Devanish Kamtam

Abstract Existing methods of grading atelectasis are typically subjective and not scalable. We aimed to develop an automated, deep learning–based framework to quantify and grade postoperative atelectasis. We retrospectively included all patients who underwent RULobectomy from 2008 to 2023. We trained three nnU-Net v2 segmentation models for preoperative and postoperative lobes and airways with volumetric quantification of the right middle lobe (RML), right lower lobe (RLL), and total lung volume.

Abstract The World Health Organization estimates 9.9% of cancers are attributable to viruses. Notably, human papillomavirus causes roughly 90% of cervical cancers, while Epstein-Barr virus (EBV) is linked to nearly 10% of gastric carcinomas. Regarding breast cancer, the association with EBV is inconclusive. While studies in some nations report an association, those in the United States largely do not.