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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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Biological tumour volume (GTV PET ) delineation on 18 F-FDG PET acquired during induction chemotherapy (ICT) is challenging due to the reduced metabolic uptake and volume of the GTV PET . Automatic segmentation algorithms applied to 18 F-FDG PET (PET-AS) imaging have been used for GTV PET delineation on 18 F-FDG PET imaging acquired before ICT. However, their role has not been investigated in 18 F-FDG PET imaging acquired after ICT. In this study we investigate PET-AS techniques, including ATLAAS a machine learned method, for accurate delineation of the GTV PET after ICT. Twenty patients were enrolled onto a prospective phase I study (FiGaRO). PET/CT imaging was acquired at baseline and 3 weeks following 1 cycle of induction chemotherapy. The GTV PET was manually delineated by a nuclear medicine physician and clinical oncologist. The resulting GTV PET was used as the reference contour. The ATLAAS original statistical model was expanded to include images of reduced metabolic activity and the ATLAAS algorithm was re-trained on the new reference dataset. Estimated GTV PET contours were derived using sixteen PET-AS methods and compared to the GTV PET using the Dice Similarity Coefficient (DSC). The mean DSC for ATLAAS, 60% Peak Thresholding (PT60), Adaptive Thresholding (AT) and Watershed Thresholding (WT) was 0.72, 0.61, 0.63 and 0.60 respectively. The GTV PET generated by ATLAAS compared favourably with manually delineated volumes and in comparison, to other PET-AS methods, was more accurate for GTV PET delineation after ICT. ATLAAS would be a feasible method to reduce inter-observer variability in multi-centre trials., (Copyright © 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Purpose: To evaluate the feasibility and impact of using a novel advanced PET auto-segmentation method in Head and Neck (H&N) radiotherapy treatment (RT) planning., Methods: ATLAAS, Automatic decision Tree-based Learning Algorithm for Advanced Segmentation, previously developed and validated on pre-clinical data, was applied to 18 F-FDG-PET/CT scans of 20 H&N patients undergoing Intensity Modulated Radiation Therapy. Primary Gross Tumour Volumes (GTVs) manually delineated on CT/MRI scans (GTVp CT/MRI ), together with ATLAAS-generated contours (GTVp ATLAAS ) were used to derive the RT planning GTV (GTVp final ). ATLAAS outlines were compared to CT/MRI and final GTVs qualitatively and quantitatively using a conformity metric., Results: The ATLAAS contours were found to be reliable and useful. The volume of GTVp ATLAAS was smaller than GTVp CT/MRI in 70% of the cases, with an average conformity index of 0.70. The information provided by ATLAAS was used to grow the GTVp CT/MRI in 10 cases (up to 10.6mL) and to shrink the GTVp CT/MRI in 7 cases (up to 12.3mL). ATLAAS provided complementary information to CT/MRI and GTVp ATLAAS contributed to up to 33% of the final GTV volume across the patient cohort., Conclusions: ATLAAS can deliver operator independent PET segmentation to augment clinical outlining using CT and MRI and could have utility in future clinical studies., (Copyright © 2017 Elsevier B.V. All rights reserved.)