Incidental presentation of appendix neuroendocrine tumor: Long-term results from a single institution.

Background: Appendix neuroendocrine tumors (NETs) are the most common tumors of the appendix and are most often diagnosed incidentally. The aim of this study was to retrospectively evaluate appendix NETs diagnosed incidentally in our clinic.

Methods: Of 8304 patients who underwent appendectomy with the diagnosis of acute appendicitis in Ankara Training and Re-search Hospital, General Surgery Clinic between January 2009 and January 2022, 33 had histopathology results evaluated as appendix NET, and a retrospective analysis was made of these cases.

High Inborn Errors of Immunity Risk in Patients with Granuloma.

Objective: Granuloma etiology includes infections, vasculitis, chemicals, malignancies, lymphoproliferative disorders, and immunological diseases. We hypothesized that patients with granuloma have an underlying primary immunodeficiency disease (PIDD).

Patients And Methods: We retrospectively enrolled 82 patients with immunological evaluation among 294 biopsy-proven granuloma patients (0- to 20-year-old).

A very rare case of a newborn with tetrasomy 9p and literature review.

Background: Tetrasomy 9p is a rare genetic condition which usually results from a supernumerary isochromosome derived from the short arm of chromosome 9. Phenotypic findings include multiple congenital anomalies, facial dysmorphism, growth and developmental delays, and also vary according to the presence and degree of mosaicism.

Case: We report on a newborn with tetrasomy 9p who deceased in the newborn period.

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study.

Background: Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences.

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records.

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation.