Multi-Specialty Expert Physician Identification of Extranodal Extension in Computed Tomography Scans of Oropharyngeal Cancer Patients: Prospective Blinded Human Inter-Observer Performance Evaluation.
null nullOnur SahinKareem A WahidNicolette TakuRenjie HeMohamed A NaserAbdallah S R MohamedAntti MäkitieBenjamin H KannKimmo KaskiJaakko SahlstenJoel JaskariMoran AmitMelissa M ChenGregory M ChronowskiEduardo M DiazAdam S GardenRyan P GoepfertJeffrey P GuenetteG Brandon GunnJussi HirvonenFrank HoebersNandita Guha-ThakurtaJason JohnsonDiana KayaShekhar D KhanparaKristofer NymanStephen Y LaiMiriam LangoKim O LearnedAnna LeeCarol M LewisAnastasios ManiakasAmy C MorenoJeffery N MyersJack PhanKristen B PytyniaDavid I RosenthalVlad SandulacheDawid SchellingerhoutShalin J ShahAndrew G SikoraMax WintermarkClifton David FullerPublished in: medRxiv : the preprint server for health sciences (2023)
Detection of ENE in HPV+OPC patients on CT imaging remains a difficult task with high variability, regardless of clinician specialty. Although some differences do exist between the specialists, they are often minimal. Further research in automated analysis of ENE from radiographic images is likely needed.
Keyphrases
- computed tomography
- end stage renal disease
- dual energy
- deep learning
- contrast enhanced
- positron emission tomography
- ejection fraction
- endothelial cells
- newly diagnosed
- chronic kidney disease
- image quality
- primary care
- high resolution
- magnetic resonance imaging
- randomized controlled trial
- prognostic factors
- peritoneal dialysis
- machine learning
- patient reported outcomes
- high grade
- convolutional neural network
- clinical practice
- magnetic resonance
- optical coherence tomography
- fluorescence imaging
- loop mediated isothermal amplification
- quantum dots
- sensitive detection
- bioinformatics analysis