High-grade neuroendocrine head and neck cancer: Case series and review of the literature.

BACKGROUND: High-grade neuroendocrine cancers (NEC) of the head and neck (HN) are rare and aggressive, accounting for ≤1 % of all HN cancers, with a 5-year overall survival (OS) of ≤20 %. This case series examines clinical characteristics, treatments, and outcomes of patients diagnosed at a regional UK HN cancer centre over the last 23 years. METHODS: A retrospective review of medical records was conducted for all patients diagnosed with NEC HN from 1st January 2000 until 1st March 2023 at Velindre Cancer Centre. RESULTS: During the study period, 19 cases of NEC HN were identified, primarily affecting males (n = 15, 79 %). Median age of 67 years (range: 44-86). At diagnosis, 32 % of patients (n = 6) were smokers. The most common primary tumour sites were larynx (n = 5, 26.3 %) and sinonasal (n = 5, 26.3 %). Most patients presented with advanced loco-regional disease or distant metastasis, with stage IVA (n = 6, 32 %) and stage IVC (n = 6, 32 %) being the most common. The key pathology marker was synaptophysin, present in 100 % of the tested patients (n = 15). In the study, of the 12 patients with non-metastatic disease, 10 received a combination of treatments that included radiotherapy (RT). Some of these patients also received chemotherapy (CT) at the same time as their radiotherapy. Surgery alone was used in two patients with stage II disease. Seven subjects had complete responses, and one achieved a partial response. Among the seven metastatic patients, three received CT, and one underwent palliative RT, all achieving a partial response. In all cases, the CT used was carboplatin and etoposide. After a median follow-up of 11 months (range: 1-96), the median OS was 27 months for the overall population, 51 months for those treated radically, and three months for metastatic patients with palliative treatment. The 1-year OS for all patients was 54.3 %, the 2-year OS was 46.5 %, and the 5-year OS was 23.3 %. Among patients treated radically, these rates were 65.3 %, 52.2 %, and 26.1 %, respectively. For patients treated palliatively, the 1-year OS was 33.3 %. CONCLUSION: This case series contributes preliminary observations on the characteristics and management of non-metastatic NEC HN, suggesting potential benefits from multimodality treatment strategies. Given the small cohort size, these observations should be interpreted cautiously and seen as a foundation for further research.

Novel dosimetric validation of a commercial CT scanner based deep learning automated contour solution for prostate radiotherapy.

PURPOSE: OAR delineation accuracy influences: (i) a patient's optimised dose distribution (PD), (ii) the reported doses (RD) presented at approval, which represent plan quality. This study utilised a novel dosimetric validation methodology, comprehensively evaluating a new CT-scanner-based AI contouring solution in terms of PD and RD within an automated planning workflow. METHODS: 20 prostate patients were selected to evaluate AI contouring for rectum, bladder, and proximal femurs. Five planning 'pipelines' were considered; three using AI contours with differing levels of manual editing (nominally none (AIStd), minor editing in specific regions (AIMinEd), and fully corrected (AIFullEd)). Remaining pipelines were manual delineations from two observers (MDOb1, MDOb2). Automated radiotherapy plans were generated for each pipeline. Geometric and dosimetric agreement of contour sets AIStd, AIMinEd, AIFullEd and MDOb2 were evaluated against the reference set MDOb1. Non-inferiority of AI pipelines was assessed, hypothesising that compared to MDOb1, absolute deviations in metrics for AI contouring were no greater than that from MDOb2. RESULTS: Compared to MDOb1, organ delineation time was reduced by 24.9 min (96 %), 21.4 min (79 %) and 12.2 min (45 %) for AIStd, AIMinEd and AIFullEd respectively. All pipelines exhibited generally good dosimetric agreement with MDOb1. For RD, median deviations were within ± 1.8 cm3, ± 1.7 % and ± 0.6 Gy for absolute volume, relative volume and mean dose metrics respectively. For PD, respective values were within ± 0.4 cm3, ± 0.5 % and ± 0.2 Gy. Statistically (p < 0.05), AIMinEd and AIFullEd were dosimetrically non-inferior to MDOb2. CONCLUSIONS: This novel dosimetric validation demonstrated that following targeted minor editing (AIMinEd), AI contours were dosimetrically non-inferior to manual delineations, reducing delineation time by 79 %.

FIELDRT: an open-source platform for the assessment of target volume delineation in radiation therapy.

OBJECTIVES: Target volume delineation (TVD) has been identified as a weakness in the accuracy of radiotherapy, both within and outside of clinical trials due to the intra/interobserver variations affecting the TVD quality. Sources of variations such as poor compliance or protocol violation may have adverse effect on treatment outcomes. In this paper, we present and describe the FIELDRT software developed for the ARENA project to improve the quality of TVD through qualitative and quantitative feedbacks and individual and personalized summary of trainee"s performance. METHODS: For each site-specific clinical case included in the FIELDRT software, reference volumes, minimum and maximum "acceptable" volumes and organ at risk were derived by outlines of consultants and senior trainees. The software components currently developed include: (a) user-friendly importing interface (b) analysis toolbox to compute quantitative and qualitative (c) visualiser and (d) structured report generator for personalised feedback. The FIELDRT software was validated by comparing the performance of 63 trainees and by measuring performance over time. In addition, a trainee evaluation day was held in 2019 to collect feedback on FIELDRT. RESULTS: Results show the trainees' improvement when reoutlining a case after reviewing the feedback generated from the FIELDRT software. Comments and feedback received after evaluation day were positive and confirmed that FIELDRT can be a useful application for training purposes. CONCLUSION: We presented a new open-source software to support education in TVD and ongoing continuous professional development for clinical oncology trainees and consultants. ARENA in combination with FIELDRT implements site-specific modules with reference target and organs at risk volumes and automatically evaluates individual performance using several quantitative and qualitative feedbacks. Pilot results suggests this software could be used as an education tool to reduce variation in TVD so to guarantee high quality in radiotherapy. ADVANCES IN KNOWLEDGE: FIELDRT is a new easy and free to use software aiming at supporting education in TVD and ongoing continuous professional development. The software provides quantitative/qualitative feedback and an exportable report with an individual and personalised summary of trainee's performance.