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
- dual energy
- end stage renal disease
- deep learning
- positron emission tomography
- endothelial cells
- primary care
- newly diagnosed
- chronic kidney disease
- ejection fraction
- image quality
- magnetic resonance imaging
- emergency department
- high resolution
- machine learning
- prognostic factors
- high grade
- randomized controlled trial
- convolutional neural network
- optical coherence tomography
- magnetic resonance
- clinical trial
- induced pluripotent stem cells
- quantum dots