RationaleThe European Position Paper on Sinusitis (EPOS) guidelines provide composite criteria to evaluate chronic rhinosinusitis (CRS) control, taking into consideration the severity of patients’ symptoms, aspect of nasal mucosa and medical intake as parameters of CRS control.ObjectivesTo study the degree of CRS control using novel EPOS control criteria at 3–5 years after a functional endoscopic sinus surgery (FESS) and correlate these data to symptoms scores.MethodsAdult CRS patients (n = 560) who had undergone bilateral FESS for chronic inflammatory sinonasal disease 3–5 years prior to the study were included. Patients received a postal questionnaire asking for control items according to EPOS control criteria, visual analogue scale (VAS) scores for total and individual sinonasal symptoms, sinonasal outcome test (SNOT)‐22 and Short Form (SF)‐36 questionnaires.Measurements and main resultsAbout 19.5% of CRS patients were well controlled, with 36.8% of patients being partly controlled and 43.7% uncontrolled. The levels of control corresponded to mean total VAS, SNOT‐22 and SF‐36 scores. Subgroup analysis revealed that female gender, aspirin intolerance and revision FESS were associated with higher prevalence of uncontrolled CRS, whereas allergy, asthma and smoking status did not alter the percentage of patients in each category of control. In 81 patients attending the outpatient clinic, nasal endoscopy changed classification in only four patients (4.9%).ConclusionsBased on the novel EPOS control criteria, at least 40% of CRS patients are uncontrolled at 3–5 years after FESS. Therefore, better treatment strategies leading to higher disease control are warranted in CRS care.

Purpose/objective: Precise delineation of organs at risk (OARs) in head and neck cancer (HNC) is necessary for accurate radiotherapy. Although guidelines exist, significant interobserver variability (IOV) remains. The aim was to validate a 3D convolutional neural network (CNN) for semi-automated delineation of OARs with respect to delineation accuracy, efficiency and consistency compared to manual delineation. Material/Methods: 16 OARs were manually delineated in 15 new HNC patients by two trained radiation oncologists (RO) independently, using international consensus guidelines. OARs were also automatically delineated by applying the CNN and corrected as needed by both ROs separately. Both delineations were performed two weeks apart and blinded to each other. IOV between both ROs was quantified using Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD). To objectify network accuracy, differences between automated and corrected delineations were calculated using the same similarity measures.Results: Average correction time of the automated delineation was 33% shorter than manual delineation (23 vs 34 minutes)(p<10-6). IOV improved significantly with network initialisation for nearly all OARs (p<0.05), resulting in decreased ASSD averaged over all OARs from 1.9 to 1.2 mm. The network achieved an accuracy of 90% and 84% DSC averaged over all OARs for RO1 and RO2 respectively, with an ASSD of 0.7 and 1.5 mm, which was in 93% and 73% of the cases lower than the IOV. Conclusion:The CNN developed for automated OAR delineation in HNC was shown to be more efficient and consistent compared to manual delineation, which justify its implementation in clinical practice.

No abstract

Background In radiotherapy inaccuracy in organ at risk (OAR) delineation can impact treatment plan optimisation and treatment plan evaluation. Brouwer et al. showed significant interobserver variability (IOV) in OAR delineation in head and neck cancer (HNC) and published international consensus guidelines (ICG) for OAR delineation in 2015. The aim of our study was to evaluate IOV in the presence of these guidelines. Methods HNC radiation oncologists (RO) from each Belgian radiotherapy centre were invited to complete a survey and submit contours for 5 HNC cases. Reference contours (OARref) were obtained by a clinically validated artificial intelligence-tool trained using ICG. Dice similarity coefficients (DSC), mean surface distance (MSD) and 95% Hausdorff distances (HD95) were used for comparison. Results Fourteen of twenty-two RO (64%) completed the survey and submitted delineations. Thirteen (93%) confirmed the use of delineation guidelines, of which six (43%) used the ICG. The OARs whose delineations agreed best with the OARref were mandible [median DSC 0.9, range (0.8–0.9); median MSD 1.1 mm, range (0.8–8.3), median HD95 3.4 mm, range (1.5–38.7)], brainstem [median DSC 0.9 (0.6–0.9); median MSD 1.5 mm (1.1–4.0), median HD95 4.0 mm (2.3–15.0)], submandibular glands [median DSC 0.8 (0.5–0.9); median MSD 1.2 mm (0.9–2.5), median HD95 3.1 mm (1.8–12.2)] and parotids [median DSC 0.9 (0.6–0.9); median MSD 1.9 mm (1.2–4.2), median HD95 5.1 mm (3.1–19.2)]. Oral cavity, cochleas, PCMs, supraglottic larynx and glottic area showed more variation. RO who used the consensus guidelines showed significantly less IOV (p = 0.008). Conclusions Although ICG for delineation of OARs in HNC exist, they are only implemented by about half of RO participating in this study, which partly explains the delineation variability. However, this study highlights that guidelines alone do not suffice to eliminate IOV and that more effort needs to be done to accomplish further treatment standardisation, for example with artificial intelligence.