Background: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer variability are main issues in manual OAR delineation, mainly in the head and neck (H & N) district. Deep-learning based auto-segmentation is a promising strategy to improve OARs contouring in radiotherapy departments. A comparison of deep-learning-generated auto-contours (AC) with manual contours (MC) was performed by three expert radiation oncologists from a single center. Methods: Planning computed tomography (CT) scans of patients undergoing RT treatments for H&N cancers were considered. CT scans were processed by Limbus Contour auto-segmentation software, a commercial deep-learning auto-segmentation based software to generate AC. H&N protocol was used to perform AC, with the structure set consisting of bilateral brachial plexus, brain, brainstem, bilateral cochlea, pharyngeal constrictors, eye globes, bilateral lens, mandible, optic chiasm, bilateral optic nerves, oral cavity, bilateral parotids, spinal cord, bilateral submandibular glands, lips and thyroid. Manual revision of OARs was performed according to international consensus guidelines. The AC and MC were compared using the Dice similarity coefficient (DSC) and 95% Hausdorff distance transform (DT). Results: A total of 274 contours obtained by processing CT scans were included in the analysis. The highest values of DSC were obtained for the brain (DSC 1.00), left and right eye globes and the mandible (DSC 0.98). The structures with greater MC editing were optic chiasm, optic nerves and cochleae. Conclusions: In this preliminary analysis, deep-learning auto-segmentation seems to provide acceptable H&N OAR delineations. For less accurate organs, AC could be considered a starting point for review and manual adjustment. Our results suggest that AC could become a useful time-saving tool to optimize workload and resources in RT departments. Full article

Objective: Tumours of the nasal septum are a rare and heterogeneous group of lesions in the sinonasal tract. The management of the different lesions of this site is debated. The aim of this study is to share our experience on a rare clinical condition and stimulate other centres to publish theirs. Methods: We retrospectively analysed the databases of sinonasal tumours treated at the Sections of Otolaryngology (ENT) of two University Hospitals (Palermo and Genova) between 2012 and 2020. Results: From the two databases, a cohort of 32 patients with tumours of nasal septum were selected. All patients underwent an endoscopic examination. Large tumours underwent preoperative computed tomography (CT) scan without contrast medium. In 22 (68.7%) cases, the preoperative radiologic evaluation also included magnetic resonance imaging (MRI) with gadolinium to obtain a better differentiation of the lesions and study the vascular pattern. All the large lesions were biopsied under endoscopic guidance using local anaesthesia; the same approach was used to remove the tumours and their attachment with safe resection margins. Conclusions: While malignant lesions require an excision of the mass with resection of all layers of the nasal septum, benign lesions must be typed according to histological considerations in order to plan the most appropriate type of surgical resection. Full article