Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?

Introduction: We aimed to develop a diagnostic deep learning model using contrast-enhanced CT images and to investigate whether cervical lymphadenopathies can be diagnosed with these deep learning methods without radiologist interpretations and histopathological examinations.

Material Method: A total of 400 patients who underwent surgery for lymphadenopathy in the neck between 2010 and 2022 were retrospectively analyzed. They were examined in four groups of 100 patients: the granulomatous diseases group, the lymphoma group, the squamous cell tumor group, and the reactive hyperplasia group.

Predictors of allocation to surgery in patients older than 50 years with partial-thickness rotator cuff tear.

Background: The purpose of this study was to determine the predictive factors for allocation to surgery in patients older than 50 years with symptomatic chronic partial-thickness rotator cuff tear (PTRCT).

Methods: Patients older than 50 years with a confirmed diagnosis of unilateral isolated PTRCT were included in this retrospective study. In the minimum follow-up of 2 years, eventual allocation to surgical or nonsurgical treatment was determined individually.