Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

Abstract

Objectives Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist. Methods A multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance. Results On average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced (p < 0.001) from 41 (95% CI = 39–42 s) to 36 s (95% CI = 35– 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115). Conclusions Radiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system. Key Points • Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time. • The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams. • The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.

Does artificial intelligence aid in the detection of different types of breast cancer?

“…Our findings are also agreeing with smaller scale studies [11][12][13][14][15][16]. Rodriguez-Ruiz et al [17] in 2019, performed a study on 240 mammograms (100 cancers, 40 leading to false-positive recalls, 100 normal) reported that sensitivity increased with AI support to 86% vs 83% with mammography alone.…”

Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study

“…To verify the CAD model's correlation associated with radiologists' experience, we focused only on mass, which is an uncomprehensive system for all the signs in breast lesions. Second, compared with other similar studies, where an average of 16.4 radiologists participated in the reader study (34)(35)(36)(37)(38), the number of readers in MRMC was relatively small in our study. Moreover, this study did not set two replicate experiments to verify the agreements for the same radiologist in different reading times.…”

Breast cancer screening with digital breast tomosynthesis: comparison of different reading strategies implementing artificial intelligence

“…While there are several studies of AI for DM, studies of AI for DBT are relatively scarce, with only a few studies of DBT on screening material at the time of writing, including two focusing on DBT reading workload reduction [18][19][20][21]. Several reader studies on the use of AI as decision support for DBT with cancerenriched datasets have shown a reduction in reading time per examination with maintained or increased accuracy [22][23][24][25][26][27]. An AI model for predicting future short-term cancer risk from DBT has also been developed [28].…”