Adjuvant therapy and recurrence risk in non-myoinvasive high-grade (stage IC) endometrial cancer: A systematic review and meta-analysis.

Objective: To summarize practice patterns and outcomes among patients with non-myoinvasive high-grade (formerly stage IA, now stage IC) endometrial cancer.

Methods: We conducted a systematic search using MEDLINE, Embase, Cochrane, Web of Science, and ClinicalTrials.gov databases from inception to May 8, 2024 to identify studies reporting on treatment and outcomes of non-myoinvasive high-grade endometrial cancer.

Geographic and racial disparities in the quality of surgical care among patients with nonmetastatic uterine cancer.

Background: Although the rates of minimally invasive surgery and sentinel lymph node biopsy have increased considerably over time in the surgical management of early-stage uterine cancer, practice varies significantly in the United States, and there are disparities among low-volume centers and patients of Black race. A significant number of counties in the United States are without a gynecologic oncologist, and almost half of the counties with the highest gynecologic cancer rates lack a local gynecologic oncologist.

Objective: This study aimed to evaluate the relationships of distance traveled and proximity to gynecologic oncologists with the receipt of and racial disparities in the quality of surgical care among patients who underwent a hysterectomy for nonmetastatic uterine cancer.

The doc versus the bot: A pilot study to assess the quality and accuracy of physician and chatbot responses to clinical questions in gynecologic oncology.

Artificial intelligence (AI) applications to medical care are currently under investigation. We aimed to evaluate and compare the quality and accuracy of physician and chatbot responses to common clinical questions in gynecologic oncology. In this cross-sectional pilot study, ten questions about the knowledge and management of gynecologic cancers were selected.

Reduced-order model to approximate response matrices for filter stack spectrometers.

We present a reduced-order model to calculate response matrices rapidly for filter stack spectrometers (FSSs). The reduced-order model allows response matrices to be built modularly from a set of pre-computed photon and electron transport and scattering calculations through various filter and detector materials. While these modular response matrices are not appropriate for high-fidelity analysis of experimental data, they encode sufficient physics to be used as a forward model in design optimization studies of FSSs, particularly for machine learning approaches that require sampling and testing a large number of FSS designs.