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Objective: Timely treatment initiation in head and neck cancer (HNC) care is of great importance regarding survival, oncological, functional, and psychological outcomes. Therefore, waiting times are assessed in the Dutch Head and Neck Audit (DHNA). This audit aims to assess and improve the quality of care through feedback and benchmarking. For this study, we examined how waiting times evolved since the start of the DHNA. Study Design: Prospective cohort study. Setting: National multicentre study. Methods: The DHNA was established in 2014 and reached national coverage of all patients treated for primary HNC in 2019. DHNA data on curative patients from 2015 to 2021 was extracted on national (benchmark) and hospital level. We determined 3 measures for waiting time: (1) the care pathway interval (CPI, first visit to start treatment), (2) the time to treatment interval (TTI, biopsy to start treatment), and (3) CPI‐/TTI‐indicators (percentage of patients starting treatment ≤30 days). The Dutch national quality norm for the CPI‐indicator is 80%. Results: The benchmark median CPI and TTI improved between 2015 and 2021 from 37 to 26 days and 37 to 33 days, respectively. Correspondingly, the CPI‐ and TTI‐indicators, respectively, increased from 39% to 64% and 35% to 40% in 2015 to 2021. Outcomes for all hospitals improved and dispersion between hospitals declined. Four hospitals exceeded the 80% quality norm in 2021. Conclusion: Waiting times improved gradually over time, with 4 hospitals exceeding the quality standard in 2021. On the hospital‐level, process improvement plans have been initiated. Systematic registration, auditing, and feedback of data support the improvement of quality of care. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Patients with limited distant metastatic disease, also known as oligometastasis, show better survival rates than polymetastatic patients, and may be amenable for curative‐intent treatment. The definition of oligometastasis, however, is unknown, and no quantitative analyses on the cutoff value for oligometastasis have been performed before. This study aims to derive specific threshold values for the number of metastases and affected locations that defines oligometastatic disease in head and neck squamous cell carcinoma.A retrospective cohort study was conducted including all patients diagnosed with distant metastases between 2006 and 2021. For each patient, the number of distant metastases and affected locations was recorded on the basis of the available imaging at the time of diagnosis. Cox regression analyses and a machine‐learning k‐means algorithm were used to determine threshold values.A total of 384 patients untreated for their metastatic foci were analyzed. Most patients (n = 207; 53.9%) had metastasis to one anatomic location, followed by metastases in two anatomic locations (n = 62; 16.1%). The majority of patients had ≥9 metastatic foci (n = 174; 45.3%), followed by one focus (n = 74; 19.3%) and two foci (n = 32; 8.3%). Cox regression and machine‐learning k‐means models showed that although the number of metastases did not predict survival, the number of affected locations did significantly (p < .001), by identifying a threshold of two locations.Contrary to the prevalent dogma, the definition of oligometastasis should not be defined by the number of metastases but rather by the number of affected locations, with a maximum number of affected locations set at two. [ABSTRACT FROM AUTHOR]

Importance: Prognostication is an important aspect of clinical decision-making, but it is often challenging. Previous studies show that both patients and physicians tend to overestimate survival chances. Prediction models may assist in estimating and quantifying prognosis. However, insufficient understanding of the development, possibilities, and limitations of such models can lead to misinterpretations. Although many excellent books and comprehensive methodological articles on prognostic model research are published, they may not be accessible enough for the clinical audience. Our aim is to provide an overview on the main issues regarding prediction research for health care professionals to achieve better interpretation and increase the use of prognostic models in daily clinical practice., Observations: The first steps of model development include coding of predictors, model specification, and estimation. Next, we discuss the assessment of the performance of a prediction model, including discrimination and calibration aspects, followed by approaches to internal and external validation and updating. Finally, model reporting, presentation, and steps toward clinical implementation are presented., Conclusions and Relevance: After thorough consideration of the research question, data inspection, and coding of predictors, one can start with the specification of a prediction model. The number of candidate predictors should be kept limited, in view of the number of events in the data, to prevent overfitting. Calibration and discrimination are 2 aspects of model performance that complement each other and should be assessed preferably at external validation. Model development should be accompanied by qualitative research among patients and physicians to facilitate the development of a valuable tool and maximize possibilities for successful implementation. After model presentation is optimized, impact studies are required to assess the clinical value of a prediction model.

We evaluated the radiation outcome and prognostic factors in a population-based study of early (T1N0M0) glottic carcinoma. Survival parameters and prognostic factors were evaluated by uni- and multivariate analysis in 316 consecutive irradiated patients with T1 glottic carcinoma in the Comprehensive Cancer Center West region of the western Netherlands. Median follow-up was 70 months (range 1-190 months). Five and ten-year local control was 86 and 84%. Disease specific survival was 97% at 5 and 10 years. In multivariate analysis, pre-existent laryngeal hypertrophic laryngitis was the only predictive factor for local control (relative risk = 3.0, P = 0.02). Comorbidity was prognostic for overall survival. No factor was predictive for disease specific survival. Pre-existent laryngeal hypertrophic laryngitis is a new risk factor associated with reduced local control in T1 glottic carcinoma treated with radiotherapy.