Gengmin Niu

Abstract Widespread clinical implementation of rapidly evolving auto-segmentation tools remains constrained by a scarcity of high-quality prospective evidence. Here we show the results of a prospective, multicenter, observational trial (NCT05787522) evaluating the clinical performance of a deep learning model (iCurveE) for artificial intelligence (AI)-assisted delineation of organs at risk (OARs) in thoracic and breast cancer radiotherapy.

AE Adverse events BED Biological effective dose BMs Brain metastases CK Cyberknife CRN Cerebral radiation necrosis DBCRNAB Duration between CRN diagnosis and BEV treatment DBRAB Duration between radiotherapy and BEV treatment DBRACRN Duration between radiotherapy and CRN diagnosis ESMO European Society for Medical Oncology IQRs Interquartile ranges IVBEV Intravenous bevacizumab NCI-CTCAE National Cancer Institute's Common Terminology Criteria OS Overall survival PD Progressive disease PET/CT...