Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x

Abstract

Purpose Artificial intelligence (AI)-based computer-aided detection/diagnosis (CADe/x) has helped improve radiologists’ performance and provides results equivalent or superior to those of radiologists’ alone. This prospective multicenter cohort study aims to generate real-world evidence on the overall benefits and disadvantages of using AI-based CADe/x for breast cancer detection in a population-based breast cancer screening program comprising Korean women aged ≥ 40 years. The purpose of this report is to compare the diagnostic accuracy of radiologists with and without the use of AI-based CADe/x in mammography readings for breast cancer screening of Korean women with average breast cancer risk. Methods Approximately 32,714 participants will be enrolled between February 2021 and December 2022 at 5 study sites in Korea. A radiologist specializing in breast imaging will interpret the mammography readings with or without the use of AI-based CADe/x. If recall is required, further diagnostic workup will be conducted to confirm the cancer detected on screening. The findings will be recorded for all participants regardless of their screening status to identify study participants with breast cancer diagnosis within both 1 year and 2 years of screening. The national cancer registry database will be reviewed in 2026 and 2027, and the results of this study are expected to be published in 2027. In addition, the diagnostic accuracy of general radiologists and radiologists specializing in breast imaging from another hospital with or without the use of AI-based CADe/x will be compared considering mammography readings for breast cancer screening. Discussion The Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM) study is a prospective multicenter study that aims to compare the diagnostic accuracy of radiologists with and without the use of AI-based CADe/x in mammography readings for breast cancer screening of women with average breast cancer risk. AI-STREAM is currently in the patient enrollment phase. Trial Registration ClinicalTrials.gov Identifier: NCT05024591

Breast Cancer Screening Using a Modified Inertial Projective Algorithms for Split Feasibility Problems

“…where ϱ n k ∈ ∂cðy n k Þ. By our assumption as ∂c is bounded (27) and (32), we have cðω * Þ ≤ 0, this shows that ω * ∈ C. By Opial's lemma in [26], we can conclude that fω n g converges weakly to a solution in Ω. This completes the proof.…”

“…Nowadays, there are many studies interested in the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening programme. Some believe that artificial intelligence (AI) has helped improve radiologists' performance and provides results equivalent or superior to those of radiologists' alone such as reduce the volume in screen-reading without affecting cancer detection substantially [ 32 ]. Although there are some issues that should be more explore including possible factors on recall and interval cancers [ 33 ], Freeman et al [ 34 ] performed the systematic review of test accuracy and concluded that there is inadequate evidence in judgement of accuracy of artificial intelligence (AI) in detecting breast cancer on screening mammography.…”

“…Very recently, Wang and Yu [32] generalized an inertial relaxed CQ of Yang [13] by modifying C n , Q n as follows:…”

Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review

“…The importance of data size (B1.1) and data quality (B1.2) was predominant in the included studies, being directly related to the effectiveness of machine learning and the issue of data sharing (e.g., less than 17% of original papers’ data is publicly available or under request as shown in Supplement Table S4 ). As a result of screening programs and higher availability, it was highlighted that digital MG and DBT studies usually have large datasets (many thousands of patients, from 9919 to 32,714 women in studies with MG [ 58 ] and DBT [ 51 ]) often acquired in several centers, in comparison to studies involving US and MRI [ 1 , 10 ]. MRI studies especially lacked data (from 93 [ 59 ] to 1715 patients [ 25 ] in included studies, with varying types of MR protocols).…”

Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine

“…The trial will continue for two more years to assess the primary endpoint of the IC rate [ 64 ]. Other studies, such as the AI-STREAM in South Korea, are also actively investigating the effects of AI in single-reader concurrent reading settings [ 65 ].…”