Performance of artificial intelligence in 7533 consecutive prevalent screening mammograms from the BreastScreen Australia program.

Objectives: To assess the performance of an artificial intelligence (AI) algorithm in the Australian mammography screening program which routinely uses two independent readers with arbitration of discordant results.

Methods: A total of 7533 prevalent round mammograms from 2017 were available for analysis. The AI program classified mammograms into deciles on the basis of breast cancer (BC) risk.

Multiparametric MR evaluation of uterine leiomyosarcoma and STUMP versus leiomyoma in symptomatic women planned for high frequency focussed ultrasound: accuracy of imaging parameters and interobserver agreement for identification of malignancy.

Objective: To assess accuracy of and interobserver agreement on multiparametric MR findings to distinguish uterine leiomyoma (LM) from uterine leiomyosarcoma (LMS) and soft tissue tumour of unknown malignant potential.

Methods: Inclusion criteria: All females over 18 years with least one uterine mass measuring 5 cm or more in at least one of the three standard orthogonal dimensions on MR with histopathological confirmation of LM, LMS, or soft tissue tumour of unknown malignant potential (STUMP) in the 3 months following MR. Patients with LMS were drawn from a larger cohort being assessed for MR-guided focussed ultrasound (MRgFUS) suitability.