Background: The ovarian cancer (OC) preclinical detectable phase (PCDP), defined as the interval during which cancer is detectable prior to clinical diagnosis, remains poorly characterised. We report exploratory analyses from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).
Methods: In UKCTOCS between Apr-2001 and Sep-2005, 101,314 postmenopausal women were randomised to no screening (NS) and 50,625 to annual multimodal screening (MMS) (until Dec-2011) using serum CA-125 interpreted by the Risk of Ovarian Cancer Algorithm (ROCA).
Purpose: Sybil is a validated publicly available deep learning-based algorithm that can accurately predict lung cancer risk from a single low-dose computed tomography (LDCT) scan. We aimed to study the effect of image reconstruction parameters and CT scanner manufacturer on Sybil's performance.
Materials And Methods: Using LDCTs of a subset of the National Lung Screening Trial participants, which we previously used for internal validation of the Sybil algorithm (test set), we ran the Sybil algorithm on LDCT series pairs matched on kilovoltage peak, milliampere-seconds, reconstruction interval, reconstruction diameter, and either reconstruction filter or axial slice thickness.
Background: Ovarian cancer remains the deadliest of the gynecologic cancers in the United States. There have been limited advances in treatment strategies that have seen marked increases in overall survival. Thus, it is essential to continue developing and validating new treatment strategies and markers to identify patients who would benefit from the new strategy.
View Article and Find Full Text PDF