumor margin assessment and incomplete tumor resection, adversely affecting patient outcomes. Hyperspectral imaging (HSI) is a promising approach to address this issue. However, its application in surgery has been limited by the lack of medically approved HSI devices compliant with MDR regulations, as well as challenges regarding the integration into the surgical workflow. Methods: In this feasibility study, we employed endoscopic HSI during surgery to visualize the tumor sites of 12 head and neck cancer patients. We optimized the HSI workflow to minimize time required during surgery and to reduce the adaptation period needed for surgeons to adjust to the new workflow. Additionally, we implemented data processing to enable real-time classification and visualization of HSI within the intraoperative setting. HSI evaluation was conducted using principal component analysis and k-means clustering, with this clustering validated through comparison with expert annotations. Results: Our complete HSI workflow requires two to three minutes, with each HSI measurement—including evaluation and visualization—taking less than 10 s, achieving an accuracy of 79%, sensitivity of 72%, and specificity of 84%. Medical personnel became proficient with the HSI system after two surgeries. Conclusions: This study presents an HSI workflow for in vivo tissue differentiation during head and neck cancer surgery, providing accurate and visually accessible results within minimal time. This approach enhances the in vivo evaluation of tumor margins, leading to more clear margins and, consequently, improved patient outcomes.

Background: This population-based study investigated the influence of different lymph node (LN) classifications on overall survival (OS) in head and neck cancer (HNC). Methods: 401 patients (median age: 57 years; 47% stage IV) of the Thuringian cancer registries with diagnosis of a primary HNC receiving a neck dissection (ND) in 2009 and 2010 were included. OS was assessed in relation to total number of LN removed, number of positive LN, LN ratio, and log odds of positive LN (LODDS). Results: Mean number of LODDS was 0–0.96 ± 0.57. When limiting the multivariate analysis to TNM stage, only the UICC staging (stage IV: HR 9.218; 95% CI 2.721–31.224; p < 0.001) and LODDS > – 1.0 (HR 2.120; 95% CI 1.129–3.982; p = 0.019) were independently associated with lower OS. Conclusion: LODDS was an independent and superior predictor for OS in HNC in a population-based setting with representative real-life data.